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Recommending patents based on latent topics

Published:12 October 2013Publication History

ABSTRACT

The availability of large volumes of granted patents and applications, all publicly available on the Web, enables the use of sophisticated text mining and information retrieval methods to facilitate access and analysis of patents. In this paper we investigate techniques to automatically recommend patents given a query patent. This task is critical for a variety of patent-related analysis problems such as finding relevant citations, research of relevant prior art, and infringement analysis. We investigate the use of latent Dirichlet allocation and Dirichlet multinomial regression to represent patent documents and to compute similarity scores. We compare our methods with state-of-the-art document representations and retrieval techniques and demonstrate the effectiveness of our approach on a collection of US patent publications.

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        • Published in

          cover image ACM Conferences
          RecSys '13: Proceedings of the 7th ACM conference on Recommender systems
          October 2013
          516 pages
          ISBN:9781450324090
          DOI:10.1145/2507157
          • General Chairs:
          • Qiang Yang,
          • Irwin King,
          • Qing Li,
          • Program Chairs:
          • Pearl Pu,
          • George Karypis

          Copyright © 2013 ACM

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          Publication History

          • Published: 12 October 2013

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          RecSys '13 Paper Acceptance Rate32of136submissions,24%Overall Acceptance Rate254of1,295submissions,20%

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