Collaborative Item Embedding Model for Implicit Feedback Data

TL;DR

This paper introduces a novel item embedding approach integrated with matrix factorization to better capture local item relationships, improving top-n recommendation accuracy in collaborative filtering systems.

Contribution

It presents a new method combining item embedding with matrix factorization, enhancing the modeling of local item relationships in collaborative filtering.

Findings

Outperforms existing methods on three real-world datasets

Improves top-n recommendation accuracy

Effectively captures local item relationships

Abstract

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.