Multi-Collaborative Filtering Trust Network Model for Web 2.0 Recommender

In customer relationship management (CRM), online recommender assumes an important role of suggesting the right product or information to the right customer automatically. Hence customers are empowered with the choices that are predicted to be preferred by the system. The underlying technique is often a collaborative filtering (CF) algorithm that harvests both information from similar products and peer users for inferring a suggested item out of many for a user. CF and its variants have been studied extensively in the literature on online recommender; however, most of the works were based on Web 1.0 where all the information necessary for the computation is by default assumed to be always available, as if it were readily stored in a database. In the distributed environment of Web 2.0 such as social networks, the required information by CF may either be incomplete or scattered over different sources. This poses certain computational challenges for Web 2.0 recommender. The contribution of this paper is a novel model of CF that attempts to meet these challenges. This model uses a trust-network as well as emergence of information from multiple sources for utilizing CF for a recommender in a social network. This integrated model is called Multi-Collaborative Filtering Trust Network by various sources, M-CFTN in short.