Abstract

A recommender system uses specific algorithms and techniques in order to suggest specific services, goods or other type of recommendations that users could be interested in. User’s preferences or ratings are used as inputs and top-N recommendations are produced by the system. The evaluation of the recommendations is usually based on accuracy metrics such as the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE), while on the other hand Precision and Recall is used to measure the quality of the top-N recommendations. Recommender systems development has been mainly focused in the development of new recommendation algorithms. However, one of the major problems in modern offline recommendation system is the sparsity of the datasets and the selection of the suitable users Y that could produce the best recommendations for users X. In this paper, we propose an algorithm that uses Fuzzy sets and Fuzzy norms in order to evaluate the correlation between users in the data set so the system can select and use only the most relevant users. At the same time, we are extending our previous work about Reproduction of experiments in recommender systems by developing new explanations and variables for the proposed new algorithm. Our proposed approach has been experimentally evaluated using a real dataset and the results show that it is really efficient and it can increase both accuracy and quality of recommendations.

Highlights

  • The use of recommender systems is very common, especially in applications like eCommerce and social networks

  • The increasingly importance, use and popularity of recommender systems research both in academia and in industry has led to the development of new algorithms and their experimental evaluation

  • In this paper an extended methodology of reproducibility in recommender systems combined with source dataset optimization methods with the use or Recommender101 platform has been presented

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Summary

Introduction

The use of recommender systems is very common, especially in applications like eCommerce and social networks. The increasingly importance, use and popularity of recommender systems research both in academia and in industry has led to the development of new algorithms and their experimental evaluation. Researchers are mostly focusing in creating more effective algorithms and models by trying to minimize the MAE and RMSE while at the same time they are trying to improve precision and recall of top-N recommendations [9, 10]. While this is important to do, it should be noted that the problem of reproducing the results exists and it is considered important [11]. In a previous work of the research team [13], offline recommender system results were successfully reproduced using an explanation-based approach

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