mlsanityData to 7 Oct 2026

Item2Vec: Neural Item Embedding for Collaborative Filtering

arXiv 1603.04259 ↗First posted 14 Mar 2016cs.LG cs.AI cs.IRcited by 72

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Abstract (arXiv)

Many Collaborative Filtering (CF) algorithms are item-based in the sense that they analyze item-item relations in order to produce item similarities. Recently, several works in the field of Natural Language Processing (NLP) suggested to learn a latent representation of words using neural embedding algorithms. Among them, the Skip-gram with Negative Sampling (SGNS), also known as word2vec, was shown to provide state-of-the-art results on various linguistics tasks. In this paper, we show that item-based CF can be cast in the same framework of neural word embedding. Inspired by SGNS, we describe a method we name item2vec for item-based CF that produces embedding for items in a latent space. The method is capable of inferring item-item relations even when user information is not available. We present experimental results that demonstrate the effectiveness of the item2vec method and show it is competitive with SVD.

Key facts. Cited by 72 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Mar 2016.

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Citing papers per year: 2021: 17; 2022: 14; 2023: 15; 2024: 12; 2025: 5; 2026: 9.

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  1. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 307
  2. 2022Blurring-Sharpening Process Models for Collaborative Filteringcited by 35
  3. 2021Zero-Shot Recommendation as Language Modelingcited by 28
  4. 2021Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testingcited by 17
  5. 2021Drug Package Recommendation via Interaction-aware Graph Inductioncited by 14
  6. 2024Does Negative Sampling Matter? A Review with Insights into its Theory and Applicationscited by 14
  7. 2022SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLPcited by 12
  8. 2026AdapTools: Adaptive Tool-based Indirect Prompt Injection Attacks on Agentic LLMscited by 12
  9. 2023Deep Integrated Explanationscited by 9
  10. 2021Recommender systems based on graph embedding techniques: A comprehensive reviewcited by 8
  11. 2021Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gatescited by 8
  12. 2022Interpreting BERT-based Text Similarity via Activation and Saliency Mapscited by 8
38 more
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  2. 2022Supervised Contrastive Learning for Recommendationcited by 7
  3. 2021Empowering General-purpose User Representation with Full-life Cycle Behavior Modelingcited by 6
  4. 2022HINNPerf: Hierarchical Interaction Neural Network for Performance Prediction of Configurable Systemscited by 5
  5. 2023CCGen: Explainable Complementary Concept Generation in E-Commercecited by 5
  6. 2024Bridging Conversational and Collaborative Signals for Conversational Recommendationcited by 5
  7. 2021Theoretical Understandings of Product Embedding for E-commerce Machine Learningcited by 4
  8. 2021Explore User Neighborhood for Real-time E-commerce Recommendationcited by 4
  9. 2022Stock Embeddings: Learning Distributed Representations for Financial Assetscited by 4
  10. 2022MetricBERT: Text Representation Learning via Self-Supervised Triplet Trainingcited by 3
  11. 2023Representation Learning via Variational Bayesian Networkscited by 3
  12. 2023Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyondcited by 3
  13. 2023Learning to Explain: A Model-Agnostic Framework for Explaining Black Box Modelscited by 3
  14. 2023LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendationcited by 3
  15. 2021Neural Embeddings of Urban Big Data Reveal Emergent Structures in Citiescited by 2
  16. 2022Mitigating Filter Bubbles within Deep Recommender Systemscited by 2
  17. 2022Causal Structure Learning with Recommendation Systemcited by 2
  18. 2022Heterogeneous Information Crossing on Graphs for Session-based Recommender Systemscited by 2
  19. 2022Influential Recommender Systemcited by 2
  20. 2022Two Is Better Than One: Dual Embeddings for Complementary Product Recommendationscited by 2
  21. 2023TwERC: High Performance Ensembled Candidate Generation for Ads Recommendation at Twittercited by 2
  22. 2023DistDNAS: Search Efficient Feature Interactions within 2 Hourscited by 2
  23. 2025ImageGem: In-the-wild Generative Image Interaction Dataset for Generative Model Personalizationcited by 2
  24. 2021Graph Convolutional Embeddings for Recommender Systemscited by 1
  25. 2021RikoNet: A Novel Anime Recommendation Enginecited by 1
  26. 2023Pretrained Embeddings for E-commerce Machine Learning: When it Fails and Why?cited by 1
  27. 2023Social World Knowledge: Modeling and Applicationscited by 1
  28. 2024GACE: Learning Graph-Based Cross-Page Ads Embedding For Click-Through Rate Predictioncited by 1
  29. 2024Within-basket Recommendation via Neural Pattern Associatorcited by 1
  30. 2024E2Vec: Feature Embedding with Temporal Information for Analyzing Student Actions in E-Book Systemscited by 1
  31. 2024Enhancing Prediction Models with Reinforcement Learningcited by 1
  32. 2025Interact2Vec -- An efficient neural network-based model for simultaneously learning users and items embeddings in recommender systemscited by 1
  33. 2026Escaping the Euclidean Void: Manifold-Informed Flow Matching for Sequential Recommendationcited by 1
  34. 2021Variational Inference for Category Recommendation in E-Commerce platformscited by 0
  35. 2021Represent Items by Items: An Enhanced Representation of the Target Item for Recommendationcited by 0
  36. 2021DGEM: A New Dual-modal Graph Embedding Method in Recommendation Systemcited by 0
  37. 2021Aligning Hotel Embeddings using Domain Adaptation for Next-Item Recommendationcited by 0
  38. 2021SocialVec: Social Entity Embeddingscited by 0

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