Random Walk-based Top-k Tag Generation in Bipartite Networks of Entity-Term Type

Tag generation aims to find relevant tags for a given entity, which has numerous applications, such as classification, information retrieval and recommender system. Practically, the data of real applications is sparse and lacks sufficient description for entities, which might lead to incomprehensive results. Random walk with restart (RWR) can find the hidden relationship between nodes by utilizing indirect connections. However, the traditional RWR computation is based on the whole structure of the given network, which maintains a matrix for storing all relevances between nodes. And the efficiency problem would be run into as network grows large. In this paper, we propose a top-k tag generation algorithm, namely DRWR, for efficiently generating the tags from entity-term network. The terms are treated as candidate tags, and the most relevant terms are treated as the tags for a given entity. The relevance computation between entity and terms is divided into two stages: off-line stage and online stage. In off-line stage, the relevances between terms are computed over the term-term network that is built based on the whole structure of entity-term network. In on-line stage, the relevances between entity and each term are computed based on the relevances between terms. For supporting fast on-line query processing, we develop a pruning algorithm, which skips the operations on relevances between terms smaller than a threshold. Extensive experiments on real datasets demonstrate the efficiency and effectiveness of the proposed approach.