Abstract
The fast-growing popularity of online social communities and the massive amounts of user-generated content pose a critical need for, and new challenges on, content recommender system. The system needs to identify the unique and diverse interests of individual users and deliver content to interested users on a real-time basis. In this work, we propose Farseer, a system for personalized real-time content recommendation and delivery in online social communities. The proposed solution consists of a set of integrated offline and online algorithms that identify and utilize unique item-based interest clusters and cluster-based item rating in order to recommend newly-generated content items to individual users in real time. Our main contributions are (1) a detailed analysis of content popularity distribution and user interest distribution in online social communities; (2) a novel interest-based clustering and cluster-based content recommendation solution; and (3) a complete implementation and deployment in an online social community. Evaluation results gathered from real-world user studies demonstrate that the proposed system outperforms three widely-used collaborative filtering algorithms (kNN, PLSA, SVD) in existing recommender systems. It can effectively identify personal interests and improve the quality and efficiency of real-time personalized content recommendation in online social communities.
Published Version
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