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...

Recommender systems are an effective solution to the information overload problem, specially in the online world where we are constantly faced with inordinately many choices. These systems try to find the items such as books or movies that match best with users’ preferences. Based on the different approaches to finding the items of interests to users, we can classify the recommender systems int...

The evaluation of recommender systems is key to the successful application of recommender systems in practice. However, recommender-systems evaluation has received too little attention in the recommender-system community, in particular in the community of research-paper recommender systems. In this paper, we examine and discuss the appropriateness of different evaluation methods, i.e. offline e...