Distributed-Representation Based Hybrid Recommender System with Short Item Descriptions
This addresses the challenge of insufficient rating data for new items in recommender systems, though it is an incremental improvement.
The paper tackles the cold-start problem in collaborative filtering by incorporating short item descriptions into a matrix factorization framework using distributed word representations, and shows empirical effectiveness compared to state-of-the-art approaches.
Collaborative filtering (CF) aims to build a model from users' past behaviors and/or similar decisions made by other users, and use the model to recommend items for users. Despite of the success of previous collaborative filtering approaches, they are all based on the assumption that there are sufficient rating scores available for building high-quality recommendation models. In real world applications, however, it is often difficult to collect sufficient rating scores, especially when new items are introduced into the system, which makes the recommendation task challenging. We find that there are often "short" texts describing features of items, based on which we can approximate the similarity of items and make recommendation together with rating scores. In this paper we "borrow" the idea of vector representation of words to capture the information of short texts and embed it into a matrix factorization framework. We empirically show that our approach is effective by comparing it with state-of-the-art approaches.