Analyzing Potential of Personal Values-Based User Modeling for Long Tail Item Recommendation
Analyzing Potential of Personal Values-Based User Modeling for Long Tail Item Recommendation
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DOI：
10.20965/jaciii.2018.p0506
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Y. Takama;Yu-Sheng Chen;Ryori Misawa;H. Ishikawa
中科院分区：
文献类型：
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作者：
Y. Takama;Yu-Sheng Chen;Ryori Misawa;H. Ishikawa
本文研究了基于个人价值观的用户建模在长尾项目推荐中的潜力。长尾项目被定义为那些不受欢迎但受到少数特定用户青睐的项目。尽管向相关用户推荐长尾项目对此类项目的提供者和消费者都有好处，但众所周知，这对大多数推荐算法来说是一个挑战。特别是，长尾项目是由少数用户购买和/或评分的项目，因此很难准确预测其评分。本文假设用户评价长尾项目时个人价值观的影响更加明显，并通过线下实验对其进行检验。评级匹配率（RMRate）的提出是为了将用户的个人价值观纳入推荐系统。由于 RMRate 将个人价值观建模为项目属性的权重，因此很容易合并到现有的推荐算法中。进行了实验来评估长尾项目推荐的性能；实验结果表明，基于个人价值观的用户建模可以在保持精度的同时推荐不太受欢迎的商品。
This paper examines the potential of personal values-based user modeling for long tail item recommendation. Long tail items are defined as those which are not popular but are preferred by small numbers of specific users. Although recommending long tail items to relevant users is beneficial for both the providers and consumers of such items, it is known to be a challenge for most recommendation algorithms. In particular, a long tail item is one that would be purchased and/or rated by a small number of users, so it is difficult to predict its rating accurately. This paper assumes that the influence of personal values becomes more obvious when users evaluate long tail items, and examines it through offline experiment. The Rating Matching Rate (RMRate) has been proposed in order to incorporate users’ personal values into recommender systems. As the RMRate models personal values as the weight of an item’s attribute, it is easy to incorporate into existing recommendation algorithms. An experiment was conducted to evaluate the performance of long tail item recommendation; Experimental result shows that personal values-based user modeling can recommend less popular items while maintaining precision.