A Distributed Collaborative Filtering Algorithm Using Multiple Data Sources
This addresses the problem of inaccurate recommendations due to sparse user-item data for users and providers of recommender systems, but it is incremental as it builds on existing distributed and multi-source approaches.
The paper tackles the data sparsity problem in collaborative filtering for recommender systems by proposing a distributed algorithm that exploits multiple data sources, such as social networks and tags, to improve recommendation quality, with experimental results showing effectiveness compared to state-of-the-art methods.
Collaborative Filtering (CF) is one of the most commonly used recommendation methods. CF consists in predicting whether, or how much, a user will like (or dislike) an item by leveraging the knowledge of the user's preferences as well as that of other users. In practice, users interact and express their opinion on only a small subset of items, which makes the corresponding user-item rating matrix very sparse. Such data sparsity yields two main problems for recommender systems: (1) the lack of data to effectively model users' preferences, and (2) the lack of data to effectively model item characteristics. However, there are often many other data sources that are available to a recommender system provider, which can describe user interests and item characteristics (e.g., users' social network, tags associated to items, etc.). These valuable data sources may supply useful information to enhance a recommendation system in modeling users' preferences and item characteristics more accurately and thus, hopefully, to make recommenders more precise. For various reasons, these data sources may be managed by clusters of different data centers, thus requiring the development of distributed solutions. In this paper, we propose a new distributed collaborative filtering algorithm, which exploits and combines multiple and diverse data sources to improve recommendation quality. Our experimental evaluation using real datasets shows the effectiveness of our algorithm compared to state-of-the-art recommendation algorithms.