AI powered open source recommender system engine supports classical/LLM rankers and multimodal content via embedding
- recommender-system
- collaborative-filtering
- go
- knn
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AI powered open source recommender system engine supports classical/LLM rankers and multimodal content via embedding
A Python scikit for building and analyzing recommender systems
Recommend new arxiv papers of your interest daily according to your Zotero libarary.
Intelligent faculty outreach and research matching for prospective graduate applicants
Web interface for browsing, search and filtering recent arxiv submissions
A unified, comprehensive and efficient recommendation library
Fast Python Collaborative Filtering for Implicit Feedback Datasets
本地私有、开源的自进化跨平台 AI 内容发现 Agent:先理解你,再主动从 B站、小红书、抖音、YouTube、X、知乎、Reddit、微博等平台与开放 Web 寻找内容。(支持 deepseek harness 插件) | Local-first open-source cross-platform AI content discovery agent: understands you, then proactively finds content across Bilibili, Xiaohongshu, Douyin, YouTube, X, Zhihu, Reddit, Weibo and the open web.(support deepseek harness plugin)
历史学习项目:Spark 电影推荐、爬虫、Web 与后台。新版持续开发见 cinematch-recommender。
All-in-one platform for search, recommendations, RAG, and analytics offered via API
A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors which can be used for ANN search.
TensorFlow Recommenders is a library for building recommender system models using TensorFlow.
Repository hosting code for "Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations" (https://arxiv.org/abs/2402.17152).
Neural Collaborative Filtering
arxiv-sanity lite: tag arxiv papers of interest get recommendations of similar papers in a nice UI using SVMs over tfidf feature vectors based on paper abstracts.
Generate recommendations using collaborative filtering
A C library for product recommendations/suggestions using collaborative filtering (CF)
Collaborative Filtering (CF) Algorithms in Go!
A Macau food recommendation Agent built on Thompson Sampling + StackedPareto weight fusion, with a full evaluation pipeline (5-fold CV, bootstrap CIs, stress tests) on 5,000 simulated users and 100k interactions. Competition entry for the QwenPaw AI Developer Challenge.
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