STRESS: A Social Trust-Aware System for Recommending Web Services

1. Introduction

Recommender systems constitute a significant response to the information overload problem that users have to deal with constantly. These systems rely primarily on users' ratings of items' descriptions. The quality of recommendations is dependent on the nature of details (in terms of quality and quantity) available to users. Collaborative Filtering (CF), which tends to identify similar users to a user, is the dominant technique for recommender systems. However, it is difficult to compute similarity between users when ratings are sparse (Zarghami et al., 2009). Moreover, similar does not always mean trustworthiness, which raises concerns over the reliability of CF recommendations.

Under the new era of internet and cyber societies, online social networking has fast become as a popular mode of virtual communication that has fundamentally transformed the ways people interact with each other (So and Lam, 2014). In the context of Web-based Social Networks (SNs) where users are connected together, we need to consider additional factors that can help measure and interpret the similarity between user profiles. Most of existing recommender systems incorporates social trust relationships among users into WSs’ recommendation (Tang et al., 2013). SNs capture relationships between users like friendship and collegiality like in (D'Andrea et al., 2012). These SNs makes ideal for improving recommender system’s capabilities. Furthermore, research in the area of recommender systems in SNs has shown that users would prefer to receive recommendations from whom they trust more. Embedding trust into SNs should lead into trust-aware recommender systems (Ray, 2012). Hence, recommendations are made based on the ratings given by users; these latters can be either directly or indirectly trusted by the current user.

The proliferation of WSs reflects the second evolution wave of the Internet making the Web a platform for on-line applications collaboration (Dragoni, 2010). However, several constraints continue to limit WSs adoption by the IT community, for instance WSs only know about themselves, not about their users’ social connections or peers, they cannot also delegate their invocations to others peers, limit user's intervention considerably, and operate as black boxes (Maamar et al., 2011c). Social computing seems offering innovative solutions to some of these limitations, which improves the quality of recommendations. Social computing is at the crossroad of computer sciences and social sciences disciplines by looking into ways of improving application design and development using elements that people encounter daily. Blending social computing with service-oriented computing leads to Social Web Services (SWSs) that exhibit certain Social Qualities of Service (SQoS) towards peers, thus allows them “knowing” with whom they have worked in the past and with whom they would potentially like to work in the future. Thereby, SWSs are expected to take the initiative in advising users how to develop and reuse value-added services (Maamar et al., 2011c).

Building upon these considerations, we propose a recommender system that combines trust-based CF techniques and social elements so that personalized WSs recommendations are provided. To the best of our knowledge, this is the first investigation that considers SQoS of WSs and trustworthiness of users when recommending WSs. Our contributions are as follows:

• 1.

  Discover and model relationships between users, between WSs, and between users and WSs as social trust networks;

• 2.

  Develop a trust-based hybrid CF technique upon the modeled networks to learn users' trust preferences on QoS and SQoS;

• 3.

  Provide WSs recommendations from trustworthy users and SWSs that will lead to better recommendations.

The paper is organized as below. Section 2 discusses some background concepts and related works. Section 3 introduces the social trust-aware recommendation system. Next implementation and experiments details are described in section 4. Finally, the paper ends with concluding remarks and future work.

2. Background Concepts And Related Works

This section presents a brief overview of the research initiatives on SWSs and recommendation systems.