Introduction of synthetic and non-synthetic trust recommender models in collaborative filtering

Rapid expansion of the Internet makes competition between many websites and social networks more prevalent. Several users cannot choose theirfavorite options because of huge amount of information they will face. This causes information overhead problem. Recommender systems with collaborativefiltering appeared in different area to solve this problem. In recent years, expansion of e-commerce websites and social networks gives form to creditmechanism which can be used to improve performance of collaborative filtering system and to eliminate their limitations.Most models of credit are based on scoreswhich users give to items and also relation credits and popularity. In this model, if users scoring to items islittle it will cause data dispersion.In this paper, different types of trust models in recommender systems will be studied. Also these systems are classified into synthetic and non-synthetictypes. Among modern methods are hybrid personal trustandhybrid personal and grouptrust. Finally, allmodels are compared with each other and theiradvantages and disadvantages are clarified. Comparing with different methods shows that the final trustmethod has a higher recommendation quality than other collaborative filteringmethods. It also increases accuracy of prediction superbly.

Keywords:

Recommender systems , collaborative filtering , trust , hybrid personal trust , group trust