AI powered open source recommender system engine supports classical/LLM rankers and multimodal content via embedding
- recommender-system
- collaborative-filtering
- go
- knn
77,353 visible repositories in the index
1–14 of 14 · Most stars
AI powered open source recommender system engine supports classical/LLM rankers and multimodal content via embedding
Intelligent faculty outreach and research matching for prospective graduate applicants
Web interface for browsing, search and filtering recent arxiv submissions
A Python implementation of LightFM, a hybrid recommendation algorithm.
A unified, comprehensive and efficient recommendation library
An implementation of a deep learning recommendation model (DLRM)
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)
AudioMuse-AI uses sonic analysis to rediscover forgotten songs, uncover hidden connections in your music library, and generate intelligent playlists for Navidrome, Jellyfin, LMS, Lyrion, Emby and Plex: no metadata or external services required.
All-in-one platform for search, recommendations, RAG, and analytics offered via API
A low code Machine Learning personalized ranking service for articles, listings, search results, recommendations that boosts user engagement. A friendly Learn-to-Rank engine
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 implementation of DeepFM for CTR prediction.
Repository hosting code for "Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations" (https://arxiv.org/abs/2402.17152).
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.