Exploiting ratings and trust to resolve the data sparsity and cold start of recommender systems
Guibing Guo · 2014
Collaborative filtering (CF) is a widely used technique for recommender systems.The essential principle is that users with similar preference in the past are likely to give similar ratings on the items of interest in the future.However, collaborative filtering inherently suffers from two severe issues: data sparsity and cold start.The former issue refers to the difficulty in finding sufficient and reliable similar users, given that users generally rate only a small portion of items, while the latter issue refers to the difficulty presented by the cold-start users who rate zero or only a few items.Both issues are due to a lack of user ratings, and severely prevent recommender systems from generating accurate and personalized recommendations.To help resolve these issues, we have worked on two lines of research in this thesis by exploiting the value of both ratings and trust.Firstly, we propose two approaches to leverage user ratings for recommender systems.The first approach is to design a Bayesian similarity measure based on Bayesian inference, taking into consideration both the direction and length of rating vectors.We posit that not all the rating pairs should be equally counted in order to accurately model user correlation.Three different evidence factors are designed to compute the importance weights of rating pairs.Further, our principled method reduces the correlation due to chance and potential system bias.Experimental results on six real-world data sets show that our approach achieves superior accuracy in comparison with other counterparts.This method aims to make better use of existing user ratings.Secondly, we propose a new information source for recommender systems, called prior ratings.Prior ratings are based on users' experiences of virtual products represented in a mediated environment, and they can be submitted prior to purchase.A conceptual model of prior ratings is proposed, integrating the environmental factor presence whose effects on product evaluation have not been studied previously.A user study conducted in website and virtual store modalities demonstrates the validity of the conceptual model, in that users are more willing and confident to provide prior ratings in virtual environments.A method is proposed to i I would like to express my deepest appreciation and gratitude to my advisors, Dr.