[Pytorch] Generative retrieval model using semantic IDs from "Recommender Systems with Generative Retrieval"

Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression

The code to accompany the freeCodeCamp tutorial explaining how to use large language models to build a semantic book recommender.

Semantic IDs: How to train an LLM-Recommender Hybrid with steerability and reasoning on recommendations.

Bridging the Gap Between Semantic and Interaction Similarity in Recommender Systems

KDD2020 Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion

GenRec: Generative Recommender Systems with RQ-VAE semantic IDs, Transformer-based retrieval, and LLM integration. Built on PyTorch with distributed training support.

Implementation about a recommender System using RQ-VAE Semantic IDs

Official implementation of the paper "Rethinking Generative Recommender Tokenizer: Recsys-Native Encoding and Semantic Quantization Beyond LLMs"

Variable-Length Semantic IDs for Recommender Systems

基于HNSW的语义专家推荐系统

Semantic Book Recommender (Python, LLM, OpenAI, LangChain, Gradio)

Semantically Understanding Bias with LIME. Using the LIME-RS Algorithm to understand bias in recommender systems.

A recommender system based on semantic space clustering

Semantic book recommendation system using LLM embeddings, zero-shot classification, and emotion scoring with a Gradio dashboard.

This study aims to investigate the effectiveness of three Transformers (BERT, RoBERTa, XLNet) in handling data sparsity and cold start problems in the recommender system. We present a Transformer-based hybrid recommender system that predicts missing ratings and ex- tracts semantic embeddings from user reviews to mitigate the issues.

Daily arXiv paper recommender powered by semantic similarity to your Zotero library. Auto-analysis & Notion sync. 基于 Zotero 库语义相似度的每日 arXiv 论文推荐工具，AI 多模态分析 + 自动同步 Notion。

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Twitter semantic recommender based of entities detected by Wikipedia NER

Official code for "Enriching Semantic Profiles into Knowledge Graph for Recommender Systems Using Large Language Models" (KDD'26)

A complex pharmaceutical recommendation system that makes use of cutting-edge technologies like LangChain for dynamic information retrieval, Neo4j for effective data storage, and SBERT for semantic embedding.

Sistema di raccomandazione film basato su conoscenza, che unisce MovieLens e DBpedia via SPARQL. Usa una strategia Multi-Armed Bandit con softmax per suggerire film semanticamente affini agli interessi dell’utente, considerando generi, tag, attori, registi e anni.

Repository for the DASFAA 2025 Publication: Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systems

A thesis project on constraint programming and recommender system, leveraging semantic web and data mining technologies. It implements a course selection recommendation service based on input preference. The data model is based on Uppsala University's Computer Science Master programme education plan.

Clusterization of Twitter users and general-purpose recommender system

Using large language models to build a semantic book recommender.

A semantic recommender demo for the lablab.ai hackathon

AI-powered resume-to-job recommender system that extracts skills from resumes using spaCy + PDFMiner, applies semantic search with Transformers, and ranks matches with keyword heuristics. Includes an interactive Streamlit app with job title and location filters.

AI-powered article recommender using sentence embeddings and FAISS for semantic search. Includes a FastAPI backend and Streamlit frontend.

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