An Effective Friend Recommendation Method Using Learning to Rank and Social Influence

Social network sites have become an important medium for people to receive information anytime anywhere. Users of social network sites share information by posting updates. The updates shared by friends form social update streams that provide people with up-to-date information. To receive novel information, users of social network sites are encouraged to establish social relations. However, having too many friends can lead to an information overload problem causing users to be overwhelmed by the huge number of updates shared continuously by numerous friends. The information overload problem can result in bad user experiences. It may also affect user intentions to join social network sites and thereby possibly reduce the sites’ advertising earnings which are based on the number of users. To resolve this problem, there is an urgent need of effective friend recommendation methods. A user is considered as a valuable friend if people like the updates the user posts. In this paper, we propose a model-based recommendation method which suggests valuable friends to users. Techniques of matrix factorization and learning to rank are designed to model the latent preferences of users and updates. At the same time, social influence is incorporated into the proposed method to enhance the learned preferences. Valuable friends are recommended if the preferences of the updates that they share are highly associated with the preferences of a target user. Our experiment findings that are based on a huge real-world dataset demonstrate the effectiveness of the social influence and learning to rank on a friend recommendation task. The results show that the proposed method is effective and it outperforms many well-known friend recommendation methods in terms of the coverage rate and ranking performance

In pursuit of satisfaction and the prevention of embarrassment : affective state in group recommender systems

Peer reviewedPostprin

Studying and Modeling the Connection between People's Preferences and Content Sharing

People regularly share items using online social media. However, people's decisions around sharing---who shares what to whom and why---are not well understood. We present a user study involving 87 pairs of Facebook users to understand how people make their sharing decisions. We find that even when sharing to a specific individual, people's own preference for an item (individuation) dominates over the recipient's preferences (altruism). People's open-ended responses about how they share, however, indicate that they do try to personalize shares based on the recipient. To explain these contrasting results, we propose a novel process model of sharing that takes into account people's preferences and the salience of an item. We also present encouraging results for a sharing prediction model that incorporates both the senders' and the recipients' preferences. These results suggest improvements to both algorithms that support sharing in social media and to information diffusion models.Comment: CSCW 201

Recommendation, collaboration and social search

This chapter considers the social component of interactive information retrieval: what is the role of other people in searching and browsing? For simplicity we begin by considering situations without computers. After all, you can interactively retrieve information without a computer; you just have to interact with someone or something else. Such an analysis can then help us think about the new forms of collaborative interactions that extend our conceptions of information search, made possible by the growth of networked ubiquitous computing technology. Information searching and browsing have often been conceptualized as a solitary activity, however they always have a social component. We may talk about 'the' searcher or 'the' user of a database or information resource. Our focus may be on individual uses and our research may look at individual users. Our experiments may be designed to observe the behaviors of individual subjects. Our models and theories derived from our empirical analyses may focus substantially or exclusively on an individual's evolving goals, thoughts, beliefs, emotions and actions. Nevertheless there are always social aspects of information seeking and use present, both implicitly and explicitly. We start by summarizing some of the history of information access with an emphasis on social and collaborative interactions. Then we look at the nature of recommendations, social search and interfaces to support collaboration between information seekers. Following this we consider how the design of interactive information systems is influenced by their social elements

Career Handbook

The job search, no matter what step you are on, can be a daunting and intimidating process. We want you to know that you are not alone in this journey. Since the day you arrived on campus, you have been surrounded by the support of family, friends, professors, staff, and peers. As you move into the next stage of your professional journey, we want you to know that you have the support of Career Services, the Alumni Association, and all of the employers who are part of the Hire a Rebel family to mentor, guide, and walk with you through the jobbing process. We are invested in the community of Las Vegas, the network of UNLV, and in each of you to help you to grow and transition from student to professional. The road to career success is not as easy as we might hope for. You may encounter setbacks and struggles throughout the next few years, but keep in mind that each situation you encounter and every decision you make is shaping you into a Rebel professional. By taking advantage of the resources that come along with being a UNLV Rebel, you will network with amazing and successful professionals already thriving in the field, build your own career toolkit, and navigate through the professional world with the skill set that you learned while you were a student at UNLV. Whether you are working in an office, stage, gallery, restaurant, school, or even your own home, you are equipped with the knowledge, drive, and determination to find success. You have the spirit of a Rebel within you. When roadblocks get in your way, use your network and your talents to navigate around, over, or through them. When you are met with overwhelming success, share those victories with your Rebel family. Remember that you are now and will always be connected through UNLV. We all have your back and are all excited for you on this journey. You are a Rebel today and you will be a Rebel forever. Welcome to the Hire a Rebel family!https://digitalscholarship.unlv.edu/career_handbook/1000/thumbnail.jp

HOW DO CONSUMERS USE SOCIAL SHOPPING WEBSITES? THE IMPACT OF SOCIAL ENDORSEMENTS

Social endorsements are user-generated endorsements of products or services, such as “likes” and personal collections, in an online social platform. We examine the effect of prior social endorsements on subsequent users’ tendency to endorse or examine a product in a social shopping context, where a social platform connect consumers and enable a collaborative shopping experience. This research consists of two parts. In part I, we identify two ways prior social endorsements can affect subsequent user behavior: as a crowd endorsement, which is an aggregate number of endorsements a product receives for anyone who comes across the product, and as a friend endorsement, which is an endorsement with the endorser’s identity delivered only to the endorser’s friends or followers. Using a panel data of 1656 products on a leading social shopping platform, we quantify the relationship between crowd and friend endorsements and subsequent examination (“click”) and endorsement (“like”) of the products, noting that examination is a private behavior while endorsement is a public behavior. Our results are consistent with the identity signaling theory where identity-conscious consumers converge with the aspiration group (the followers) in their public behavior (e.g. endorsement) and diverge from the avoidance groups (the crowd). We also find differences between public and private behaviors. Moreover, the symbolic nature of social shopping platform trumps the traditional dichotomy of symbolic/functional product attributes. Part II of this study seeks to clarify the underlying mechanism through lab experiments. We hypothesize that consumers’ evaluative attitude, specifically the value-expressive type, moderates the relationship between crowd and friend endorsements and a focal user’s product choice. Our initial results of the second study show support for this idea in the cases when the product choice is not obvious

Which Factors Determine User’s First and Repeat Online Music Listening Respectively? Music Itself, User Itself, or Online Feedback

In the era of Web 2.0, does online feedback mainly dominant online users’ buying behavior, or are user’s own preference and product quality still important? Previous studies paid more attention to the influence of online feedback on users’ online buying behavior, however this paper focuses on how users’ own factors, product quality related factors and online feedback factors together influence a user’s buying behavior, and also how does this effect change as time goes by. Taking online music as our research industry and using the data from Last.fm website, this research shows that users’ preference and product quality are still the two most dominate factors influencing users’ online music listening, while online feedback plays an important role on users’ first listening. It is also found that the different influences of crowds and friends