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Showing posts from February, 2017

Predicting Privacy Behavior on Online Social Networks

Cailing Dong, Hongxia Jin , and Bart P. Knijnenburg (AAAI-15) http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/viewFile/10554/10492 People often share personal details about themselves and even share their current activity and/or real-time location. All this public sharing of personal and sometimes private information may increase security risks, or lead to threats to one’s personal reputation. Although, users are aware of those issues, a trade-off between the benefit of social interaction and potential risk of information sharing decisions is difficult for users to make. In this paper, Dong et al. present an investigation about the psychological and contextual factors that affect users’ privacy decision-making practices, and then use the most important features w.r.t these factors to build a model that is able to predict users’ disclosure behavior on Online Social Networks (OSN). The prediction result can be used to give users personalized advice on making privacy decision. Dong et…

Tag-aware Recommender Systems by Fusion of Collaborative Filtering Algorithms

Karen H. L. Tso-Sutter, Leandro Balby Marinho and Lars Schmidt-Thieme (SAC-08)
http://dl.acm.org/citation.cfm?id=1364171
The paper “Tag-aware Recommender Systems by Fusion of Collaborative Filtering Algorithms” introduces an approach that allows to integrate tags in recommender systems in order to improve recommendation quality. Users’ rating information to items and content information of items are widely exploited in most of traditional recommender systems. Unlike attributes which are “global” descriptions of items, tags are “local” descriptions of items given by the users. Thus, tags could be interesting and useful information to enhance recommender system algorithms.
The main goal of recommender systems is to predict items or ratings of items that users are interested in so that they can recommend items to the users. Metadata such as content information of items has typically been used as additional knowledge to improve the quality of recommendations. Attribute aware RS algorithms…

Collaborative Filtering Meets Mobile Recommendation: A User-Centered Approach

Vincent W. Zheng, Bin Cao, Yu Zheng, Xing Xie and Qiang Yang (AAAI-10) http://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/viewFile/1615/1964 The paper “Collaborative Filtering Meets Mobile Recommendation: A User-Centered Approach” introduces an approach, user-centered collaborative location and activity filtering (UCLAF), to extract data from many users and apply collaborative filtering to find like-minded users and like-patterned activities at different locations. These findings from social information can help to do mobile recommendation on location and activities. Different from previous work, Vincent et al. model the user-location activity relations with a tensor representation; and suggest that a regularized tensor and matrix decomposition are better to address the sparse data problem. The result of this paper shows that UCLAF has a better performance compared to some state-of-the-art solutions. With the rapidly increase of internet data about location, location tracking services s…

SoRec: Social Recommendation Using Probabilistic Matrix Factorization

Hao Ma, Haixuan Yang, Michael R. Lyu, Irwin King (CIKM-08) http://dl.acm.org/citation.cfm?id=1458205 In this paper, Hao Ma et al. present an approach that help to deal with very large datasets and users who have made few ratings in recommender systems. Traditional methods (e.g., Pearson Correlation Coefficient and Cosine) assume that two users have rated at least some items in common so that they can compute the similarity of these users. Thus, memory-based and model-based collaborative filtering algorithms fail to find similarities of users who have never rated any items. In daily life, people usually ask their family or friends for recommendations of new movies, songs, books, restaurants or places to visit. Therefore, the authors propose a factor analysis approach based on probabilistic matrix factorization to solve the data sparsity and poor prediction accuracy problems by employing both users’ social network information and rating records. Different with traditional perspectives tha…