No abstract

No abstract

To characterize the relationship between items, graph-based ranking algorithms are widely used in various applications, such as information retrieval, recommender system, and natural language processing. Many ranking approaches tackle the dilemma between relevance and diversity. Diversity is considered as a critical objective of reducing redundancy and retrieving prestige information that has high coverage. However, the traditional evaluation of diversification is found to be deficient. In this paper, we address the coverage problem from a viewpoint of influence diffusion. Firstly, we transform the coverage problem into the diffusion problem and propose a novel measure called essential influence that combines relevance and diversity into a single function. Next, we propose a reinforced random walk, InfRank, of which the heuristic function is based on the essential influence. We applied InfRank on two applications, ranking in networks and tag recommendation. Our approach outperforms existing network-based ranking methods.

When exposed to an item in a recommender system, a user may consume it (known as success exposure) or neglect it (known as failure exposure). The recently proposed methods that consider both success and failure exposure merely regard failure exposure as a constant prior, thus being capable of neither modeling various user behavior nor adapting to overdispersed data. In this paper, we propose a novel model, hierarchical negative binomial factorization, which models data dispersion via a hierarchical Bayesian structure, thus alleviating the effect of data overdispersion to help with performance gain for recommendation. Moreover, we factorize the dispersion of zero entries approximately into two low-rank matrices, thus reducing the updating time linear to the number of nonzero entries. The experiment shows that the proposed model outperforms state-of-the-art Poisson-based methods merely with a slight loss of inference speed.

No abstract