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...

Recommender systems (RSs) are personalized information search and discovery applications helping users to identify choose useful items information. In this paper, we focus on the tourism application scenario its specific requirements. We discuss a novel RS approach that copes with constraints of domain produces recommendations better match true needs tourists. illustrate proposed next POI recom...

Social commerce based on relations is developing rapidly in recent years and the personalized recommender systems make the contribution. Based on the traditional collaborative filtering (CF) algorithm, this study proposes a social recommender systems that combing preference similarity, reputation-based trust and social relations between users. Using the real data from Epinions.com, we compared ...

The paper reviews and classifies recent research in recommender systems both in the field of Computer Science and Information Systems. The goal of this work is to identify existing trends, open issues and possible directions for future research. Our analysis is based on a review of 330 papers on recommender systems, which were published in high-impact conferences and journals during the past fi...