A comprehensive approach for the evaluation of recommender systems using implicit feedback

摘要

Evaluation strategies are essentials in assessing the degree of satisfaction that recommender systems can provide to users. The evaluation schemes rely heavily on user feedback, however these feedbacks may be casual, biased or spam which leads to an inappropriate evaluation. In this paper, a comprehensive approach for the evaluation of recommendation system is proposed. The implicit user feedbacks are taken for the different products on the basis of the reviews provided to them. A novel sincerity check mechanism is suggested to render the biasedness and casual among the users. Further, mathematical model is presented to classify the products preference criteria. The list of the preferred products yield different ranking. Rank aggregation algorithm is used to obtain a final ranking, which is compared with the base ranking to be evaluated. Hence, with the help of suggested methodology, an evaluation strategy is suggested that avoids the risk of fake and biased feedbacks. The comparison of the proposed approach with existing schemes shows the superiority of the aforementioned approach from various parameters. It is envisaged that the proposed evaluation scheme lays a platform for users to assess the recommender systems for their ease and reliable online shopping.