In this paper, we mine and learn to predict how similar a pair of users' interests towards videos are, based on demographic (age, gender and location) and social (friendship, interaction and group membership) information of these users. We use the video access patterns of active users as ground truth (a form of benchmark). We adopt tag-based user profiling to establish this ground truth, and justify why it is used instead of video-based methods, or many latent topic models such as LDA and Collaborative Filtering approaches. We then show the effectiveness of the different demographic and social features, and their combinations and derivatives, in predicting user interest similarity, based on different machine-learning methods for combining multiple features. We propose a hybrid tree-encoded linear model for combining the features, and show that it out-performs other linear and treebased models. Our methods can be used to predict user interest similarity when the ground-truth is not available, e.g. for new users, or inactive users whose interests may have changed from old access data, and is useful for video recommendation. Our study is based on a rich dataset from Tencent, a popular service provider of social networks, video services, and various other services in China.
With the popularity of OSNs, a better understanding of how much two individuals are alike in their interests, namely, interest similarity, will benefit various applications in OSNs. For example, information about interest similarity can be leveraged to improve friend recommendation based on the observation that the like-minded users are more likely to become friends @cite_1 . Moreover, targeted online advertising can also benefit from this because we could largely expand the pool of potential clients by finding users who are similar to the existing clients @cite_11 . In this paper, we apply the inferred interest similarity to video recommendation where the collaborative recommender systems identify users who are similar to a target user in order to recommend items to her.
@cite_1 @cite_11
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Online social networks (OSNs) like Facebook, and Myspace recommend new friends to registered users based on local features of the graph (i.e. based on the number of common friends that two users share). However, OSNs do not exploit the whole structure of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-size social networks. In this paper, we define a basic node similarity measure that captures effectively local graph features. We also exploit global graph features introducing transitive node similarity. Moreover, we derive variants of our method that apply in signed networks. We perform extensive experimental comparison of the proposed method against existing recommendation algorithms using synthetic and real data sets (Facebook, Hi5 and Epinions). Our experimental results show that our FriendTNS algorithm outperforms other approaches in terms of accuracy and it is also time efficient. We show that a significant accuracy improvement can be gained by using information about both positive and negative edges. Online social networks (OSNs) recommend new friends to registered users based on local-based features of the graph (i.e. based on the number of common friends that two users share). However, OSNs do not exploit all different length paths of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global-based approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-sized social networks. In this paper we provide friend recommendations, also known as the link prediction problem, by traversing all paths of a limited length, based on the ''algorithmic small world hypothesis''. As a result, we are able to provide more accurate and faster friend recommendations. We also derive variants of our method that apply to different types of networks (directed undirected and signed unsigned). We perform an extensive experimental comparison of the proposed method against existing link prediction algorithms, using synthetic and three real data sets (Epinions, Facebook and Hi5). We also show that a significant accuracy improvement can be gained by using information about both positive and negative edges. Finally, we discuss extensively various experimental considerations, such as a possible MapReduce implementation of FriendLink algorithm to achieve scalability. In recent years, Online Social Networks (OSNs) have essentially become an integral part of our daily lives. There are hundreds of OSNs, each with its own focus and offers for particular services and functionalities. To take advantage of the full range of services and functionalities that OSNs offer, users often create several accounts on various OSNs using the same or different personal information. Retrieving all available data about an individual from several OSNs and merging it into one profile can be useful for many purposes. In this paper, we present a method for solving the Entity Resolution (ER), problem for matching user profiles across multiple OSNs. Our algorithm is able to match two user profiles from two different OSNs based on machine learning techniques, which uses features extracted from each one of the user profiles. Using supervised learning techniques and extracted features, we constructed different classifiers, which were then trained and used to rank the probability that two user profiles from two different OSNs belong to the same individual. These classifiers utilized 27 features of mainly three types: name based features (i.e., the Soundex value of two names), general user info based features (i.e., the cosine similarity between two user profiles), and social network topological based features (i.e., the number of mutual friends between two users' friends list). This experimental study uses real-life data collected from two popular OSNs, Facebook and Xing. The proposed algorithm was evaluated and its classification performance measured by AUC was 0.982 in identifying user profiles across two OSNs. Online Social Networks (OSNs) encourage users to create an online presence that reflects their offline identity. OSNs create the illusion that these online accounts correspond to the correct offline person, but in reality the OSN lacks the resources to detect impersonation. We propose that OSN users identify each other based on interaction and experience. We believe that impersonation can be thwarted by users who possess exclusive shared knowledge, secret information shared only between a pair of OSN friends. We describe existing protocols that use shared secrets to exchange public keys without revealing those secrets to attackers. We present results from a user study on Facebook to show that users do share exclusive knowledge with their Facebook friends and attackers are rarely able to guess that knowledge. Finally, we show that friend identification can be extended using a web of trust built on the OSN friend graph.