We outline the history of recommender systems from their roots in information retrieval and filtering to their role in today’s Internet economy. Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. Research in recommender systems lies at the intersection of several areas of computer science...

Recommender systems are the systems that try to make recommendations to each user based on performance, personal tastes, user behaviors, and the context that match their personal preferences and help them in the decision-making process. One of the most important subjects regarding these systems is to increase the system accuracy which means how much the recommendations are close to the user int...

The comprehensive evaluation of the performance a recommender system is complex endeavor: many facets need to be considered in configuring an adequate and effective setting. Such include, for instance, defining specific goals evaluation, choosing method, underlying data, suitable metrics. In this article, we consolidate systematically organize dispersed knowledge on systems evaluation. We intro...

Unlike traditional user-item recommendation tasks (e.g., movie or consumer-product recommendation), reciprocal recommender systems (RRSs) online dating services and job-recruitment sites) must consider the interests of both two users. Pair matching prediction can improve efficiency with which RRSs match potential partners. Graph Neural Networks (GNNs) are powerful models for learning representa...