A study of the impact of the weighted reciprocal rank fusion method on the quality of recommender systems

Abstract

Providing high-quality recommendations is an important factor in increasing the level of audience engagement, since under conditions of rapid growth in the volume of available information, a significant part of which is presented in the form of implicit feedback, recommender systems ensure the effective selection and ranking of items according to individual user preferences. CF is one of the most widespread strategies for constructing such systems and generates recommendations based on the analysis of user interactions with similar behaviour patterns, which makes it possible to identify not only obvious but also unexpected potentially relevant items. During the generation of recommendations by different CF algorithms, recommendation lists for each user are produced that differ in the composition and order of items, which is caused by differences in the principles of determining the relevance of items. Since CF algorithms produce distinct recommendation lists for each user, it is appropriate to apply the WRRF method, which ensures the aggregation of rankings generated by algorithms in order to construct a single ranked recommendation list of higher quality. The purpose of this work is to study the influence of the WRRF method on the quality of generation and ranking of recommendation lists obtained as a result of the pairwise combination of CF algorithms through rank aggregation of items. According to the results of the experimental study, it has been established that the use of the WRRF method in the vast majority of cases ensures an improvement in recommendation quality compared with the best algorithm in the corresponding pair. The experimental evaluation was carried out using six CF algorithms on three datasets transformed into the implicit feedback format. The obtained results can be used in the development and improvement of industrial recommender systems in order to increase the quality of recommendation ranking without significant complication of their software architecture.