Switching Hybrid Method Based on User Similarity and Global Statistics for Collaborative Filtering

Patricia Ortal,Masato Edahiro

doi:10.1109/access.2020.3040432

Patricia Ortal, Masato Edahiro

Open Access

https://doi.org/10.1109/access.2020.3040432

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Abstract

Collaborative filtering (CF) is a technique used in recommender systems to provide meaningful suggestions based on known feedback obtained from like-minded users. The measure of similarity plays a critical role in the performance of neighborhood-based CF methods. However, conventional similarity measures suffer from limitations because they only consider the direction of the rating vectors. We propose a novel similarity measure that considers the semantic nuances of the ratings; in particular, it weights the contributions of ratings in proportion to the users’ degree of indifference towards the items. Additionally, to address the sparsity problem that affects the performance of CF techniques, we propose a switching hybrid method that predicts user ratings based on either our custom similarity measure or through user and item biases. We evaluated the proposed method on six different datasets and compared it with other CF methods. The results show that the proposed recommender consistently outperforms those using conventional similarity measures when the sparsity of the dataset is high.

Highlights

Decision making involves the evaluation and selection of an option between alternatives based on specific criteria
The switching hybrid consists of a Collaborative filtering (CF) method that uses our proposed similarity measure, and a CF technique that computes the prediction based on global statistics
We report the accuracy in terms of the root-mean-square error (RMSE) and mean absolute error (MAE), which are two commonly used metrics in the literature

Summary

Introduction

Decision making involves the evaluation and selection of an option between alternatives based on specific criteria. Classical decision theory is associated with the identification of optimal decisions, considering an ideal decision maker who is internally consistent and completely rational. Such a logical system fails to explain real-world scenarios [1]. In highly difficult situations, they may be unable to make a decision; this is known as analysis paralysis. Recent research argues that situations in which several options are presented can negatively affect consumer expectations. This leads to choice deferral and lower satisfaction with the selected option [3]

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Conclusion