Unified entity search in social media community

Abstract

The for is the most common behavior on the Web, especially in social media communities where (such as images, videos, people, locations, and tags) are highly heterogeneous and correlated. While previous research usually deals with these social media separately, we are investigating in this paper a unified, multi-level, and correlative graph to represent the unstructured social media data, through which various applications (e.g., friend suggestion, personalized image search, image tagging, etc.) can be realized more effectively in one single framework. We regard the social media objects equally as entities and all of these applications as entity search problem which searches for with different types. We first construct a multi-level graph which organizes the heterogeneous into multiple levels, with one type of as vertices in each level. The edges between graphs pairwisely connect the weighted by intra-relations in the same level and inter-links across two different levels distilled from the social behaviors (e.g., tagging, commenting, and joining communities). To infer the strength of intra-relations, we propose a circular propagation scheme, which reinforces the mutual exchange of information across different types in a cyclic manner. Based on the constructed unified graph, we explicitly formulate as a global optimization problem in a unified Bayesian framework, in which various applications are efficiently realized. Empirically, we validate the effectiveness of our unified graph for various social media applications on million-scale real-world dataset.