Personalized news recommendation via implicit social experts
Personalized news recommendation via implicit social experts
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通过隐性社会专家进行个性化新闻推荐
DOI：
10.1016/j.ins.2013.08.034
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发表时间：
2014-01-01
影响因子：
8.1
通讯作者：
Li, Tao
中科院分区：
文献类型：
--
作者：
Lin, Chen;Xie, Runquan;Li, Tao
随着互联网的发展，个性化新闻推荐成为一个很有前途的研究方向。基于不同策略的各种新闻推荐系统已经被提出来为在线新闻读者提供新闻个性化服务。然而，很少有研究工作报告利用隐含的“社会”因素（即，新闻阅读界的潜在有影响力的专家），以促进新闻个性化。在本文中，我们探讨了整合基于内容的方法，协同过滤和信息扩散模型，采用概率矩阵分解技术的可行性。我们提出了一个新的个性化新闻推荐框架，通过隐式社会专家，其中潜在的影响者在虚拟社交网络上的意见，从隐式反馈提取作为辅助资源推荐。我们评估和比较我们提出的推荐方法与不同的基线上收集的新闻文章从多个流行的新闻网站。实验结果表明，我们的方法的有效性和有效性，特别是在处理所谓的冷启动问题。(C)2013爱思唯尔公司All rights reserved.
.Personalized news recommendation has become a promising research direction as the Internet provides fast access to real-time information around the world. A variety of news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing the implicit "social" factors (i.e., the potential influential experts in news reading community) among news readers to facilitate news personalization. In this paper, we investigate the feasibility of integrating content-based methods, collaborative filtering and information diffusion models by employing probabilistic matrix factorization techniques. We propose PRemiSE, a novel Personalized news Recommendation framework via implicit Social Experts, in which the opinions of potential influencers on virtual social networks extracted from implicit feedbacks are treated as auxiliary resources for recommendation. We evaluate and compare our proposed recommendation method with various baselines on a collection of news articles obtained from multiple popular news websites. Experimental results demonstrate the efficacy and effectiveness of our method, particularly, on handling the so-called cold-start problem. (C) 2013 Elsevier Inc. All rights reserved.