A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship
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.”
Sophy Silver, Facebook's UK & Ireland Public Relations Chief
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, a variety of recommendation techniques has been developed. To our best knowledge, collaborative filtering, content-based, and hybrid approaches are three popular approaches that have been used to generate recommendations [23]. An approach that has received less attention is using the social relations on individuals as an additional source of information. The principle of homophily from the social network field suggests that “similarity breeds connection.” In other words, users share many attributes with the people close to them. This suggests that if we have information about the connections in a person's network, we can infer some of that person's attributes. Most commercial recommender systems are strongly supported by the demographic information of users. Since some of the similarities within a network are caused by the influence and interactions of its members, it would be reasonable and feasible to develop a social recommendation based on the connections of individual users. In reality, people tend to be affected by the opinions of and suggestions by people with similar interests, shopping experts, and close friends.
However, most of current social networking platform, such as Facebook and twitter, and electronic commerce platform, such as Amazon and Yahoo! Shopping, are independently operated. The supporting recommender systems are also independently deployed on the two kinds of platforms based on social factors and purchase history respectively. As a result, the electronic commerce (retailing) platforms generally do not consider social factors such as relationships and trust etc. among the users and the power of social influence is not exploited. Contrarily, social networking platforms generally do not consider online shopping related factors such as purchase history and product rating etc.
To address these issues, in this research, we synthesize the features of social networking and electronic commerce platforms to design a social recommender mechanism that considers the factors of preference similarity, recommendation trust, and social relationship in order to increase the prediction accuracy of product recommendations in e-commerce. The factors of human interactions and relations (e.g. trusts [30], [61], reputations [40], [64], and social relationship [34], [68]) have been applied separately in different application contexts. In this research, by building several new social metric formulas, we exploit and consolidate various types of consulting information source to generate product recommendations.
The proposed mechanism allows us to identify suitable products for individual customers by utilizing the collective intelligence from social networks and to balance the consulted sources based on these personalized preferences. Our experimental results based on users' evaluations in Yahoo! Shopping show that the proposed model could enhance recommendation accuracy. The proposed model could be practically applied to new emerging social commerce platforms.
The remainder of this paper is organized as follows. In Section 2, we discuss the existing literature related to our research topics. In Section 3, we discuss the factors that contribute to the proposed social recommendation framework. Section 4 describes the experimental data source, settings, and procedures. The experimental results and evaluations are discussed in Section 5. Section 6 concludes our research contributions and presents future research directions.
Section snippets
Recommender systems
Recommender systems can help users identify the items that suit their needs or preferences in an effective way. They are usually used to solve information overload problems and to grow sales in e-commerce [55].
For providing personalized recommendations, there are two ways to receive users' preferences [21]: implicit and explicit. First, the implicit method collects users' behavior to infer their preferences. When detecting changes, these user preference data change simultaneously [2]. Choi et
The model
In product purchasing, people tend to ask for advice or suggestions from people with similar interests or professional expertise, or from close friends. However, close friends may not have the expertise or interest in certain products. Furthermore, we may not always believe the suggestions of product experts with whom we have no acquaintance. Consulted sources also differ when product types vary. Therefore, an effective product recommendation should appropriately incorporate these factors. In
Experiments
In the following section, we conduct an empirical study based on the proposed social recommender framework. According to the evaluation results from the recommendation ratings of users participating in e-commerce, we compare the performance of the proposed framework with those of other traditional collaborative product recommendation approaches.
Results and evaluations
In order to evaluate and compare the performances of different product recommendation strategies, we randomly separated the collected 7199 product rating records into a training dataset (95%, 6839 records) and an evaluation dataset (5%, 360 records).
Conclusion
When shopping online, people tend to seek the suggestions and help of similar people, shopping experts, and close friends. However, most of current social networking platform, such as Facebook and twitter, and electronic commerce platform, such as Amazon and Yahoo! Shopping, are independently operated. The recommender systems deployed by famous electronic commerce websites, such as Amazon.com and eBay, are based on personal purchase history, aggregated rating of members, and feedbacks [58].
Acknowledgment
This research was supported by the National Science Council of Taiwan (Republic of China) under grant NSC 99-2410-H-009-035-MY2.
Yung-Ming Li is a Professor at the Institute of Information Management, National Chiao Tung University in Taiwan. He received his Ph.D. in Information Systems from the University of Washington. His research interests include network science, Internet economics, and business intelligence. His research has appeared in IEEE/ACM Transactions on Networking, INFORMS Journal on Computing, European Journal of Operational Research, Decision Support Systems, International Journal of Electronic Commerce,
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Yung-Ming Li is a Professor at the Institute of Information Management, National Chiao Tung University in Taiwan. He received his Ph.D. in Information Systems from the University of Washington. His research interests include network science, Internet economics, and business intelligence. His research has appeared in IEEE/ACM Transactions on Networking, INFORMS Journal on Computing, European Journal of Operational Research, Decision Support Systems, International Journal of Electronic Commerce, Electronic Commerce Research and Applications, ICIS, WTIS, among others.
Chun-Te Wu is an information technology manager at NeoEnergy Microelectronics Inc. in Taiwan. He received his M.S. degree from the Institute of Information Management, National Chiao Tung University in Taiwan. His research interests focus on electronic commerce and mobile computing.
Cheng-Yang Lai is a Ph.D. student at the Institute of Information Management, National Chiao Tung University in Taiwan. His research interests include electronic commerce and business intelligence. His research has appeared in Electronic Commerce Research and Applications and Information Sciences.