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collaborativefiltering

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Here are 9 public repositories matching this topic...

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RiyaBhandari07 / HCP-Hybrid-Recommender-System-v0.1

Star 1

HCP Hybrid Recommender System v0.1: A healthcare marketing recommendation engine using LSTM, topic modeling, and collaborative/content-based filtering to predict personalized content and channel engagement for HCPs. Includes data preprocessing, model training, and a Streamlit dashboard for interactive visualization.

pythonmachine-learningtensorflowhealthcarerecommender-systemdeeplearningnlp-machine-learninglstm-neural-networkscontent-based-recommendationtopicmodelingcollaborativefilteringstreamlit-dashboardshealthcare-marketing
  • Updated Aug 30, 2026
  • Jupyter Notebook

AchintaMitra23 / Movie-Recommendation-System-Using-Sentiment-Analysis

Star 0

System is going to filter out the best possible movies basis on some criteria in recommendation area even after analyzing and previewing the reviews of the particular movie using sentiment analysis theory.

pythonnlpmachine-learningnaive-bayes-classifiercosine-similaritycount-vectorizerknn-classificationpearson-correlationcollaborativefilteringtfidf-vectorizercontentbasedfiltering
  • Updated Sep 21, 2021
  • Jupyter Notebook

Biplabini-1992 / Zee_Recommender_System

Star 0

Zee Recommender Systems is a personalized movie recommendation project built using the MovieLens dataset. It implements collaborative filtering, similarity-based models, and matrix factorization to enhance user experience by suggesting movies tailored to individual preferences. Includes EDA, evaluation (RMSE & MAPE) and visualization of embeddings.

pythonedamachinelearningknnmovielenscollaborativefilteringmatrixfactorizationrecommendersystem
  • Updated Dec 26, 2025
  • Jupyter Notebook
Shopper-Spectrum_-Segmentation-and-Recomm

rahulraimau / Shopper-Spectrum_-Segmentation-and-Recomm

Star 0

Shopper Spectrum: Streamlit app for customer segmentation (RFM + KMeans) and product recommendations (collaborative filtering) using e-commerce data.

numpyedapandasmachinelearningdatavisualizationdatacleaningcollaborativefilteringcosinesimilaritystandardscalerfeatureengineeringscikitlearndatatransformationkmeansclusteringpivottablesstreamlitappcustomersegmentationrfmanalysisrealtimepredictionproductrecommendation
  • Updated Jul 27, 2025
  • HTML

qvunguyen / movie-recommendation-system

Star 0

The Movie Recommendation System is a Python application that provides personalized movie suggestions using collaborative and content-based filtering techniques. Utilizing the MovieLens 25M dataset, it offers customizable recommendations based on user ID, movie title, and desired suggestion count, creating an engaging and tailored movie discovery.

collaborativefilteringmovierecommendationcontentbasedfilteringpersonalizedsuggestionsmovielensdataset
  • Updated Apr 4, 2023
  • Jupyter Notebook

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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#


collaborativefiltering


Star


Here are
9 public repositories
matching this topic...

Language:All
Filter by language

All
9
Jupyter Notebook
5
HTML
1
Java
1
Python
1
RiyaBhandari07 /
HCP-Hybrid-Recommender-System-v0.1

Star
1
 HCP Hybrid Recommender System v0.1: A healthcare marketing recommendation engine using LSTM, topic modeling, and collaborative/content-based filtering to predict personalized content and channel engagement for HCPs. Includes data preprocessing, model training, and a Streamlit dashboard for interactive visualization.
pythonmachine-learningtensorflowhealthcarerecommender-systemdeeplearningnlp-machine-learninglstm-neural-networkscontent-based-recommendationtopicmodelingcollaborativefilteringstreamlit-dashboardshealthcare-marketing
  • 
Updated
Aug 30, 2026
  • Jupyter Notebook
AchintaMitra23 /
Movie-Recommendation-System-Using-Sentiment-Analysis

Star
0
System is going to filter out the best possible movies basis on some criteria in recommendation area even after analyzing and previewing the reviews of the particular movie using sentiment analysis theory. 
pythonnlpmachine-learningnaive-bayes-classifiercosine-similaritycount-vectorizerknn-classificationpearson-correlationcollaborativefilteringtfidf-vectorizercontentbasedfiltering
  • 
Updated
Sep 21, 2021
  • Jupyter Notebook
Biplabini-1992 /
Zee_Recommender_System

Star
0
Zee Recommender Systems is a personalized movie recommendation project built using the MovieLens dataset. It implements collaborative filtering, similarity-based models, and matrix factorization to enhance user experience by suggesting movies tailored to individual preferences. Includes EDA, evaluation (RMSE & MAPE) and visualization of embeddings.
pythonedamachinelearningknnmovielenscollaborativefilteringmatrixfactorizationrecommendersystem
  • 
Updated
Dec 26, 2025
  • Jupyter Notebook
Shopper-Spectrum_-Segmentation-and-Recomm
rahulraimau /
Shopper-Spectrum_-Segmentation-and-Recomm

Star
0
Shopper Spectrum: Streamlit app for customer segmentation (RFM + KMeans) and product recommendations (collaborative filtering) using e-commerce data.
numpyedapandasmachinelearningdatavisualizationdatacleaningcollaborativefilteringcosinesimilaritystandardscalerfeatureengineeringscikitlearndatatransformationkmeansclusteringpivottablesstreamlitappcustomersegmentationrfmanalysisrealtimepredictionproductrecommendation
  • 
Updated
Jul 27, 2025
  • HTML
qvunguyen /
movie-recommendation-system

Star
0
The Movie Recommendation System is a Python application that provides personalized movie suggestions using collaborative and content-based filtering techniques. Utilizing the MovieLens 25M dataset, it offers customizable recommendations based on user ID, movie title, and desired suggestion count, creating an engaging and tailored movie discovery.
collaborativefilteringmovierecommendationcontentbasedfilteringpersonalizedsuggestionsmovielensdataset
  • 
Updated
Apr 4, 2023
  • Jupyter Notebook

Add this topic to your repo


To associate your repository with the
collaborativefiltering
topic, visit your repo's landing page and select "manage topics."


Learn more


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

#


collaborativefiltering


Star


Here are
9 public repositories
matching this topic...

Language:All
Filter by language

All
9
Jupyter Notebook
5
HTML
1
Java
1
Python
1
RiyaBhandari07 /
HCP-Hybrid-Recommender-System-v0.1

Star
1
 HCP Hybrid Recommender System v0.1: A healthcare marketing recommendation engine using LSTM, topic modeling, and collaborative/content-based filtering to predict personalized content and channel engagement for HCPs. Includes data preprocessing, model training, and a Streamlit dashboard for interactive visualization.
pythonmachine-learningtensorflowhealthcarerecommender-systemdeeplearningnlp-machine-learninglstm-neural-networkscontent-based-recommendationtopicmodelingcollaborativefilteringstreamlit-dashboardshealthcare-marketing
  • 
Updated
Aug 30, 2026
  • Jupyter Notebook
AchintaMitra23 /
Movie-Recommendation-System-Using-Sentiment-Analysis

Star
0
System is going to filter out the best possible movies basis on some criteria in recommendation area even after analyzing and previewing the reviews of the particular movie using sentiment analysis theory. 
pythonnlpmachine-learningnaive-bayes-classifiercosine-similaritycount-vectorizerknn-classificationpearson-correlationcollaborativefilteringtfidf-vectorizercontentbasedfiltering
  • 
Updated
Sep 21, 2021
  • Jupyter Notebook
Biplabini-1992 /
Zee_Recommender_System

Star
0
Zee Recommender Systems is a personalized movie recommendation project built using the MovieLens dataset. It implements collaborative filtering, similarity-based models, and matrix factorization to enhance user experience by suggesting movies tailored to individual preferences. Includes EDA, evaluation (RMSE & MAPE) and visualization of embeddings.
pythonedamachinelearningknnmovielenscollaborativefilteringmatrixfactorizationrecommendersystem
  • 
Updated
Dec 26, 2025
  • Jupyter Notebook
Shopper-Spectrum_-Segmentation-and-Recomm
rahulraimau /
Shopper-Spectrum_-Segmentation-and-Recomm

Star
0
Shopper Spectrum: Streamlit app for customer segmentation (RFM + KMeans) and product recommendations (collaborative filtering) using e-commerce data.
numpyedapandasmachinelearningdatavisualizationdatacleaningcollaborativefilteringcosinesimilaritystandardscalerfeatureengineeringscikitlearndatatransformationkmeansclusteringpivottablesstreamlitappcustomersegmentationrfmanalysisrealtimepredictionproductrecommendation
  • 
Updated
Jul 27, 2025
  • HTML
qvunguyen /
movie-recommendation-system

Star
0
The Movie Recommendation System is a Python application that provides personalized movie suggestions using collaborative and content-based filtering techniques. Utilizing the MovieLens 25M dataset, it offers customizable recommendations based on user ID, movie title, and desired suggestion count, creating an engaging and tailored movie discovery.
collaborativefilteringmovierecommendationcontentbasedfilteringpersonalizedsuggestionsmovielensdataset
  • 
Updated
Apr 4, 2023
  • Jupyter Notebook

Add this topic to your repo


To associate your repository with the
collaborativefiltering
topic, visit your repo's landing page and select "manage topics."


Learn more


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

#


collaborativefiltering


Star


Here are
9 public repositories
matching this topic...

Language:All
Filter by language

All
9
Jupyter Notebook
5
HTML
1
Java
1
Python
1
RiyaBhandari07 /
HCP-Hybrid-Recommender-System-v0.1

Star
1
 HCP Hybrid Recommender System v0.1: A healthcare marketing recommendation engine using LSTM, topic modeling, and collaborative/content-based filtering to predict personalized content and channel engagement for HCPs. Includes data preprocessing, model training, and a Streamlit dashboard for interactive visualization.
pythonmachine-learningtensorflowhealthcarerecommender-systemdeeplearningnlp-machine-learninglstm-neural-networkscontent-based-recommendationtopicmodelingcollaborativefilteringstreamlit-dashboardshealthcare-marketing
  • 
Updated
Aug 30, 2026
  • Jupyter Notebook
AchintaMitra23 /
Movie-Recommendation-System-Using-Sentiment-Analysis

Star
0
System is going to filter out the best possible movies basis on some criteria in recommendation area even after analyzing and previewing the reviews of the particular movie using sentiment analysis theory. 
pythonnlpmachine-learningnaive-bayes-classifiercosine-similaritycount-vectorizerknn-classificationpearson-correlationcollaborativefilteringtfidf-vectorizercontentbasedfiltering
  • 
Updated
Sep 21, 2021
  • Jupyter Notebook
Biplabini-1992 /
Zee_Recommender_System

Star
0
Zee Recommender Systems is a personalized movie recommendation project built using the MovieLens dataset. It implements collaborative filtering, similarity-based models, and matrix factorization to enhance user experience by suggesting movies tailored to individual preferences. Includes EDA, evaluation (RMSE & MAPE) and visualization of embeddings.
pythonedamachinelearningknnmovielenscollaborativefilteringmatrixfactorizationrecommendersystem
  • 
Updated
Dec 26, 2025
  • Jupyter Notebook
Shopper-Spectrum_-Segmentation-and-Recomm
rahulraimau /
Shopper-Spectrum_-Segmentation-and-Recomm

Star
0
Shopper Spectrum: Streamlit app for customer segmentation (RFM + KMeans) and product recommendations (collaborative filtering) using e-commerce data.
numpyedapandasmachinelearningdatavisualizationdatacleaningcollaborativefilteringcosinesimilaritystandardscalerfeatureengineeringscikitlearndatatransformationkmeansclusteringpivottablesstreamlitappcustomersegmentationrfmanalysisrealtimepredictionproductrecommendation
  • 
Updated
Jul 27, 2025
  • HTML
qvunguyen /
movie-recommendation-system

Star
0
The Movie Recommendation System is a Python application that provides personalized movie suggestions using collaborative and content-based filtering techniques. Utilizing the MovieLens 25M dataset, it offers customizable recommendations based on user ID, movie title, and desired suggestion count, creating an engaging and tailored movie discovery.
collaborativefilteringmovierecommendationcontentbasedfilteringpersonalizedsuggestionsmovielensdataset
  • 
Updated
Apr 4, 2023
  • Jupyter Notebook

Add this topic to your repo


To associate your repository with the
collaborativefiltering
topic, visit your repo's landing page and select "manage topics."


Learn more


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content

#


collaborativefiltering


Star


Here are
9 public repositories
matching this topic...

Language:All
Filter by language

All
9
Jupyter Notebook
5
HTML
1
Java
1
Python
1
RiyaBhandari07 /
HCP-Hybrid-Recommender-System-v0.1

Star
1
 HCP Hybrid Recommender System v0.1: A healthcare marketing recommendation engine using LSTM, topic modeling, and collaborative/content-based filtering to predict personalized content and channel engagement for HCPs. Includes data preprocessing, model training, and a Streamlit dashboard for interactive visualization.
pythonmachine-learningtensorflowhealthcarerecommender-systemdeeplearningnlp-machine-learninglstm-neural-networkscontent-based-recommendationtopicmodelingcollaborativefilteringstreamlit-dashboardshealthcare-marketing
  • 
Updated
Aug 30, 2026
  • Jupyter Notebook
AchintaMitra23 /
Movie-Recommendation-System-Using-Sentiment-Analysis

Star
0
System is going to filter out the best possible movies basis on some criteria in recommendation area even after analyzing and previewing the reviews of the particular movie using sentiment analysis theory. 
pythonnlpmachine-learningnaive-bayes-classifiercosine-similaritycount-vectorizerknn-classificationpearson-correlationcollaborativefilteringtfidf-vectorizercontentbasedfiltering
  • 
Updated
Sep 21, 2021
  • Jupyter Notebook
Biplabini-1992 /
Zee_Recommender_System

Star
0
Zee Recommender Systems is a personalized movie recommendation project built using the MovieLens dataset. It implements collaborative filtering, similarity-based models, and matrix factorization to enhance user experience by suggesting movies tailored to individual preferences. Includes EDA, evaluation (RMSE & MAPE) and visualization of embeddings.
pythonedamachinelearningknnmovielenscollaborativefilteringmatrixfactorizationrecommendersystem
  • 
Updated
Dec 26, 2025
  • Jupyter Notebook
Shopper-Spectrum_-Segmentation-and-Recomm
rahulraimau /
Shopper-Spectrum_-Segmentation-and-Recomm

Star
0
Shopper Spectrum: Streamlit app for customer segmentation (RFM + KMeans) and product recommendations (collaborative filtering) using e-commerce data.
numpyedapandasmachinelearningdatavisualizationdatacleaningcollaborativefilteringcosinesimilaritystandardscalerfeatureengineeringscikitlearndatatransformationkmeansclusteringpivottablesstreamlitappcustomersegmentationrfmanalysisrealtimepredictionproductrecommendation
  • 
Updated
Jul 27, 2025
  • HTML
qvunguyen /
movie-recommendation-system

Star
0
The Movie Recommendation System is a Python application that provides personalized movie suggestions using collaborative and content-based filtering techniques. Utilizing the MovieLens 25M dataset, it offers customizable recommendations based on user ID, movie title, and desired suggestion count, creating an engaging and tailored movie discovery.
collaborativefilteringmovierecommendationcontentbasedfilteringpersonalizedsuggestionsmovielensdataset
  • 
Updated
Apr 4, 2023
  • Jupyter Notebook

Add this topic to your repo


To associate your repository with the
collaborativefiltering
topic, visit your repo's landing page and select "manage topics."


Learn more


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

#


collaborativefiltering


Star


Here are
9 public repositories
matching this topic...

Language:All
Filter by language

All
9
Jupyter Notebook
5
HTML
1
Java
1
Python
1
RiyaBhandari07 /
HCP-Hybrid-Recommender-System-v0.1

Star
1
 HCP Hybrid Recommender System v0.1: A healthcare marketing recommendation engine using LSTM, topic modeling, and collaborative/content-based filtering to predict personalized content and channel engagement for HCPs. Includes data preprocessing, model training, and a Streamlit dashboard for interactive visualization.
pythonmachine-learningtensorflowhealthcarerecommender-systemdeeplearningnlp-machine-learninglstm-neural-networkscontent-based-recommendationtopicmodelingcollaborativefilteringstreamlit-dashboardshealthcare-marketing
  • 
Updated
Aug 30, 2026
  • Jupyter Notebook
AchintaMitra23 /
Movie-Recommendation-System-Using-Sentiment-Analysis

Star
0
System is going to filter out the best possible movies basis on some criteria in recommendation area even after analyzing and previewing the reviews of the particular movie using sentiment analysis theory. 
pythonnlpmachine-learningnaive-bayes-classifiercosine-similaritycount-vectorizerknn-classificationpearson-correlationcollaborativefilteringtfidf-vectorizercontentbasedfiltering
  • 
Updated
Sep 21, 2021
  • Jupyter Notebook
Biplabini-1992 /
Zee_Recommender_System

Star
0
Zee Recommender Systems is a personalized movie recommendation project built using the MovieLens dataset. It implements collaborative filtering, similarity-based models, and matrix factorization to enhance user experience by suggesting movies tailored to individual preferences. Includes EDA, evaluation (RMSE & MAPE) and visualization of embeddings.
pythonedamachinelearningknnmovielenscollaborativefilteringmatrixfactorizationrecommendersystem
  • 
Updated
Dec 26, 2025
  • Jupyter Notebook
Shopper-Spectrum_-Segmentation-and-Recomm
rahulraimau /
Shopper-Spectrum_-Segmentation-and-Recomm

Star
0
Shopper Spectrum: Streamlit app for customer segmentation (RFM + KMeans) and product recommendations (collaborative filtering) using e-commerce data.
numpyedapandasmachinelearningdatavisualizationdatacleaningcollaborativefilteringcosinesimilaritystandardscalerfeatureengineeringscikitlearndatatransformationkmeansclusteringpivottablesstreamlitappcustomersegmentationrfmanalysisrealtimepredictionproductrecommendation
  • 
Updated
Jul 27, 2025
  • HTML
qvunguyen /
movie-recommendation-system

Star
0
The Movie Recommendation System is a Python application that provides personalized movie suggestions using collaborative and content-based filtering techniques. Utilizing the MovieLens 25M dataset, it offers customizable recommendations based on user ID, movie title, and desired suggestion count, creating an engaging and tailored movie discovery.
collaborativefilteringmovierecommendationcontentbasedfilteringpersonalizedsuggestionsmovielensdataset
  • 
Updated
Apr 4, 2023
  • Jupyter Notebook

Add this topic to your repo


To associate your repository with the
collaborativefiltering
topic, visit your repo's landing page and select "manage topics."


Learn more


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

#


collaborativefiltering


Star


Here are
9 public repositories
matching this topic...

Language:All
Filter by language

All
9
Jupyter Notebook
5
HTML
1
Java
1
Python
1
RiyaBhandari07 /
HCP-Hybrid-Recommender-System-v0.1

Star
1
 HCP Hybrid Recommender System v0.1: A healthcare marketing recommendation engine using LSTM, topic modeling, and collaborative/content-based filtering to predict personalized content and channel engagement for HCPs. Includes data preprocessing, model training, and a Streamlit dashboard for interactive visualization.
pythonmachine-learningtensorflowhealthcarerecommender-systemdeeplearningnlp-machine-learninglstm-neural-networkscontent-based-recommendationtopicmodelingcollaborativefilteringstreamlit-dashboardshealthcare-marketing
  • 
Updated
Aug 30, 2026
  • Jupyter Notebook
AchintaMitra23 /
Movie-Recommendation-System-Using-Sentiment-Analysis

Star
0
System is going to filter out the best possible movies basis on some criteria in recommendation area even after analyzing and previewing the reviews of the particular movie using sentiment analysis theory. 
pythonnlpmachine-learningnaive-bayes-classifiercosine-similaritycount-vectorizerknn-classificationpearson-correlationcollaborativefilteringtfidf-vectorizercontentbasedfiltering
  • 
Updated
Sep 21, 2021
  • Jupyter Notebook
Biplabini-1992 /
Zee_Recommender_System

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Zee Recommender Systems is a personalized movie recommendation project built using the MovieLens dataset. It implements collaborative filtering, similarity-based models, and matrix factorization to enhance user experience by suggesting movies tailored to individual preferences. Includes EDA, evaluation (RMSE & MAPE) and visualization of embeddings.
pythonedamachinelearningknnmovielenscollaborativefilteringmatrixfactorizationrecommendersystem
  • 
Updated
Dec 26, 2025
  • Jupyter Notebook
Shopper-Spectrum_-Segmentation-and-Recomm
rahulraimau /
Shopper-Spectrum_-Segmentation-and-Recomm

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Shopper Spectrum: Streamlit app for customer segmentation (RFM + KMeans) and product recommendations (collaborative filtering) using e-commerce data.
numpyedapandasmachinelearningdatavisualizationdatacleaningcollaborativefilteringcosinesimilaritystandardscalerfeatureengineeringscikitlearndatatransformationkmeansclusteringpivottablesstreamlitappcustomersegmentationrfmanalysisrealtimepredictionproductrecommendation
  • 
Updated
Jul 27, 2025
  • HTML
qvunguyen /
movie-recommendation-system

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The Movie Recommendation System is a Python application that provides personalized movie suggestions using collaborative and content-based filtering techniques. Utilizing the MovieLens 25M dataset, it offers customizable recommendations based on user ID, movie title, and desired suggestion count, creating an engaging and tailored movie discovery.
collaborativefilteringmovierecommendationcontentbasedfilteringpersonalizedsuggestionsmovielensdataset
  • 
Updated
Apr 4, 2023
  • Jupyter Notebook

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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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RiyaBhandari07 /
HCP-Hybrid-Recommender-System-v0.1

Star
1
 HCP Hybrid Recommender System v0.1: A healthcare marketing recommendation engine using LSTM, topic modeling, and collaborative/content-based filtering to predict personalized content and channel engagement for HCPs. Includes data preprocessing, model training, and a Streamlit dashboard for interactive visualization.
pythonmachine-learningtensorflowhealthcarerecommender-systemdeeplearningnlp-machine-learninglstm-neural-networkscontent-based-recommendationtopicmodelingcollaborativefilteringstreamlit-dashboardshealthcare-marketing
  • 
Updated
Aug 30, 2026
  • Jupyter Notebook
AchintaMitra23 /
Movie-Recommendation-System-Using-Sentiment-Analysis

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0
System is going to filter out the best possible movies basis on some criteria in recommendation area even after analyzing and previewing the reviews of the particular movie using sentiment analysis theory. 
pythonnlpmachine-learningnaive-bayes-classifiercosine-similaritycount-vectorizerknn-classificationpearson-correlationcollaborativefilteringtfidf-vectorizercontentbasedfiltering
  • 
Updated
Sep 21, 2021
  • Jupyter Notebook
Biplabini-1992 /
Zee_Recommender_System

Star
0
Zee Recommender Systems is a personalized movie recommendation project built using the MovieLens dataset. It implements collaborative filtering, similarity-based models, and matrix factorization to enhance user experience by suggesting movies tailored to individual preferences. Includes EDA, evaluation (RMSE & MAPE) and visualization of embeddings.
pythonedamachinelearningknnmovielenscollaborativefilteringmatrixfactorizationrecommendersystem
  • 
Updated
Dec 26, 2025
  • Jupyter Notebook
Shopper-Spectrum_-Segmentation-and-Recomm
rahulraimau /
Shopper-Spectrum_-Segmentation-and-Recomm

Star
0
Shopper Spectrum: Streamlit app for customer segmentation (RFM + KMeans) and product recommendations (collaborative filtering) using e-commerce data.
numpyedapandasmachinelearningdatavisualizationdatacleaningcollaborativefilteringcosinesimilaritystandardscalerfeatureengineeringscikitlearndatatransformationkmeansclusteringpivottablesstreamlitappcustomersegmentationrfmanalysisrealtimepredictionproductrecommendation
  • 
Updated
Jul 27, 2025
  • HTML
qvunguyen /
movie-recommendation-system

Star
0
The Movie Recommendation System is a Python application that provides personalized movie suggestions using collaborative and content-based filtering techniques. Utilizing the MovieLens 25M dataset, it offers customizable recommendations based on user ID, movie title, and desired suggestion count, creating an engaging and tailored movie discovery.
collaborativefilteringmovierecommendationcontentbasedfilteringpersonalizedsuggestionsmovielensdataset
  • 
Updated
Apr 4, 2023
  • Jupyter Notebook

Add this topic to your repo


To associate your repository with the
collaborativefiltering
topic, visit your repo's landing page and select "manage topics."


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