Collaborative Item Embedding Model for Implicit Feedback Data
This work addresses a specific bottleneck in recommender systems for users by enhancing matrix factorization with item embeddings, though it is incremental as it builds on existing methods.
The paper tackled the problem of capturing local relationships between items in collaborative filtering for recommender systems by proposing a method that integrates item embeddings into matrix factorization, resulting in improved performance on top-n recommendation tasks across three real-world datasets.
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.