基于随机漫步和重启的混合推荐系统

Arif Wijonarko, Dade Nurjanah, D. S. Kusumo
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引用次数: 3

摘要

社会标签系统(STS)是一个非常流行的网络应用程序,数以百万计的人加入了这个系统,并积极地分享他们的内容。这些数量庞大的用户以不受限制的方式将内容和标签淹没在STS中,威胁到系统检索相关内容和信息共享的能力。推荐系统(RS)是一种通过过滤相关内容而非相关内容来克服信息过载问题的成功方法。除了管理大众分类法信息,STS还处理用户的社交网络信息。这两个信息都可以被RS用来为它的用户生成一个好的推荐。本文提出了一种针对现有混合推荐系统的改进方法,将社交网络信息纳入混合推荐系统的输入中。推荐生成过程包括随机行走与重启(RWR)以及基于内容的过滤(CBF)和协同过滤(CF)方法。在系统中引入了一些参数来控制每种方法的权重贡献。在两个领域,社交书签(Delicious.com)和音乐分享(Last.fm),对一组真实世界的开放数据集进行了全面的实验,以测试所提出的混合推荐系统。结果表明,与现有方法相比,该方法在准确性方面有很大提高。该混合算法在Delicious数据集上的RWR比前者高24.4%,在Lastfm数据集上的CBF比后者高53.85%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid recommender system using random walk with restart for social tagging system
Social Tagging Systems (STS) are very popular web application such that millions of people join the systems and actively share their contents. These enormous number of users are flooding STS with contents and tags in an unrestrained way in that threatening the capability of the system for relevant content retrieval and information sharing. Recommender Systems (RS) is a known successful method for overcome information overload problem by filtering the relevant contents over the nonrelevant contents. Besides manage folksonomy information, STS also handle social network information of its users. Both information can be used by RS to generate a good recommendation for its users. This work proposes an enhanced method for an existing hybrid recommender system, by incorporating social network information into the input of the hybrid recommender. The recommendation generation process includes Random Walk with Restart (RWR) alongside Content-Based Filtering (CBF) and Collaborative Filtering (CF) methods. Some parameters were introduced in the system to control weight contribution of each method. A comprehensive experiment with a set of a real-world open data set in two areas, social bookmark (Delicious.com) and music sharing (Last.fm) to test the proposed hybrid recommender system. The outcomes exhibit that it can give improvement compared to an existing method in terms of accuracy. The proposed hybrid achieves 24.4% more than RWR on the Delicious dataset, and 53.85% more than CBF on Lastfm dataset.
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