# Item2Vec: Neural Item Embedding for Collaborative Filtering

arXiv: https://arxiv.org/abs/1603.04259 · HTML: https://mlsanity.com/paper/1603.04259/

## Key facts

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

## Built on by (most cited first, 30 of 72)

- [Large Language Models are Zero-Shot Rankers for Recommender Systems](https://mlsanity.com/paper/2305.08845/) (2023)
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- [Interpreting BERT-based Text Similarity via Activation and Saliency Maps](https://mlsanity.com/paper/2208.06612/) (2022)
- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)
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- [Click-through Rate Prediction with Auto-Quantized Contrastive Learning](https://mlsanity.com/paper/2109.13921/) (2021)
- [Supervised Contrastive Learning for Recommendation](https://mlsanity.com/paper/2201.03144/) (2022)
- [Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling](https://mlsanity.com/paper/2110.11337/) (2021)
- [HINNPerf: Hierarchical Interaction Neural Network for Performance Prediction of Configurable Systems](https://mlsanity.com/paper/2204.03931/) (2022)
- [CCGen: Explainable Complementary Concept Generation in E-Commerce](https://mlsanity.com/paper/2305.11480/) (2023)
- [Bridging Conversational and Collaborative Signals for Conversational Recommendation](https://mlsanity.com/paper/2412.06949/) (2024)
- [Theoretical Understandings of Product Embedding for E-commerce Machine Learning](https://mlsanity.com/paper/2102.12029/) (2021)
- [Explore User Neighborhood for Real-time E-commerce Recommendation](https://mlsanity.com/paper/2103.00442/) (2021)
- [Stock Embeddings: Learning Distributed Representations for Financial Assets](https://mlsanity.com/paper/2202.08968/) (2022)
- [Learning to Explain: A Model-Agnostic Framework for Explaining Black Box Models](https://mlsanity.com/paper/2310.16584/) (2023)
- [MetricBERT: Text Representation Learning via Self-Supervised Triplet Training](https://mlsanity.com/paper/2208.06610/) (2022)
- [Representation Learning via Variational Bayesian Networks](https://mlsanity.com/paper/2306.16326/) (2023)
- [Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyond](https://mlsanity.com/paper/2308.14753/) (2023)
- [LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendation](https://mlsanity.com/paper/2310.19394/) (2023)
- [Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities](https://mlsanity.com/paper/2110.12371/) (2021)
- [Mitigating Filter Bubbles within Deep Recommender Systems](https://mlsanity.com/paper/2209.08180/) (2022)
- [Causal Structure Learning with Recommendation System](https://mlsanity.com/paper/2210.10256/) (2022)
- [Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems](https://mlsanity.com/paper/2210.12940/) (2022)

## 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.