IEEE Trans Cybern. 2017 Dec;47(12):4049-4061. doi: 10.1109/TCYB.2016.2595620. Epub 2016 Sep 12.
Recommender systems aim to identify relevant items for particular users in large-scale online applications. The historical rating data of users is a valuable input resource for many recommendation models such as collaborative filtering (CF), but these models are known to suffer from the rating sparsity problem when the users or items under consideration have insufficient rating records. With the continued growth of online social networks, the increased user-to-user relationships are reported to be helpful and can alleviate the CF rating sparsity problem. Although researchers have developed a range of social network-based recommender systems, there is no unified model to handle multirelational social networks. To address this challenge, this paper represents different user relationships in a multigraph and develops a multigraph ranking model to identify and recommend the nearest neighbors of particular users in high-order environments. We conduct empirical experiments on two real-world datasets: 1) Epinions and 2) Last.fm, and the comprehensive comparison with other approaches demonstrates that our model improves recommendation performance in terms of both recommendation coverage and accuracy, especially when the rating data are sparse.
推荐系统旨在为大型在线应用中的特定用户识别相关项目。用户的历史评级数据是许多推荐模型（如协同过滤（CF））的宝贵输入资源，但这些模型已知在考虑的用户或项目的评分记录不足时会受到评分稀疏问题的影响。随着在线社交网络的持续增长，据报道，增加的用户-用户关系是有帮助的，并可以缓解 CF 评分稀疏问题。尽管研究人员已经开发了一系列基于社交网络的推荐系统，但没有统一的模型来处理多关系社交网络。为了解决这一挑战，本文将多关系表示为一个多图，并开发了一个多图排序模型，以在高阶环境中识别和推荐特定用户的最近邻居。我们在两个真实数据集上进行了实证实验：1） Epinions 和 2） Last.fm，与其他方法的综合比较表明，我们的模型在推荐覆盖率和准确性方面都提高了推荐性能，尤其是在评分数据稀疏的情况下。
