Hybrid social media network

Abstract

Analysis and recommendation of multimedia information can be greatly improved if we know the interactions between the content, user, and concept, which can be easily observed from the social media networks. However, there are many heterogeneous entities and relations in such networks, making it difficult to fully represent and exploit the diverse array of information. In this paper, we develop a hybrid social media network, through which the heterogeneous entities and relations are seamlessly integrated and a joint inference procedure across the heterogeneous entities and relations can be developed. The network can be used to generate personalized information recommendation in response to specific targets of interests, e.g., personalized multimedia albums, target advertisement and friend/topic recommendation. In the proposed network, each node denotes an entity and the multiple edges between nodes characterize the diverse relations between the entities (e.g., friends, similar contents, related concepts, favorites, tags, etc). Given a query from a user indicating his/her information needs, a propagation over the hybrid social media network is employed to infer the utility scores of all the entities in the network while learning the edge selection function to activate only a sparse subset of relevant edges, such that the query information can be best propagated along the activated paths. Driven by the intuition that much redundancy exists among the diverse relations, we have developed a robust optimization framework based on several sparsity principles. We show significant performance gains of the proposed method over the state of the art in multimedia retrieval and recommendation using data crawled from social media sites. To the best of our knowledge, this is the first model supporting not only aggregation but also judicious selection of heterogeneous relations in the social media networks.

A robust model for paper reviewer assignment

“…In this work, we directly apply a Markov random walk process [21] on the graph. We use Q ij to denote the transition probability from node vi to node vj, which can be calculated in the following way:…”

“…Similar to [21], based on Nesterov's smoothing approximation method [22], the f 1 can be approximated by a smooth function as follows:…”

O 2 SM: Enabling Efficient Offline Access to Online Social Media and Social Networks

“…Background: Traditional ranking systems can be classified into three categories: (1) content based approaches [5,9,12], (2) collaborative filtering approaches [7,20,27], and (3) social network based approaches [19,21,25]. Content based approaches [5,9,12] rank media contents using the correlation between the attributes of media content, such as descriptions, keywords, tags, images, and video features (e.g., colors), and user preferences.…”

“…Social networks are characterized by heterogeneous entities, e.g., contents, users, and social relations. Recent studies [19,21,25] exploit relations among entities to recommend contents. For example, a user is likely to view contents generated by friends, whom the user interacts with frequently over social networks.…”

“…However, while the intuitions are appealing, a direct application of the existing techniques to our system is not easy. For example, the work in [19] is based on a social graph to evaluate social relations among entities, and works only for a fixed set of entities. It requires rebuilding the social graph and recalculation of weights when new contents arrive, which happens frequently in our system.…”