A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship

a r t i c l e i n f o Online business transactions and the success of e-commerce depend greatly on the effective design of a product recommender mechanism. This study proposes a social recommender system that can generate person-alized product recommendations based on preference similarity, recommendation trust, and social relations. Compared with traditional collaborative filtering approaches, the advantage of the proposed mechanism is its comprehensive consideration of recommendation sources. Accordingly, our experimental results show that the proposed model outperforms other benchmark methodologies in terms of recommendation accuracy. The proposed framework can also be effectively applied to e-commerce retailers to promote their products and services. 1. Introduction " Social is not just about sharing connections, it's about providing different ways for people to interact…. Social commerce excites me — we already know how powerful recommendations from friends can be and the group shopping experience can easily be replicated through social commerce. " With booming social networking technologies and platforms, most e-commerce companies are creating social network profiles of their own. J.P. Morgan anticipates that global e-commerce revenue will reach $963 billion by 2013 [28]. The report forecasts that e-commerce revenue will grow to $680 billion worldwide, up 18.9% from 2010 revenue, and online retail commerce in the U.S. alone will grow 13.2% to $187 billion. For many people, shopping is a social experience, and they often want to get their friends' opinions before buying. Social commerce is helping people buy where they connect. It integrates social media into e-retail sites and adds e-commerce functionality to social networks. For online storeowners, social commerce is becoming a way of thinking about transacting business online. Some e-commerce sites use your friends' preferences to help you make better purchasing decisions. Amazon, for example, helps you find records and books by the artists and authors your friends have listed in their Facebook profiles. Recommender systems assist users in making choices from various alternatives; the goal of these systems is to estimate user preferences and provide predictions of appropriate information. Social recommender systems aim to relieve information and interaction overload by applying various techniques that ultimately present the most relevant and attractive information to users. These personalized recommendations based on social interactions or preferences are viewed as a huge opportunity for vendors. Indeed, a survey of online retailers in 2010 found that over half planned on implementing recommendation features on their sites [20]. To date, …