Abstract
This paper describes an approach for improving the accuracy of memory-based collaborative filtering, based on the technique for order of preference by similarity to ideal solution (TOPSIS) method. Recommender systems are used to filter the huge amount of data available online based on user-defined preferences. Collaborative filtering (CF) is a commonly used recommendation approach that generates recommendations based on correlations among user preferences. Although several enhancements have increased the accuracy of memory-based CF through the development of improved similarity measures for finding successful neighbors, there has been less investigation into prediction score methods, in which rating/preference scores are assigned to items that have not yet been selected by a user. A TOPSIS solution for evaluating multiple alternatives based on more than one criterion is proposed as an alternative to prediction score methods for evaluating and ranking items based on the results from similar users. The recommendation accuracy of the proposed TOPSIS technique is evaluated by applying it to various common CF baseline methods, which are then used to analyze the MovieLens 100K and 1M benchmark datasets. The results show that CF based on the TOPSIS method is more accurate than baseline CF methods across a number of common evaluation metrics.
Highlights
Traditional information outlets—including friends, newspapers, advertisements, and mass media—have been increasingly supplanted by the Internet as a source for advice and guidance in decision making
The results clearly show that the use of TOPSIS produces a significant improvement in terms of mean average precision (MAP), with the TOPSIS adaptation of the new heuristic similarity measure (NHSM) Collaborative filtering (CF) approach producing the best results across all datasets
This paper has presented a new memory-based CF in which the TOPSIS method is applied to improve the accuracy of recommendations
Summary
Traditional information outlets—including friends, newspapers, advertisements, and mass media—have been increasingly supplanted by the Internet as a source for advice and guidance in decision making. The system predicts a user score for each item in the candidate set and promotes the highest-rated items as recommendations This process of evaluating and ranking candidate items is quite significant to the performance accuracy of the CF algorithm. We propose a method for enhancing the accuracy of memory-based CF recommendations by replacing the conventional prediction algorithm with TOPSIS, which is one of the most frequently used techniques for the evaluation and ranking of multiple alternatives. The proposed method applies TOPSIS in the evaluation and sorting of items rated by nearestneighbor users to produce a set of Top-M ranked recommendations. The TOPSIS method can be described as a measurement technique based on the use of defined criteria to rank sets of alternatives, and is widely used as a tool in decision support problems. The conclusions to this study are provided with suggestions for future work
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