Interest-based real-time content recommendation in online social communities
Interest-based real-time content recommendation in online social communities
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DOI：
10.1016/j.knosys.2011.09.019
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发表时间：
2012-04
期刊：
影响因子：
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通讯作者：
Dongsheng Li;Q. Lv;Xing Xie;L. Shang;Huanhuan Xia;T. Lu;Ning Gu
中科院分区：
文献类型：
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作者：
Dongsheng Li;Q. Lv;Xing Xie;L. Shang;Huanhuan Xia;T. Lu;Ning Gu
在线社交社区的快速发展和大量的用户生成内容对内容推荐系统提出了迫切的需求和新的挑战。该系统需要确定个人用户的独特和多样的兴趣，并实时向感兴趣的用户提供内容。在这项工作中，我们提出了Farseer，一个系统，个性化的实时内容推荐和交付在线社交社区。所提出的解决方案包括一组集成的离线和在线算法，识别和利用独特的基于项目的兴趣集群和基于集群的项目评级，以推荐新生成的内容项目给个人用户在真实的时间。我们的主要贡献是：（1）详细分析了在线社交社区中的内容流行度分布和用户兴趣分布;（2）一种新的基于兴趣的聚类和基于聚类的内容推荐解决方案;（3）在在线社交社区中的完整实现和部署。从真实世界的用户研究收集的评价结果表明，该系统优于三个广泛使用的协同过滤算法（kNN，PLSA，SVD）在现有的推荐系统。它可以有效地识别个人兴趣，提高在线社交社区中实时个性化内容推荐的质量和效率。
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.