Baidu USA LLC

Weijie Zhao

Ping Li

Shulong Tan

Zhixin Zhou

Zhaozhuo Xu

Hongliang Fei

Presented are systems and methods that construct BipartitE Graph INdices (BEGIN) embodiments for fast neural ranking. BEGIN embodiments comprise two types of nodes: sampled queries and base or searching objects. In one or more embodiments, edges connecting these nodes are constructed by using a neural network ranking measure. These embodiments extend traditional search-on-graph methods and lend themselves to fast neural ranking. Experimental results demonstrate the effectiveness and efficiency of such embodiments.

Fast neural ranking on bipartite graph indices

Efficient inner product search is important for many data ranking services, such as recommendation and Information Retrieval. Efficient retrieval via inner product dramatically influences the performance of such data searching and retrieval systems. To resolve deficiencies of prior approaches, embodiments of a new index graph construction approach, referred to generally as Norm Adjusted Proximity Graph (NAPG), for approximate Maximum Inner Product Search (MIPS) are presented. With adjusting factors estimated on sampled data, NAPG embodiments select more meaningful data points to connect with when constructing a graph-based index for inner product search. Extensive experiments verify that the improved graph-based index pushes the state-of-the-art of inner product search forward greatly, in the trade-off between search efficiency and effectiveness.

Norm adjusted proximity graph for fast inner product retrieval

Incremental proximity graph maintenance (IPGM) systems and methods for online ANN search support both online vertex deletion and insertion of vertices on proximity graphs. In various embodiments, updating a proximity graph comprises receiving a workload that represents a set of vertices in the proximity graph, each vertex being associated with a type of operation such as a query, insertion, or deletion. For a query or an insertion, a search may be executed on the graph to obtain a set of top-K vertices for each vertex. In the case of a deletion, a vertex may be deleted from the proximity graph, and a local or global reconnection update method may be used to reconstruct at least a portion of the proximity graph.

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Sunnyvale, CA, United States of America

Weijie Zhao