A Trust and User Personality Trait based Collaborative Filtering Recommender System

Abstract

Recommendation system is a system where a user gets suggestions for the product based on his/her previous preferences to the items. With the monumental growth of web services, developing adequate methods for the recommendation has become dominant in the research area. In terms of Collaborative Filtering(CF) user and item-based methods are the most presiding approaches used in RS. In order to get an improved recommendation, trust value and personality traits of users can play a key role in nding similarity between users.It solves cold start problem where neighbors of the new user are di cult to nd as they have not rated any item yet. In our work, the implicit(based on personality),latent(based on trust) and explicit(based on rating) features of user behaviour have been utilized to tackle the problems of Collaborative Filtering.The major bene t of the proposed method is its consideration of direct and indirect trust values and personality similarity compared to traditional collaborative ltering approaches.A com- parative review of traditional rating based recommender system,personality based,trust based and their combined approach is presented. Empirical analysis shows that using trust propagation and personality traits substantially increases the e ciency of the CF recommender system.