Fast Neural Ranking on Bipartite Graph Indices

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Abstract

Neural network based ranking is widely adopted due to its powerful capacity in modeling complex relationships, such as between users and items, questions and answers. Online neural network ranking – so called fast neural ranking – is considered challenging because neural network measures are usually non-convex and asymmetric. Traditional Approximate Nearest Neighbor (ANN) search which usually focuses on metric ranking measures, is not applicable to these advanced measures. In this paper, we propose to construct BipartitE Graph INdices (BEGIN) for fast neural ranking. BEGIN contains two types of nodes: base/searching objects and sampled queries. The edges connecting these types of nodes are constructed via the neural network ranking measure. The proposed algorithm is a natural extension from the traditional search on graph methods and is more suitable for fast neural ranking. Experiments demonstrate the effectiveness and efficiency of the proposed method.