Mining User Behavior in Social Environments

The growth of the Web 2.0 has brought to a widespread use of social media systems and to an increasing number of active users. This phenomenon implies that each user interacts with too many users and is overwhelmed by a huge amount of content, leading to the well know “social interaction overload” problem. In order to address this problem several research communities study Social Recommender Systems, which are information filtering systems that operate in the social media domain and aim at suggesting to the users items that are supposed to be interesting for them. Social Recommender Systems usually filter content by exploiting the social graph or by mining the user content. Since the social domain is characterized by a continuous and quick growth of the the amount of content and users, both these approaches face some problems to produce accurate and up-to-date recommendations. This PhD thesis proposes some social recommendation approaches based on the mining of the user behavior, i.e., on the exploitation of the activity of the users in social environments, in order to produce accurate and up-to-date recommendations

Of course we share! Testing Assumptions about Social Tagging Systems

Social tagging systems have established themselves as an important part in today's web and have attracted the interest from our research community in a variety of investigations. The overall vision of our community is that simply through interactions with the system, i.e., through tagging and sharing of resources, users would contribute to building useful semantic structures as well as resource indexes using uncontrolled vocabulary not only due to the easy-to-use mechanics. Henceforth, a variety of assumptions about social tagging systems have emerged, yet testing them has been difficult due to the absence of suitable data. In this work we thoroughly investigate three available assumptions - e.g., is a tagging system really social? - by examining live log data gathered from the real-world public social tagging system BibSonomy. Our empirical results indicate that while some of these assumptions hold to a certain extent, other assumptions need to be reflected and viewed in a very critical light. Our observations have implications for the design of future search and other algorithms to better reflect the actual user behavior

Social search in collaborative tagging networks : the role of ties

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How to measure information similarity in online social networks: A case study of Citeulike

In our current knowledge-driven society, many information systems encourage users to utilize their online social connections’ information collections actively as useful sources. The abundant information-sharing activities among online social connections could be valuable in enhancing and developing a sophisticated user information model. In order to leverage the shared information as a user information model, our preliminary job is to determine how to measure effectively the resulting patterns. However, this task is not easy, due to multiple aspects of information and the diversity of information preferences among social connections. Which similarity measure is the most representable for the common interests of multifaceted information among online social connections? This is the main question we will explore in this paper. In order to answer this question, we considered users’ self-defined online social connections, specifically in Citeulike, which were built around an object-centered sociality as the gold standard of shared interests among online social connections. Then, we computed the effectiveness of various similarity measures in their capabilities to estimate shared interests. The results demonstrate that, instead of focusing on monotonous bookmark-based similarities, it is significantly better to zero in on more cognitively expressible metadata-based similarities in accounting for shared interests

Recommendations based on social links

The goal of this chapter is to give an overview of recent works on the development of social link-based recommender systems and to offer insights on related issues, as well as future directions for research. Among several kinds of social recommendations, this chapter focuses on recommendations, which are based on users’ self-defined (i.e., explicit) social links and suggest items, rather than people of interest. The chapter starts by reviewing the needs for social link-based recommendations and studies that explain the viability of social networks as useful information sources. Following that, the core part of the chapter dissects and examines modern research on social link-based recommendations along several dimensions. It concludes with a discussion of several important issues and future directions for social link-based recommendation research

IMPROVING COLLABORATIVE FILTERING RECOMMENDER BY USING MULTI-CRITERIA RATING AND IMPLICIT SOCIAL NETWORKS TO RECOMMEND RESEARCH PAPERS

Research paper recommender systems (RSs) aim to alleviate the information overload of researchers by suggesting relevant and useful papers. The collaborative filtering in the area of recommending research papers can benefit by using richer user feedback data through multi-criteria rating, and by integrating richer social network data into the recommender algorithm. Existing approaches using collaborative filtering or hybrid approaches typically allow only one rating criterion (overall liking) for users to evaluate papers. We conducted a qualitative study using focus group to explore the most important criteria for rating research papers that can be used to control the paper recommendation by enabling users to set the weight for each criterion. We investigated also the effect of using different rating criteria on the user interface design and how the user can control the weight of the criteria. We followed that by a quantitative study using a questionnaire to validate our findings from the focus group and to find if the chosen criteria are domain independent. Combining social network information with collaborative filtering recommendation algorithms has successfully reduced some of the drawbacks of collaborative filtering and increased the accuracy of recommendations. All existing recommendation approaches that combine social network information with collaborative filtering in this domain have used explicit social relations that are initiated by users (e.g. “friendship”, “following”). The results have shown that the recommendations produced using explicit social relations cannot compete with traditional collaborative filtering and suffer from the low user coverage. We argue that the available data in social bookmarking Web sites can be exploited to connect similar users using implicit social connections based on their bookmarking behavior. We explore the implicit social relations between users in social bookmarking Web sites (such as CiteULike and Mendeley), and propose three different implicit social networks to recommend relevant papers to users: readership, co-readership and tag-based implicit social networks. First, for each network, we tested the interest similarities of users who are connected using the proposed implicit social networks and compare them with the interest similarities using two explicit social networks: co-authorship and friendship. We found that the readership implicit social network connects users with more similarities than users who are connected using co-authorship and friendship explicit social networks. Then, we compare the recommendation using three different recommendation approaches and implicit social network alone with the recommendation using implicit and explicit social network. We found that fusing recommendation from implicit and explicit social networks can increase the prediction accuracy, and user coverage. The trade-off between the prediction accuracy and diversity was also studied with different social distances between users. The results showed that the diversity of the recommended list increases with the increase of social distance. To summarize, the main contributions of this dissertation to the area of research paper recommendation are two-fold. It is the first to explore the use of multi-criteria rating for research papers. Secondly, it proposes and evaluates a novel approach to improve collaborative filtering in both prediction accuracy (performance) and user coverage and diversity (nonperformance measures) in social bookmarking systems for sharing research papers, by defining and exploiting several implicit social networks from usage data that is widely available

Personalized Recommendations Based On Users’ Information-Centered Social Networks

The overwhelming amount of information available today makes it difficult for users to find useful information and as the solution to this information glut problem, recommendation technologies emerged. Among the several streams of related research, one important evolution in technology is to generate recommendations based on users’ own social networks. The idea to take advantage of users’ social networks as a foundation for their personalized recommendations evolved from an Internet trend that is too important to neglect – the explosive growth of online social networks. In spite of the widely available and diversified assortment of online social networks, most recent social network-based recommendations have concentrated on limited kinds of online sociality (i.e., trust-based networks and online friendships). Thus, this study tried to prove the expandability of social network-based recommendations to more diverse and less focused social networks. The online social networks considered in this dissertation include: 1) a watching network, 2) a group membership, and 3) an academic collaboration network. Specifically, this dissertation aims to check the value of users’ various online social connections as information sources and to explore how to include them as a foundation for personalized recommendations. In our results, users in online social networks shared similar interests with their social partners. An in-depth analysis about the shared interests indicated that online social networks have significant value as a useful information source. Through the recommendations generated by the preferences of social connection, the feasibility of users’ social connections as a useful information source was also investigated comprehensively. The social network-based recommendations produced as good as, or sometimes better, suggestions than traditional collaborative filtering recommendations. Social network-based recommendations were also a good solution for the cold-start user problem. Therefore, in order for cold-start users to receive reasonably good recommendations, it is more effective to be socially associated with other users, rather than collecting a few more items. To conclude, this study demonstrates the viability of multiple social networks as a means for gathering useful information and addresses how different social networks of a novelty value can improve upon conventional personalization technology

Lightweight Tag-Aware Personalized Recommendation on the Social Web Using Ontological Similarity

With the rapid growth of social tagging systems, many research efforts are being put intopersonalized search and recommendation using social tags (i.e., folksonomies). As users can freely choosetheir own vocabulary, social tags can be very ambiguous (for instance, due to the use of homonymsor synonyms). Machine learning techniques (such as clustering and deep neural networks) are usuallyapplied to overcome this tag ambiguity problem. However, the machine-learning-based solutions alwaysneed very powerful computing facilities to train recommendation models from a large amount of data,so they are inappropriate to be used in lightweight recommender systems. In this work, we propose anontological similarity to tackle the tag ambiguity problem without the need of model training by usingcontextual information. The novelty of this ontological similarity is that it first leverages external domainontologies to disambiguate tag information, and then semantically quantifies the relevance between userand item profiles according to the semantic similarity of the matching concepts of tags in the respectiveprofiles. Our experiments show that the proposed ontological similarity is semantically more accurate thanthe state-of-the-art similarity metrics, and can thus be applied to improve the performance of content-based tag-aware personalized recommendation on the Social Web. Consequently, as a model-training-freesolution, ontological similarity is a good disambiguation choice for lightweight recommender systems anda complement to machine-learning-based recommendation solutions.Fil: Xu, Zhenghua. University of Oxford; Reino UnidoFil: Tifrea-Marciuska, Oana. Bloomberg; Reino UnidoFil: Lukasiewicz, Thomas. University of Oxford; Reino UnidoFil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Chen, Cheng. China Academy of Electronics and Information Technology; Chin