Random WalkBased Topk Tag Generation in Bipartite Networks of EntityTerm 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 topk tag generation algorithm, namely DRWR, for efficiently generating the tags from entityterm 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: offline stage and online stage. In offline stage, the relevances between terms are computed over the termterm network that is built based on the whole structure of entityterm network. In online stage, the relevances between entity and each term are computed based on the relevances between terms. For supporting fast online 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.

Listwise Learning to Rank by Exploring Unique Ratings In this paper, we propose new listwise learningtorank models that mitigate the shortcomings of existing ones. Existing listwise learningtorank models are generally derived from the classical PlackettLuce model, which has three major limitations. (1) Its permutation probabilities overlook ties, i.e., a situation when more than one document has the same rating with respect to a query. This can lead to imprecise permutation probabilities and inefficient training because of selecting documents one by one. (2) It does not favor documents having high relevance. (3) It has a loose assumption that sampling documents at different steps is independent. To overcome the first two limitations, we model ranking as selecting documents from a candidate set based on unique rating levels in decreasing order. The number of steps in training is determined by the number of unique rating levels. We propose a new loss function and associated four models for the entire sequence of weighted classification tasks by assigning high weights to the selected documents with high ratings for optimizing Normalized Discounted Cumulative Gain (NDCG). To overcome the final limitation, we further propose a novel and efficient way of refining prediction scores by combining an adapted Vanilla Recurrent Neural Network (RNN) model with pooling given selected documents at previous steps. We encode all of the documents already selected by an RNN model. In a single step, we rank all of the documents with the same ratings using the last cell of the RNN multiple times. We have implemented our models using three settings: neural networks, neural networks with gradient boosting, and regression trees with gradient boosting. We have conducted experiments on four public datasets. The experiments demonstrate that the models notably outperform stateoftheart learningtorank models.