解决推荐系统冷启动问题的混合人气模型

Noor Ifada, Ummamah, M. Kautsar
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引用次数: 2

摘要

针对推荐系统中的冷启动问题,提出了一种新的混合人气模型。当目标用户在系统中没有评级历史记录时,就会出现冷启动问题。混合流行度模型结合了用户和物品流行度的好处。物品流行度模型假定目标用户最有可能喜欢排名靠前的物品。而用户受欢迎度模型假定目标用户可能会受到给予大量评分的顶级用户的影响。当然,我们提出的HPop模型分为三个阶段:项目受欢迎程度、用户受欢迎程度和混合受欢迎程度。项目和用户受欢迎程度的比率由α的使用控制。我们使用归一化贴现累积增益(NDCG),以及精度和召回指标来评估我们的模型及其对应的性能,即IPop和UPop。使用真实世界的MovieLens数据集,我们的实验表明,使用用户流行度模型总是比项目流行度模型更有益。当α = 0.9时,HPop表现最佳,当α = 1时表现最差。从HPop到IPop和UPop的NDCG平均增幅分别为12.22%和8.02%。在精确召回率方面的结果也显示出与NDCG相似的趋势。因此,我们推测HPop、IPop和UPop的性能在任何评价指标中都是稳定的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid popularity model for solving cold-start problem in recommendation system
This research proposes a new hybrid popularity model for solving the cold-start problem in the recommendation system. A cold-start problem arises when the target user has no rating history in the system. A hybrid popularity model combines the benefit of both the user and item popularities. The item popularity model assumes that a target user is most expected to like the top-rated items. Whereas the user popularity model presumes that a target user is likely to be influenced by the top users who have given a large number of ratings. Naturally, our proposed HPop model is built in three phases: item popularity, user popularity, and hybrid popularity. The ratio of the item and user popularities are controlled by the use of α. We use the Normalized Discounted Cumulative Gain (NDCG), as well as Precision and Recall metrics to evaluate the performance of our model and its counterparts, i.e., IPop and UPop. Using a real-world MovieLens dataset, our experiments show that the employment of the user popularity model is always more beneficial than the item popularity model. HPop performs best when α = 0.9 and worst when α = 1. The NDCG average of increases from HPop to IPop and UPop are respectively 12.22% and 8.02%. The results in terms of Precision-Recall also show a similar trend to those of NDCG. Hence, we conjecture that the performances of HPop, IPop, and UPop are stable in any evaluation metrics.
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