Social network analysis has attracted increasing attention in recent years. In many social networks, besides friendship links amongst users, the phenomenon of users associating themselves with groups or communities is common. Thus, two networks exist simultaneously: the friendship network among users, and the affiliation network between users and groups. In this paper, we tackle the affiliation recommendation problem, where the task is to predict or suggest new affiliations between users and communities, given the current state of the friendship and affiliation networks. More generally, affiliations need not be community affiliations—they can be a user’s taste, so affiliation recommendation algorithms have applications beyond community recommendation. In this paper, we show that information from the friendship network can indeed be fruitfully exploited in making affiliation recommendations. Using a simple way of combining these networks, we suggest two models of user-community affinity for the purpose of making affiliation recommendations: one based on graph proximity, and another using latent factors to model users and communities. We explore the affiliation recommendation algorithms suggested by these models and evaluate these algorithms on two real world networks—Orkut and Youtube. In doing so, we motivate and propose a way of evaluating recommenders, by measuring how good the top

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Social network analysis has attracted increasing attention in recent years. In many social networks, besides friendship links amongst users, the phenomenon of users associating themselves with groups or communities is common. Thus, two networks exist simultaneously: the friendship network among users, and the affiliation network between users and groups. In this paper, we tackle the affiliation recommendation problem, where the task is to predict or suggest new affiliations between users and communities, given the current state of the friendship and affiliation networks. More generally, affiliations need not be community affiliations- they can be a user’s taste, so affiliation recommendation algorithms have applications beyond community recommendation. In this paper, we show that information from the friendship network can indeed be fruitfully exploited in making affiliation recommendations. Using a simple way of combining these networks, we suggest two models of user-community affinity for the purpose of making affiliation recommendations: one based on graph proximity, and another using latent factors to model users and communities. We explore the two classes of affiliation recommendation algorithms suggested by these models. We evaluate these algorithms on two real world networks- Orkut and Youtube. In doing so, we motivate and propose a way of evaluating recommenders, by measuring how good the top 50 recommendations are for the average user, and demonstrate the importance of choosing the right evaluation strategy. The algorithms suggested by the graph proximity model turn out to be the most effective and efficient. This use of link prediction techniques for the purpose of affiliation recommendation is, to our knowledge, novel.

Abstract—Reputation-based trust systems provide important capability in open and service-oriented computing environments. Most existing trust models fail to assess the variance of a reputation prediction. Moreover, the summation method, widely used for reputation feedback aggregation, is vulnerable to malicious feedbacks. This paper presents a general trust model, called RLM, for a more comprehensive and robust reputation evaluation. Concretely, we define a comprehensive reputation evaluation method based on two attributes: reputation value and reputation prediction variance. The reputation predication variance serves as a quality measure of the reputation value computed based on aggregation of feedbacks. For feedback aggregation, we propose the novel Kalman aggregation method, which can inherently support robust trust evaluation. To defend against malicious and coordinated feedbacks, we design the Expectation Maximization algorithm to autonomously mitigate the influence of a malicious feedback, and further apply the hypothesis test method to resist malicious feedbacks precisely. Through theoretical analysis, we demonstrate the robustness of the RLM design against adulating and defaming attacks, two popular types of feedback attacks. Our experiments show that the RLM model can effectively capture the reputation’s evolution and outperform the popular summation based trust models in terms of both accuracy and attack resilience. Concretely, under the attack of collusive malicious feedbacks, RLM offers higher robustness for the reputation prediction and a lower false positive rate for the malicious feedback detection. Index Terms—trust model, accuracy assessment, malicious feedback, robustness. 1

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Web mining has matured as a field of basic and applied research in computer science in general and e-commerce in particular. In the last decade, WWW has emerged as an all encompassing technology that has revolutionized the way people live. Web browsing has become an integral part of their lifestyle. This need has led to rise of range of technologies that overcome the information overload problem on the web, which is termed as personalization of the web. In this paper, we present a survey that gives a precise and comprehensive understanding of work done in the last five years in the field of personalization. The paper reviews how each of the approaches cater to user needs and give examples of projects associated with some of these techniques. The paper also mentions few issues for further research in this domain, based on the survey. In the end, paper concludes citing a promising future of this area of research.

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