# Collaborative Item Embedding Model for Implicit Feedback Data

arXiv: https://arxiv.org/abs/1805.05005 · HTML: https://mlsanity.com/paper/1805.05005/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2018.

## Built on by (most cited first, 1 of 1)

- [ImplicitSLIM and How it Improves Embedding-based Collaborative Filtering](https://mlsanity.com/paper/2406.00198/) (2024)

## Abstract (arXiv)

Collaborative filtering is the most popular approach for recommender systems. One way to perform collaborative filtering is matrix factorization, which characterizes user preferences and item attributes using latent vectors. These latent vectors are good at capturing global features of users and items but are not strong in capturing local relationships between users or between items. In this work, we propose a method to extract the relationships between items and embed them into the latent vectors of the factorization model. This combines two worlds: matrix factorization for collaborative filtering and item embed- ding, a similar concept to word embedding in language processing. Our experiments on three real-world datasets show that our proposed method outperforms competing methods on top-n recommendation tasks.