A flexible framework for context-aware recommendations in the social commerce domain

Abstract

Identifying the products to suggest to the target customer in order to best fit his profile of interests is a challenging task in the Social Commerce domain. In this paper we investigate the application of different model-based Collaborative Filtering (CF) techniques and we propose a framework able to provide different suggestions to the customers. The framework is aimed at expanding the customer suggestions in order to guarantee a serendipitous discovery of wished products and at the same time, it is aimed at assisting merchants in discovering potential interests of their customers. We compare several approaches by means of Epinions, MovieLens and Poste Italiane dataset (with real customers). Experimental results show an increased value of coverage of the recommendations provided by our approach without affecting recommendation quality. Moreover, personalized strategy guarantees a high level recommendation for different user navigation behaviors.

Social commerce: A literature review

“…Typically, recommendations can be generated on the basis of the user's interaction history or on the history of related users. On the other hand, consumers might use recommendation systems to suggest a new product to other members of a community [13]. Another technological features is the "customer ratings / products reviews / transaction ratings".…”