基于位置和社交网络感知的多目标优化推荐

Makbule Gülçin Özsoy, Faruk Polat, R. Alhajj
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引用次数: 13

摘要

社交网络、个人博客页面、在线交易网站、专业网页和基于位置的社交网络为数百万用户提供了一个有吸引力的平台来分享意见、评论、评级等。拥有这种多样化和全面的信息导致用户难以得出最合适和最可靠的结论。推荐系统通过提供个性化的服务,是解决信息过载问题的解决方案之一。在推荐系统中使用空间、时间和社会信息是提高性能的最新趋势。此外,考虑多个标准可以提高推荐系统的性能。本文提出并描述了一种基于多目标过滤的位置感知和社交网络感知推荐系统。结果表明,该方法在保持精度的前提下达到了较高的覆盖率。此外,该方法不受评级范围的影响,并在不同设置下提供持久的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-objective optimization based location and social network aware recommendation
Social networks, personal blog pages, on-line transaction web-sites, expertise web pages and location based social networks provide an attractive platform for millions of users to share opinions, comments, ratings, etc. Having this kind of diverse and comprehensive information leads to difficulties for users to reach the most appropriate and reliable conclusions. Recommendation systems form one of the solutions to deal with the information overload problem by providing personalized services. Using spatial, temporal and social information on recommender systems is a recent trend that increases the performance. Also, taking into account more than one criterion can improve the performance of the recommender systems. In this paper, a location and social network aware recommender system enhanced with multi objective filtering is proposed and described. The results show that the proposed method reaches high coverage while preserving precision. Besides, the proposed method is not affected by the range of ratings and provides persistent results in different settings.
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