Rule-Based Effective Collaborative Recommendation Using Unfavorable Preference

G. Suganeshwari,S. P. Syed Ibrahim

doi:10.1109/access.2020.3008514

Abstract

The biggest challenge in collaborative filtering recommendation system research is data sparsity ; it mainly occurs as user rates very few items from widely available options. Data Imputation techniques address the data sparsity problem by filling the missing values and then predicting the likeliness of the user. Most of the existing imputation systems assign high ratings to the items or incorporate additional information to enhance the performance of collaborative filtering recommendations. This paper proposes an association rule-based imputation method (RUBLE) to improve the top- $N$ prediction performance of the collaborative filtering recommendation. The proposed method identifies the unfavorable items of each user using the association rule mining technique and imputes them with low values. The proposed method not only addresses the sparsity problem but also provides a better quality of recommendation by eliminating the unfavorable items in top- $N$ predictions. Existing collaborative methods can quickly adapt to the proposed method as it is method agnostic. The experimental results show that the proposed method enhances the accuracy of the traditional recommender methods by two times on average and significantly outperforms existing imputation based approaches.

Highlights

With the advent of Web 2.0, there is a rapid growth in the increase of user information, items, and other services leading to the information overload problem
Recommendation System (RS) emerged as a vital tool to overcome the information overload problem, as it automatically identifies the items that are of interest to the user and generates a personalized recommendation based on historical preferences
PROPOSED WORK we describe our proposed imputation method that can effectively improve the top-N performance of the CFbased recommendation system

Summary

Introduction

With the advent of Web 2.0, there is a rapid growth in the increase of user information, items, and other services leading to the information overload problem. Evaluating all this information is infeasible and time-consuming, making it impossible to find the relevant items for a user. The collaborative filtering (CF) [1]–[5] is one of the most widely adopted recommendation methods employed in the e-commerce field due to its justifiability and ease of implementation. They are two types of CF approaches: the memory-based approach and the modelbased approach.

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Conclusion