ADAPTIVE FUSION METHOD FOR USER-BASED AND ITEM-BASED COLLABORATIVE FILTERING
ADAPTIVE FUSION METHOD FOR USER-BASED AND ITEM-BASED COLLABORATIVE FILTERING
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DOI：
10.1142/s0219525911003001
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发表时间：
2011-04-01
影响因子：
0.4
通讯作者：
Suzuki, Keiji
中科院分区：
文献类型：
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作者：
Yamashita, Akihiro;Kawamura, Hidenori;Suzuki, Keiji
在许多电子商务网站中，最近已经引入了从大量项目中提供个性化推荐的推荐系统。协同过滤是最成功的算法之一，它使用用户对项目的评级来提供推荐。有两种方法：基于用户的和基于项目的协同过滤。此外，提出了一种统一的基于用户和基于项目的协同过滤方法，以提高推荐的准确性。统一的方法使用一个恒定的值作为权重参数，以统一两种算法。然而，由于统一的最佳权重实际上是不同的情况下，该算法应该动态地估计一个适当的权重，并应使用它。在本研究中，我们首先调查推荐精度和权重参数之间的关系。结果表明，最优权重因情况而异。其次，我们提出了一种方法来估计适当的权重值的基础上收集的评级。然后，我们讨论了所提出的方法的有效性基于多智能体模拟和MovieLens数据集。结果表明，该方法可以估计的最佳权重的误差率为0.5%的权重值。
In many e-commerce sites, recommender systems, which provide personalized recommendations from among a large number of items, have recently been introduced. Collaborative filtering is one of the most successful algorithms which provide recommendations using ratings of users on items. There are two approaches: user-based and item-based collaborative filtering. Additionally a unifying method for user-based and item-based collaborative filtering was proposed to improve the recommendation accuracy. The unifying approach uses a constant value as a weight parameter to unify both algorithms. However, because the optimal weight for unifying is actually different depending on the situation, the algorithm should estimate an appropriate weight dynamically, and should use it. In this research, we first investigate the relationship between recommendation accuracy and the weight parameter. The results show that the optimal weight is different depending on the situation. Second, we propose an approach for estimation of the appropriate weight value based on collected ratings. Then, we discuss the effectiveness of the proposed approach based on both multi-agent simulation and the MovieLens dataset. The results show that the proposed approach can estimate the weight value within an error rate of 0.5% for the optimal weight.