Abstract
A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.
Highlights
IntroductionInformation retrieval (IR) [1] is a field to search for documents that are suitable for users’
Since there is no information on actors and directors in the MovieLens dataset, as shown in
The results of the multiple bias analysis (MBA) experiment were analyzed separately for the Vanilla model in Section 4.3.1, for the heuristic approach in Section 4.3.2, for the hybrid model in Section 4.3.3, and all of the experimental results were integrated in Section 4.3.4 to compare the accuracy
Summary
Information retrieval (IR) [1] is a field to search for documents that are suitable for users’. Information needs, and recommender systems (RSs) are an application field that is derived from IR. An RS is an agent that recommends items that are suitable for users, and collaborative filtering (CF) is typically used. CF is divided into memorybased CF (User-based CF (UBCF) [2,3]), Item-based CF (IBCF) [3,4,5,6]), model-based CF (factorization model [7,8,9,10]), content-based CF [11], and context-aware CF [12], and there is a hybrid model that synergizes the foregoing CF models to complement their advantages and disadvantages [13,14]
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