From 49bc7d0d68e9e3137a0073b1fc27b7559a414d04 Mon Sep 17 00:00:00 2001
From: darrylong <darrylong@users.noreply.github.com>
Date: Fri, 7 Jun 2024 16:10:31 +0800
Subject: [PATCH 1/5] Added static page for model viewing and filtering

---
 docs/source/_static/models/data.js     | 348 +++++++++++++++++++++++++
 docs/source/_static/models/models.html |  67 +++++
 docs/source/index.rst                  |   6 +
 3 files changed, 421 insertions(+)
 create mode 100644 docs/source/_static/models/data.js
 create mode 100644 docs/source/_static/models/models.html

diff --git a/docs/source/_static/models/data.js b/docs/source/_static/models/data.js
new file mode 100644
index 000000000..de3897070
--- /dev/null
+++ b/docs/source/_static/models/data.js
@@ -0,0 +1,348 @@
+var data = [
+    {
+        "year": "",
+        "name": "Online Indexable Bayesian Personalized Ranking (Online IBPR)",
+        "link": "cornac/models/online_ibpr",
+        "paper": "http://www.hadylauw.com/publications/cikm17a.pdf",
+        "type": "Collaborative Filtering",
+        "requirements": "cornac/models/online_ibpr/requirements.txt",
+        "platform": "CPU / GPU",
+        "quick-start": "examples/ibpr_example.py",
+        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/08_retrieval.ipynb"
+    },
+    {
+        "year": "",
+        "name": "Visual Matrix Factorization (VMF)",
+        "link": "cornac/models/vmf",
+        "paper": "https://dsail.kaist.ac.kr/files/WWW17.pdf",
+        "type": "Content-Based / Image",
+        "requirements": "cornac/models/vmf/requirements.txt",
+        "platform": "CPU / GPU",
+        "quick-start": "examples/vmf_clothing.py"
+    },
+    {
+        "year": "2016",
+        "name": "Collaborative Deep Ranking (CDR)",
+        "link": "cornac/models/cdr",
+        "paper": "http://inpluslab.com/chenliang/homepagefiles/paper/hao-pakdd2016.pdf",
+        "type": "Content-Based / Text",
+        "requirements": "cornac/models/cdr/requirements.txt",
+        "platform": "CPU / GPU",
+        "quick-start": "examples/cdr_example.py"
+    },
+    {
+        "year": "",
+        "name": "Collaborative Ordinal Embedding (COE)",
+        "link": "cornac/models/coe",
+        "paper": "http://www.hadylauw.com/publications/sdm16.pdf",
+        "type": "Collaborative Filtering",
+        "requirements": "cornac/models/coe/requirements.txt",
+        "platform": "CPU / GPU"
+    },
+    {
+        "year": "",
+        "name": "Convolutional Matrix Factorization (ConvMF)",
+        "link": "cornac/models/conv_mf",
+        "paper": "http://uclab.khu.ac.kr/resources/publication/C_351.pdf",
+        "type": "Content-Based / Text",
+        "requirements": "cornac/models/conv_mf/requirements.txt",
+        "platform": "CPU / GPU",
+        "quick-start": "examples/conv_mf_example.py",
+        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/09_deep_learning.ipynb"
+    },
+    {
+        "year": "",
+        "name": "Learning to Rank Features for Recommendation over Multiple Categories (LRPPM)",
+        "link": "cornac/models/lrppm",
+        "paper": "https://www.yongfeng.me/attach/sigir16-chen.pdf",
+        "type": "Explainable",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/lrppm_example.py"
+    },
+    {
+        "year": "",
+        "name": "Session-based Recommendations With Recurrent Neural Networks (GRU4Rec)",
+        "link": "cornac/models/gru4rec",
+        "paper": "https://arxiv.org/pdf/1511.06939.pdf",
+        "type": "Next-Item",
+        "requirements": "cornac/models/gru4rec/requirements.txt",
+        "platform": "CPU / GPU",
+        "quick-start": "examples/gru4rec_yoochoose.py"
+    },
+    {
+        "year": "",
+        "name": "Spherical K-means (SKM)",
+        "link": "cornac/models/skm",
+        "paper": "https://www.sciencedirect.com/science/article/pii/S092523121501509X",
+        "type": "Collaborative Filtering",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/skm_movielens.py"
+    },
+    {
+        "year": "",
+        "name": "Visual Bayesian Personalized Ranking (VBPR)",
+        "link": "cornac/models/vbpr",
+        "paper": "https://arxiv.org/pdf/1510.01784.pdf",
+        "type": "Content-Based / Image",
+        "requirements": "cornac/models/vbpr/requirements.txt",
+        "platform": "CPU / GPU",
+        "quick-start": "examples/vbpr_tradesy.py",
+        "cross-modality": "tutorials/vbpr_text.ipynb",
+        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/05_multimodality.ipynb"
+    },
+    {
+        "year": "2015",
+        "name": "Collaborative Deep Learning (CDL)",
+        "link": "cornac/models/cdl",
+        "paper": "https://arxiv.org/pdf/1409.2944.pdf",
+        "type": "Content-Based / Text",
+        "requirements": "cornac/models/cdl/requirements.txt",
+        "platform": "CPU / GPU",
+        "quick-start": "examples/cdl_example.py",
+        "deep-dive": "https://github.com/lgabs/cornac/blob/luan/describe-gpu-supported-models-readme/tutorials/working_with_auxiliary_data.md"
+    },
+    {
+        "year": "",
+        "name": "Hierarchical Poisson Factorization (HPF)",
+        "link": "cornac/models/hpf",
+        "paper": "http://jakehofman.com/inprint/poisson_recs.pdf",
+        "type": "Collaborative Filtering",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/hpf_movielens.py"
+    },
+    {
+        "year": "",
+        "name": "TriRank: Review-aware Explainable Recommendation by Modeling Aspects",
+        "link": "cornac/models/trirank",
+        "paper": "https://wing.comp.nus.edu.sg/wp-content/uploads/Publications/PDF/TriRank-%20Review-aware%20Explainable%20Recommendation%20by%20Modeling%20Aspects.pdf",
+        "type": "Explainable",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/trirank_example.py"
+    },
+    {
+        "year": "2014",
+        "name": "Explicit Factor Model (EFM)",
+        "link": "cornac/models/efm",
+        "paper": "https://www.yongfeng.me/attach/efm-zhang.pdf",
+        "type": "Explainable",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/efm_example.py",
+        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/07_explanations.ipynb"
+    },
+    {
+        "year": "",
+        "name": "Social Bayesian Personalized Ranking (SBPR)",
+        "link": "cornac/models/sbpr",
+        "paper": "https://cseweb.ucsd.edu/~jmcauley/pdfs/cikm14.pdf",
+        "type": "Content-Based / Social",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/sbpr_epinions.py"
+    },
+    {
+        "year": "2013",
+        "name": "Hidden Factors and Hidden Topics (HFT)",
+        "link": "cornac/models/hft",
+        "paper": "https://cs.stanford.edu/people/jure/pubs/reviews-recsys13.pdf",
+        "type": "Content-Based / Text",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/hft_example.py"
+    },
+    {
+        "year": "2012",
+        "name": "Weighted Bayesian Personalized Ranking (WBPR)",
+        "link": "cornac/models/bpr",
+        "paper": "http://proceedings.mlr.press/v18/gantner12a/gantner12a.pdf",
+        "type": "Collaborative Filtering",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/bpr_netflix.py"
+    },
+    {
+        "year": "2011",
+        "name": "Collaborative Topic Regression (CTR)",
+        "link": "cornac/models/ctr",
+        "paper": "http://www.cs.columbia.edu/~blei/papers/WangBlei2011.pdf",
+        "type": "Content-Based / Text",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/ctr_example_citeulike.py",
+        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/05_multimodality.ipynb"
+    },
+    {
+        "year": "Earlier",
+        "name": "Baseline Only",
+        "link": "cornac/models/baseline_only",
+        "paper": "http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf",
+        "type": "Baseline",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/svd_example.py"
+    },
+    {
+        "year": "",
+        "name": "Bayesian Personalized Ranking (BPR)",
+        "link": "cornac/models/bpr",
+        "paper": "https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf",
+        "type": "Collaborative Filtering",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/bpr_netflix.py",
+        "deep-dive": "https://github.com/recommenders-team/recommenders/blob/main/examples/02_model_collaborative_filtering/cornac_bpr_deep_dive.ipynb"
+    },
+    {
+        "year": "",
+        "name": "Factorization Machines (FM)",
+        "link": "cornac/models/fm",
+        "paper": "https://www.csie.ntu.edu.tw/~b97053/paper/Factorization%20Machines%20with%20libFM.pdf",
+        "type": "Collaborative Filtering / Content-Based",
+        "requirements": "Linux, CPU",
+        "platform": "",
+        "quick-start": "examples/fm_example.py",
+        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/06_contextual_awareness.ipynb"
+    },
+    {
+        "year": "",
+        "name": "Global Average (GlobalAvg)",
+        "link": "cornac/models/global_avg",
+        "paper": "https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf",
+        "type": "Baseline",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/biased_mf.py"
+    },
+    {
+        "year": "",
+        "name": "Global Personalized Top Frequent (GPTop)",
+        "link": "cornac/models/gp_top",
+        "paper": "https://dl.acm.org/doi/pdf/10.1145/3587153",
+        "type": "Next-Basket",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/gp_top_tafeng.py"
+    },
+    {
+        "year": "",
+        "name": "Item K-Nearest-Neighbors (ItemKNN)",
+        "link": "cornac/models/knn",
+        "paper": "https://dl.acm.org/doi/pdf/10.1145/371920.372071",
+        "type": "Neighborhood-Based",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/knn_movielens.py",
+        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/02_neighborhood.ipynb"
+    },
+    {
+        "year": "",
+        "name": "Matrix Factorization (MF)",
+        "link": "cornac/models/mf",
+        "paper": "https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf",
+        "type": "Collaborative Filtering",
+        "requirements": "",
+        "platform": "CPU / GPU",
+        "quick-start": "examples/biased_mf.py",
+        "pre-split-data": "examples/given_data.py",
+        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/03_matrix_factorization.ipynb"
+    },
+    {
+        "year": "",
+        "name": "Maximum Margin Matrix Factorization (MMMF)",
+        "link": "cornac/models/mmmf",
+        "paper": "https://link.springer.com/content/pdf/10.1007/s10994-008-5073-7.pdf",
+        "type": "Collaborative Filtering",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/mmmf_exp.py"
+    },
+    {
+        "year": "",
+        "name": "Most Popular (MostPop)",
+        "link": "cornac/models/most_pop",
+        "paper": "https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf",
+        "type": "Baseline",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/bpr_netflix.py"
+    },
+    {
+        "year": "",
+        "name": "Non-negative Matrix Factorization (NMF)",
+        "link": "cornac/models/nmf",
+        "paper": "http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf",
+        "type": "Collaborative Filtering",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/nmf_example.py",
+        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/03_matrix_factorization.ipynb"
+    },
+    {
+        "year": "",
+        "name": "Probabilistic Matrix Factorization (PMF)",
+        "link": "cornac/models/pmf",
+        "paper": "https://papers.nips.cc/paper/3208-probabilistic-matrix-factorization.pdf",
+        "type": "Collaborative Filtering",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/pmf_ratio.py"
+    },
+    {
+        "year": "",
+        "name": "Session Popular (SPop)",
+        "link": "cornac/models/spop",
+        "paper": "https://arxiv.org/pdf/1511.06939.pdf",
+        "type": "Next-Item / Baseline",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/spop_yoochoose.py"
+    },
+    {
+        "year": "",
+        "name": "Singular Value Decomposition (SVD)",
+        "link": "cornac/models/svd",
+        "paper": "https://people.engr.tamu.edu/huangrh/Spring16/papers_course/matrix_factorization.pdf",
+        "type": "Collaborative Filtering",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/svd_example.py",
+        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/03_matrix_factorization.ipynb"
+    },
+    {
+        "year": "",
+        "name": "Social Recommendation using PMF (SoRec)",
+        "link": "cornac/models/sorec",
+        "paper": "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.304.2464&rep=rep1&type=pdf",
+        "type": "Content-Based / Social",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/sorec_filmtrust.py",
+        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/05_multimodality.ipynb"
+    },
+    {
+        "year": "",
+        "name": "User K-Nearest-Neighbors (UserKNN)",
+        "link": "cornac/models/knn",
+        "paper": "https://arxiv.org/pdf/1301.7363.pdf",
+        "type": "Neighborhood-Based",
+        "requirements": "",
+        "platform": "CPU",
+        "quick-start": "examples/knn_movielens.py",
+        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/02_neighborhood.ipynb"
+    },
+    {
+        "year": "",
+        "name": "Weighted Matrix Factorization (WMF)",
+        "link": "cornac/models/wmf",
+        "paper": "http://yifanhu.net/PUB/cf.pdf",
+        "type": "Collaborative Filtering",
+        "requirements": "cornac/models/wmf/requirements.txt",
+        "platform": "CPU / GPU",
+        "quick-start": "examples/wmf_example.py",
+        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/04_implicit_feedback.ipynb"
+    }
+];
\ No newline at end of file
diff --git a/docs/source/_static/models/models.html b/docs/source/_static/models/models.html
new file mode 100644
index 000000000..6e30a18d6
--- /dev/null
+++ b/docs/source/_static/models/models.html
@@ -0,0 +1,67 @@
+<html lang="en">
+ <head>
+   <script src="https://cdn.jsdelivr.net/npm/ag-grid-community/dist/ag-grid-community.min.js"></script>
+ </head>
+ <body>
+    <div>
+        <button onclick="loadCFModels()">Collaborative Filtering Models</button>
+        <button onclick="loadContentModels()">Content Based Models</button>
+        <button onclick="loadExplainModels()">Explainable Models</button>
+        <button onclick="loadNextItemModels()">Next-Item Models</button>
+        <button onclick="loadNextBasketModels()">Next-Basket Models</button>
+        <button onclick="loadBaselineModels()">Baseline Models</button>
+        <button onclick="loadGpuModels()">GPU</button>
+        <button onclick="loadCpuModels()">CPU only</button>
+    </div>
+    <div>
+        <input type="text" id="filter-text-box" placeholder="Filter..." oninput="onFilterTextBoxChanged()" />
+    </div>
+    <div id="grid" class="ag-theme-quartz" style="height: 600px"></div>
+ </body>
+</html>
+<script type="text/javascript" src="data.js"></script>
+<script type="text/javascript" src="_static/models/data.js"></script>
+<script type="text/javascript">
+    function LinkRenderer(url, title) {
+        return `<a href="${url}" target="_blank">${title}</a>`
+    }
+    // Row Data Interface
+    const github_url = "https://github.com/PreferredAI/cornac/tree/master/";
+
+    // Grid API: Access to Grid API methods
+    let gridApi;
+
+    function onFilterTextBoxChanged() {
+        gridApi.setGridOption(
+            "quickFilterText",
+            document.getElementById("filter-text-box").value,
+        );
+    }
+
+
+    // Grid Options: Contains all of the grid configurations
+    const gridOptions = {
+        // Data to be displayed
+        rowData: data,
+        // Columns to be displayed (Should match rowData properties)
+        columnDefs: [
+            { field: "name", headerName: "Model Name", flex: 4 },
+            { field: "type", flex: 2 },
+            { field: "year" },
+            { field: "platform" },
+            { field: "link", cellRenderer: params => LinkRenderer(github_url + params.value, "Source Code")},
+            { field: "paper", cellRenderer: params => LinkRenderer(params.value, "Paper") },
+            { field: "requirements", cellRenderer: params => LinkRenderer(github_url + params.value, "Requirements.txt") },
+            // { field: "quick-start" },
+            // { field: "deep-dive" }
+        ],
+        defaultColDef: {
+            flex: 1,
+            filter: true,
+        },
+        pagination: true,
+        paginationAutoPageSize: true,
+    };
+    // Create Grid: Create new grid within the #myGrid div, using the Grid Options object
+    gridApi = agGrid.createGrid(document.querySelector("#grid"), gridOptions);
+</script>
\ No newline at end of file
diff --git a/docs/source/index.rst b/docs/source/index.rst
index b0f632e1f..6ab136771 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -114,3 +114,9 @@ Quick Links
          :click-parent:
 
          Contributor's Guide
+
+Models Available
+^^^^^^^^^^^^^^^^
+
+.. raw:: html
+   :file: _static/models/models.html
\ No newline at end of file

From 53cc58b64b7fcef72542635064e0cf81822b15a9 Mon Sep 17 00:00:00 2001
From: darrylong <darrylong@users.noreply.github.com>
Date: Fri, 14 Jun 2024 16:02:18 +0800
Subject: [PATCH 2/5] Update model json data

---
 docs/source/_static/models/data.js | 1076 ++++++++++++++++++++--------
 1 file changed, 766 insertions(+), 310 deletions(-)

diff --git a/docs/source/_static/models/data.js b/docs/source/_static/models/data.js
index de3897070..678a72bb0 100644
--- a/docs/source/_static/models/data.js
+++ b/docs/source/_static/models/data.js
@@ -1,348 +1,804 @@
 var data = [
     {
-        "year": "",
-        "name": "Online Indexable Bayesian Personalized Ranking (Online IBPR)",
-        "link": "cornac/models/online_ibpr",
+        "Year": "2024",
+        "Type": "Hybrid / Sentiment / Explainable",
+        "Name": "Hypergraphs with Attention on Reviews (HypAR)",
+        "Link": "cornac/models/hypar",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.hypar.recom_hypar",
+        "paper": "https://doi.org/10.1007/978-3-031-56027-9_14",
+        "PyTorch": true,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.HypAR"
+        ]
+    },
+    {
+        "Year": "2022",
+        "Type": "Content-Based / Text & Image",
+        "Name": "Disentangled Multimodal Representation Learning for Recommendation (DMRL)",
+        "Link": "cornac/models/dmrl",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.dmrl.recom_dmrl",
+        "paper": "https://arxiv.org/pdf/2203.05406.pdf",
+        "PyTorch": true,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.DMRL"
+        ]
+    },
+    {
+        "Year": "2021",
+        "Type": "Collaborative Filtering / Content-Based",
+        "Name": "Bilateral Variational Autoencoder for Collaborative Filtering (BiVAECF)",
+        "Link": "cornac/models/bivaecf",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.bivaecf.recom_bivaecf",
+        "paper": "https://dl.acm.org/doi/pdf/10.1145/3437963.3441759",
+        "PyTorch": true,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.BiVAECF"
+        ]
+    },
+    {
+        "Year": "2021",
+        "Type": "Content-Based / Image",
+        "Name": "Causal Inference for Visual Debiasing in Visually-Aware Recommendation (CausalRec)",
+        "Link": "cornac/models/causalrec",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.causalrec.recom_causalrec",
+        "paper": "https://arxiv.org/abs/2107.02390",
+        "PyTorch": true,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.CausalRec"
+        ]
+    },
+    {
+        "Year": "2021",
+        "Type": "Explainable",
+        "Name": "Explainable Recommendation with Comparative Constraints on Product Aspects (ComparER)",
+        "Link": "cornac/models/comparer",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.comparer.recom_comparer_sub",
+        "paper": "https://dl.acm.org/doi/pdf/10.1145/3437963.3441754",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.ComparERSub",
+            "cornac.models.ComparERObj"
+        ]
+    },
+    {
+        "Year": "2020",
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-        "link": "cornac/models/trirank",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.HPF"
+        ]
+    },
+    {
+        "Year": "2015",
+        "Type": "Explainable",
+        "Name": "TriRank: Review-aware Explainable Recommendation by Modeling Aspects",
+        "Link": "cornac/models/trirank",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.trirank.recom_trirank",
         "paper": "https://wing.comp.nus.edu.sg/wp-content/uploads/Publications/PDF/TriRank-%20Review-aware%20Explainable%20Recommendation%20by%20Modeling%20Aspects.pdf",
-        "type": "Explainable",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/trirank_example.py"
-    },
-    {
-        "year": "2014",
-        "name": "Explicit Factor Model (EFM)",
-        "link": "cornac/models/efm",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.TriRank"
+        ]
+    },
+    {
+        "Year": "2014",
+        "Type": "Explainable",
+        "Name": "Explicit Factor Model (EFM)",
+        "Link": "cornac/models/efm",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.efm.recom_efm",
         "paper": "https://www.yongfeng.me/attach/efm-zhang.pdf",
-        "type": "Explainable",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/efm_example.py",
-        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/07_explanations.ipynb"
-    },
-    {
-        "year": "",
-        "name": "Social Bayesian Personalized Ranking (SBPR)",
-        "link": "cornac/models/sbpr",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.EFM"
+        ]
+    },
+    {
+        "Year": "2014",
+        "Type": "Content-Based / Social",
+        "Name": "Social Bayesian Personalized Ranking (SBPR)",
+        "Link": "cornac/models/sbpr",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#social-bayesian-personalized-ranking-sbpr",
         "paper": "https://cseweb.ucsd.edu/~jmcauley/pdfs/cikm14.pdf",
-        "type": "Content-Based / Social",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/sbpr_epinions.py"
-    },
-    {
-        "year": "2013",
-        "name": "Hidden Factors and Hidden Topics (HFT)",
-        "link": "cornac/models/hft",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.SBPR"
+        ]
+    },
+    {
+        "Year": "2013",
+        "Type": "Content-Based / Text",
+        "Name": "Hidden Factors and Hidden Topics (HFT)",
+        "Link": "cornac/models/hft",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.hft.recom_hft",
         "paper": "https://cs.stanford.edu/people/jure/pubs/reviews-recsys13.pdf",
-        "type": "Content-Based / Text",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/hft_example.py"
-    },
-    {
-        "year": "2012",
-        "name": "Weighted Bayesian Personalized Ranking (WBPR)",
-        "link": "cornac/models/bpr",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.HFT"
+        ]
+    },
+    {
+        "Year": "2012",
+        "Type": "Collaborative Filtering",
+        "Name": "Weighted Bayesian Personalized Ranking (WBPR)",
+        "Link": "cornac/models/bpr",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#weighted-bayesian-personalized-ranking-wbpr",
         "paper": "http://proceedings.mlr.press/v18/gantner12a/gantner12a.pdf",
-        "type": "Collaborative Filtering",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/bpr_netflix.py"
-    },
-    {
-        "year": "2011",
-        "name": "Collaborative Topic Regression (CTR)",
-        "link": "cornac/models/ctr",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.BPR",
+            "cornac.models.WBPR"
+        ]
+    },
+    {
+        "Year": "2011",
+        "Type": "Content-Based / Text",
+        "Name": "Collaborative Topic Regression (CTR)",
+        "Link": "cornac/models/ctr",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.ctr.recom_ctr",
         "paper": "http://www.cs.columbia.edu/~blei/papers/WangBlei2011.pdf",
-        "type": "Content-Based / Text",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/ctr_example_citeulike.py",
-        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/05_multimodality.ipynb"
-    },
-    {
-        "year": "Earlier",
-        "name": "Baseline Only",
-        "link": "cornac/models/baseline_only",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.CTR"
+        ]
+    },
+    {
+        "Year": "Earlier",
+        "Type": "Baseline",
+        "Name": "Baseline Only",
+        "Link": "cornac/models/baseline_only",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#baseline-only",
         "paper": "http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf",
-        "type": "Baseline",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/svd_example.py"
-    },
-    {
-        "year": "",
-        "name": "Bayesian Personalized Ranking (BPR)",
-        "link": "cornac/models/bpr",
-        "paper": "https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf",
-        "type": "Collaborative Filtering",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/bpr_netflix.py",
-        "deep-dive": "https://github.com/recommenders-team/recommenders/blob/main/examples/02_model_collaborative_filtering/cornac_bpr_deep_dive.ipynb"
-    },
-    {
-        "year": "",
-        "name": "Factorization Machines (FM)",
-        "link": "cornac/models/fm",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.BaselineOnly"
+        ]
+    },
+    {
+        "Year": "Earlier",
+        "Type": "Collaborative Filtering",
+        "Name": "Bayesian Personalized Ranking (BPR)",
+        "Link": "cornac/models/bpr",
+        "docs": "https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.BPR",
+            "cornac.models.WBPR"
+        ]
+    },
+    {
+        "Year": "Earlier",
+        "Type": "Collaborative Filtering / Content-Based",
+        "Name": "Factorization Machines (FM)",
+        "Link": "cornac/models/fm",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#factorization-machines-fm",
         "paper": "https://www.csie.ntu.edu.tw/~b97053/paper/Factorization%20Machines%20with%20libFM.pdf",
-        "type": "Collaborative Filtering / Content-Based",
-        "requirements": "Linux, CPU",
-        "platform": "",
-        "quick-start": "examples/fm_example.py",
-        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/06_contextual_awareness.ipynb"
-    },
-    {
-        "year": "",
-        "name": "Global Average (GlobalAvg)",
-        "link": "cornac/models/global_avg",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.FM"
+        ]
+    },
+    {
+        "Year": "Earlier",
+        "Type": "Baseline",
+        "Name": "Global Average (GlobalAvg)",
+        "Link": "cornac/models/global_avg",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.global_avg.recom_global_avg",
         "paper": "https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf",
-        "type": "Baseline",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/biased_mf.py"
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.GlobalAvg"
+        ]
     },
     {
-        "year": "",
-        "name": "Global Personalized Top Frequent (GPTop)",
-        "link": "cornac/models/gp_top",
+        "Year": "Earlier",
+        "Type": "Next-Basket",
+        "Name": "Global Personalized Top Frequent (GPTop)",
+        "Link": "cornac/models/gp_top",
         "paper": "https://dl.acm.org/doi/pdf/10.1145/3587153",
-        "type": "Next-Basket",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/gp_top_tafeng.py"
-    },
-    {
-        "year": "",
-        "name": "Item K-Nearest-Neighbors (ItemKNN)",
-        "link": "cornac/models/knn",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.GPTop"
+        ]
+    },
+    {
+        "Year": "Earlier",
+        "Type": "Neighborhood-Based",
+        "Name": "Item K-Nearest-Neighbors (ItemKNN)",
+        "Link": "cornac/models/knn",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#item-k-nearest-neighbors-itemknn",
         "paper": "https://dl.acm.org/doi/pdf/10.1145/371920.372071",
-        "type": "Neighborhood-Based",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/knn_movielens.py",
-        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/02_neighborhood.ipynb"
-    },
-    {
-        "year": "",
-        "name": "Matrix Factorization (MF)",
-        "link": "cornac/models/mf",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.ItemKNN",
+            "cornac.models.UserKNN"
+        ]
+    },
+    {
+        "Year": "Earlier",
+        "Type": "Collaborative Filtering",
+        "Name": "Matrix Factorization (MF)",
+        "Link": "cornac/models/mf",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.mf.recom_mf",
         "paper": "https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf",
-        "type": "Collaborative Filtering",
-        "requirements": "",
-        "platform": "CPU / GPU",
-        "quick-start": "examples/biased_mf.py",
-        "pre-split-data": "examples/given_data.py",
-        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/03_matrix_factorization.ipynb"
-    },
-    {
-        "year": "",
-        "name": "Maximum Margin Matrix Factorization (MMMF)",
-        "link": "cornac/models/mmmf",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.MF"
+        ]
+    },
+    {
+        "Year": "Earlier",
+        "Type": "Collaborative Filtering",
+        "Name": "Maximum Margin Matrix Factorization (MMMF)",
+        "Link": "cornac/models/mmmf",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.mmmf.recom_mmmf",
         "paper": "https://link.springer.com/content/pdf/10.1007/s10994-008-5073-7.pdf",
-        "type": "Collaborative Filtering",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/mmmf_exp.py"
-    },
-    {
-        "year": "",
-        "name": "Most Popular (MostPop)",
-        "link": "cornac/models/most_pop",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.MMMF"
+        ]
+    },
+    {
+        "Year": "Earlier",
+        "Type": "Baseline",
+        "Name": "Most Popular (MostPop)",
+        "Link": "cornac/models/most_pop",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.most_pop.recom_most_pop",
         "paper": "https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf",
-        "type": "Baseline",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/bpr_netflix.py"
-    },
-    {
-        "year": "",
-        "name": "Non-negative Matrix Factorization (NMF)",
-        "link": "cornac/models/nmf",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.MostPop"
+        ]
+    },
+    {
+        "Year": "Earlier",
+        "Type": "Collaborative Filtering",
+        "Name": "Non-negative Matrix Factorization (NMF)",
+        "Link": "cornac/models/nmf",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.nmf.recom_nmf",
         "paper": "http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf",
-        "type": "Collaborative Filtering",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/nmf_example.py",
-        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/03_matrix_factorization.ipynb"
-    },
-    {
-        "year": "",
-        "name": "Probabilistic Matrix Factorization (PMF)",
-        "link": "cornac/models/pmf",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.NMF"
+        ]
+    },
+    {
+        "Year": "Earlier",
+        "Type": "Collaborative Filtering",
+        "Name": "Probabilistic Matrix Factorization (PMF)",
+        "Link": "cornac/models/pmf",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.pmf.recom_pmf",
         "paper": "https://papers.nips.cc/paper/3208-probabilistic-matrix-factorization.pdf",
-        "type": "Collaborative Filtering",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/pmf_ratio.py"
-    },
-    {
-        "year": "",
-        "name": "Session Popular (SPop)",
-        "link": "cornac/models/spop",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.PMF"
+        ]
+    },
+    {
+        "Year": "Earlier",
+        "Type": "Next-Item / Baseline",
+        "Name": "Session Popular (SPop)",
+        "Link": "cornac/models/spop",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.spop.recom_spop",
         "paper": "https://arxiv.org/pdf/1511.06939.pdf",
-        "type": "Next-Item / Baseline",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/spop_yoochoose.py"
-    },
-    {
-        "year": "",
-        "name": "Singular Value Decomposition (SVD)",
-        "link": "cornac/models/svd",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.SPop"
+        ]
+    },
+    {
+        "Year": "Earlier",
+        "Type": "Collaborative Filtering",
+        "Name": "Singular Value Decomposition (SVD)",
+        "Link": "cornac/models/svd",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.svd.recom_svd",
         "paper": "https://people.engr.tamu.edu/huangrh/Spring16/papers_course/matrix_factorization.pdf",
-        "type": "Collaborative Filtering",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/svd_example.py",
-        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/03_matrix_factorization.ipynb"
-    },
-    {
-        "year": "",
-        "name": "Social Recommendation using PMF (SoRec)",
-        "link": "cornac/models/sorec",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.SVD"
+        ]
+    },
+    {
+        "Year": "Earlier",
+        "Type": "Content-Based / Social",
+        "Name": "Social Recommendation using PMF (SoRec)",
+        "Link": "cornac/models/sorec",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.sorec.recom_sorec",
         "paper": "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.304.2464&rep=rep1&type=pdf",
-        "type": "Content-Based / Social",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/sorec_filmtrust.py",
-        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/05_multimodality.ipynb"
-    },
-    {
-        "year": "",
-        "name": "User K-Nearest-Neighbors (UserKNN)",
-        "link": "cornac/models/knn",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.SoRec"
+        ]
+    },
+    {
+        "Year": "Earlier",
+        "Type": "Neighborhood-Based",
+        "Name": "User K-Nearest-Neighbors (UserKNN)",
+        "Link": "cornac/models/knn",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#user-k-nearest-neighbors-userknn",
         "paper": "https://arxiv.org/pdf/1301.7363.pdf",
-        "type": "Neighborhood-Based",
-        "requirements": "",
-        "platform": "CPU",
-        "quick-start": "examples/knn_movielens.py",
-        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/02_neighborhood.ipynb"
-    },
-    {
-        "year": "",
-        "name": "Weighted Matrix Factorization (WMF)",
-        "link": "cornac/models/wmf",
+        "PyTorch": false,
+        "TensorFlow": false,
+        "packages": [
+            "cornac.models.ItemKNN",
+            "cornac.models.UserKNN"
+        ]
+    },
+    {
+        "Year": "Earlier",
+        "Type": "Collaborative Filtering",
+        "Name": "Weighted Matrix Factorization (WMF)",
+        "Link": "cornac/models/wmf",
+        "docs": "https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.wmf.recom_wmf",
         "paper": "http://yifanhu.net/PUB/cf.pdf",
-        "type": "Collaborative Filtering",
-        "requirements": "cornac/models/wmf/requirements.txt",
-        "platform": "CPU / GPU",
-        "quick-start": "examples/wmf_example.py",
-        "deep-dive": "https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/04_implicit_feedback.ipynb"
+        "PyTorch": false,
+        "TensorFlow": true,
+        "packages": [
+            "cornac.models.WMF"
+        ]
     }
 ];
\ No newline at end of file

From a62d66479dda18c66692259238c7618923e22ec5 Mon Sep 17 00:00:00 2001
From: darrylong <darrylong@users.noreply.github.com>
Date: Fri, 14 Jun 2024 16:02:52 +0800
Subject: [PATCH 3/5] Update model js table

---
 docs/source/_static/models/models.html | 73 ++++++++++++++++++--------
 1 file changed, 52 insertions(+), 21 deletions(-)

diff --git a/docs/source/_static/models/models.html b/docs/source/_static/models/models.html
index 6e30a18d6..e478cd4c2 100644
--- a/docs/source/_static/models/models.html
+++ b/docs/source/_static/models/models.html
@@ -3,20 +3,23 @@
    <script src="https://cdn.jsdelivr.net/npm/ag-grid-community/dist/ag-grid-community.min.js"></script>
  </head>
  <body>
+    <h3>Filters</h3>
     <div>
-        <button onclick="loadCFModels()">Collaborative Filtering Models</button>
-        <button onclick="loadContentModels()">Content Based Models</button>
-        <button onclick="loadExplainModels()">Explainable Models</button>
-        <button onclick="loadNextItemModels()">Next-Item Models</button>
-        <button onclick="loadNextBasketModels()">Next-Basket Models</button>
-        <button onclick="loadBaselineModels()">Baseline Models</button>
-        <button onclick="loadGpuModels()">GPU</button>
-        <button onclick="loadCpuModels()">CPU only</button>
+        <button onclick="clearFilters()">Clear Filters</button>
     </div>
-    <div>
-        <input type="text" id="filter-text-box" placeholder="Filter..." oninput="onFilterTextBoxChanged()" />
+    <div style="margin-top: 16px;">
+        <button onclick="filterModels('Collaborative Filtering')">Collaborative Filtering Models</button>
+        <button onclick="filterModels('Content-Based')">Content Based Models</button>
+        <button onclick="filterModels('Explainable')">Explainable Models</button>
+        <button onclick="filterModels('Next-Item')">Next-Item Models</button>
+        <button onclick="filterModels('Next-Basket')">Next-Basket Models</button>
+        <button onclick="filterModels('Baseline')">Baseline Models</button>
+    </div>
+    <div style="margin-top: 16px;">
+        <input type="text" id="filter-text-box" size="50" placeholder="Fliter" oninput="onFilterTextBoxChanged()" />
     </div>
-    <div id="grid" class="ag-theme-quartz" style="height: 600px"></div>
+    <br />
+    <div id="grid" class="ag-theme-quartz" style="height: 480px"></div>
  </body>
 </html>
 <script type="text/javascript" src="data.js"></script>
@@ -38,6 +41,22 @@
         );
     }
 
+    function filterModels(filter) {
+        gridApi.setColumnFilterModel("Type", {
+            type: "contains",
+            filter: filter,
+        })
+        .then(() => {
+            gridApi.onFilterChanged();
+        });
+    }
+
+    function clearFilters(){
+        gridApi.setColumnFilterModel("Type", null)
+        .then(() => {
+            gridApi.onFilterChanged();
+        });
+    }
 
     // Grid Options: Contains all of the grid configurations
     const gridOptions = {
@@ -45,22 +64,34 @@
         rowData: data,
         // Columns to be displayed (Should match rowData properties)
         columnDefs: [
-            { field: "name", headerName: "Model Name", flex: 4 },
-            { field: "type", flex: 2 },
-            { field: "year" },
-            { field: "platform" },
-            { field: "link", cellRenderer: params => LinkRenderer(github_url + params.value, "Source Code")},
-            { field: "paper", cellRenderer: params => LinkRenderer(params.value, "Paper") },
-            { field: "requirements", cellRenderer: params => LinkRenderer(github_url + params.value, "Requirements.txt") },
-            // { field: "quick-start" },
-            // { field: "deep-dive" }
+            { field: "Year" },
+            { 
+                field: "Name",
+                headerName: "Model Name (Hover over for package name)",
+                
+                flex: 4,
+                cellRenderer: params => LinkRenderer(params.data.docs, params.data.Name),
+                tooltipValueGetter: (params) => "Package Name: " + params.data.packages,
+            },
+            { field: "Type", flex: 2 },
+            { 
+                field: "PyTorch", 
+                headerName: "PyTorch",
+                cellRenderer: params => params.value ? "✅" : "❌",
+            },
+            { 
+                field: "TensorFlow",
+                headerName: "TensorFlow",
+                cellRenderer: params => params.value ? "✅" : "❌",
+            },
         ],
         defaultColDef: {
             flex: 1,
             filter: true,
+            // floatingFilter: true,
         },
         pagination: true,
-        paginationAutoPageSize: true,
+        paginationAutoPageSize: true
     };
     // Create Grid: Create new grid within the #myGrid div, using the Grid Options object
     gridApi = agGrid.createGrid(document.querySelector("#grid"), gridOptions);

From f4ceb9f4f6c144f159958cd2d84626431a6e5c91 Mon Sep 17 00:00:00 2001
From: darrylong <darrylong@users.noreply.github.com>
Date: Fri, 14 Jun 2024 16:06:30 +0800
Subject: [PATCH 4/5] Add generate model json python file for js table

---
 docs/generate_model_js.py | 101 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 101 insertions(+)
 create mode 100644 docs/generate_model_js.py

diff --git a/docs/generate_model_js.py b/docs/generate_model_js.py
new file mode 100644
index 000000000..ba894314e
--- /dev/null
+++ b/docs/generate_model_js.py
@@ -0,0 +1,101 @@
+import json
+
+
+def get_key_val(part):
+    key_index_start = part.index('[')
+    key_index_end = part.index(']')
+    val_index_start = part.rindex('(')
+    val_index_end = part.rindex(')')
+
+    key = part[key_index_start + 1: key_index_end]
+    val = part[val_index_start + 1: val_index_end]
+
+    return key, val
+
+
+# Read the content from README.md
+with open('../README.md', 'r') as file:
+    content = file.read()
+
+# Extract the relevant information from the content
+models = []
+lines = content.split('\n')
+lines = lines[lines.index('## Models') + 4: lines.index('## Resources') - 2]
+
+headers = []
+headers = lines[0].split('|')[1:-1]
+headers = [header.strip() for header in headers]
+
+for line in lines[2:]:
+    parts = line.split('|')[1:-1]
+    parts = [part.strip() for part in parts]
+    model = dict(zip(headers, parts))
+    models.append(model)
+
+year = None
+
+for model in models:
+    # handle empty years
+    if model["Year"] == "":
+        model["Year"] = year
+    else:
+        year = model["Year"]
+        
+    # handle model, docs and paper part
+    name_paper_str = model["Model and Paper"]
+
+    for i, part in enumerate(name_paper_str.split(', ')):
+        key, val = get_key_val(part)
+
+        if i == 0:
+            model["Name"] = key
+            model["Link"] = val
+        else:
+            model[key] = val
+    
+    # handle environment part
+    
+    env_part = model["Environment"].split(', ')[0]
+    
+    search_dict = {
+            "PyTorch": "torch",
+            "TensorFlow": "tensorflow"
+    }
+    
+    if "requirements" in env_part:
+        _, requirements_dir = get_key_val(env_part)
+
+        # read requirements file
+        with open(f'../{requirements_dir}', 'r') as file:
+            requirements = file.read()
+            
+            for header, package in search_dict.items():
+                model[header] = package in requirements
+    else:
+        for header, _ in search_dict.items():
+            model[header] = False
+    
+    # remove non required keys
+    model.pop("Model and Paper")
+    model.pop("Environment")
+    
+    # Get package name
+    model_dir = model["Link"]
+    
+    with open(f'../{model_dir}/__init__.py', 'r') as file:
+        init_data = file.read()
+        
+        package_names = []
+        
+        for row in init_data.split('\n'):
+            if "import" in row:
+                package_name = row[row.index("import") + len("import "):]
+                package_names.append(f"cornac.models.{package_name}")
+        
+        model["packages"] = package_names
+
+json_str = json.dumps(models, indent=4)
+
+# Write the JSON object to a file
+with open('source/_static/models/data.js', 'w') as file:
+    file.write(f"var data = {json_str};")

From be55b7ecc4ba9dff0bd3da577163149c2c4f8dab Mon Sep 17 00:00:00 2001
From: darrylong <darrylong@users.noreply.github.com>
Date: Tue, 18 Jun 2024 11:10:50 +0800
Subject: [PATCH 5/5] Reset filter input when clear filters button is selected

---
 docs/source/_static/models/models.html | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/docs/source/_static/models/models.html b/docs/source/_static/models/models.html
index e478cd4c2..fcee3e469 100644
--- a/docs/source/_static/models/models.html
+++ b/docs/source/_static/models/models.html
@@ -56,6 +56,12 @@ <h3>Filters</h3>
         .then(() => {
             gridApi.onFilterChanged();
         });
+        // reset filter box
+        document.getElementById("filter-text-box").value="";
+        gridApi.setGridOption(
+            "quickFilterText",
+            "",
+        );
     }
 
     // Grid Options: Contains all of the grid configurations