# From Word Embeddings to Item Recommendation

arXiv: https://arxiv.org/abs/1601.01356 · HTML: https://mlsanity.com/paper/1601.01356/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2016.

## Built on by (most cited first, 4 of 4)

- [Adaptive Clustering and Personalization in Multi-Agent Stochastic Linear Bandits](https://mlsanity.com/paper/2106.08902/) (2021)
- [Document Embedding for Scientific Articles: Efficacy of Word Embeddings vs TFIDF](https://mlsanity.com/paper/2107.05151/) (2021)
- [Using Person Embedding to Enrich Features and Data Augmentation for Classification](https://mlsanity.com/paper/2206.15162/) (2022)
- [Multi-Margin Cosine Loss: Proposal and Application in Recommender Systems](https://mlsanity.com/paper/2405.04614/) (2024)

## Abstract (arXiv)

Social network platforms can use the data produced by their users to serve them better. One of the services these platforms provide is recommendation service. Recommendation systems can predict the future preferences of users using their past preferences. In the recommendation systems literature there are various techniques, such as neighborhood based methods, machine-learning based methods and matrix-factorization based methods. In this work, a set of well known methods from natural language processing domain, namely Word2Vec, is applied to recommendation systems domain. Unlike previous works that use Word2Vec for recommendation, this work uses non-textual features, the check-ins, and it recommends venues to visit/check-in to the target users. For the experiments, a Foursquare check-in dataset is used. The results show that use of continuous vector space representations of items modeled by techniques of Word2Vec is promising for making recommendations.