Cv-PCR

Abstract

Patent citation recommendation and prior patent search, critical for patent filing and patent examination, have become increasingly difficult due to the rapidly growing number of patents. Unlike paper citations that focus on reference comprehensiveness, patent citations tend to be more parsimonious and refer only to those prior patents bearing significant technological and/or economic value, as they define the scope of the citing patent and thus have significant legal and economic implications. Based on the insight that patent citations are important information reflecting the value of cited patents to the citing patent, we propose a heterogeneous patent citation-bibliographic network that combines patent citations (reflecting value relation) and bibliographic information (reflecting similarity relation) together. From this network, we extract various features that reflect the value of a prior patent to a query patent with regard to the context of the query patent such as its assignee, classifications, etc. We then propose a two-stage framework for patent citation recommendation. Our idea is that by exploiting those context-specific value measures of candidate patents to the query patent, the proposed framework is able to make effective patent citation recommendations. We evaluate the proposed context-guided value-driven framework using a collection of 1.8M U.S. patents. Experimental results validate our ideas and show that those value-driven features are very effective and significantly outperform two state-of-the-art methods in terms of both the precision and recall rates.

Context-Aware and Sequential Pattern Mining based recommendations for Research Papers: A Hybrid Approach

“…Some measurement techniques have been utilized by the recommender systems for research papers [101][102][103] such as index h [104], bibliographic coupling [105], and co-citation [106] approaches. Other related studies on this field comprise recommender system for educational digital library [107], academic notifying services [108], automatic accumulation of academic studies [109][110][111], expert research [112], research discoveries via recommender systems [113,114], academic events recommender system [115], venue recommendations for research papers [116], patent citation recommendations [117], and recommendations for research datasets [118].…”

A survey on scholarly data: From big data perspective

“…The concept of recommendation systems finds important applications in the field of big scholarly data. Several types of recommender systems can be used to recommend research papers, books [122], academic events [131], venues [132], news feed [129] [130], citations for patents [133] and academic datasets [134]. Brusilovsky et al [123] have also introduced the concept of educational recommendation.…”