Multiple-attribute Entity Recommendation Based on Classification

doi:10.16182/j.issn1004731x.joss.201802005

Abstract

Abstract: In the process of exploring entity recommendation, the entity containing diverse attributes has gained more and more attention. Most of the current researchers mainly select one attribute, and embody it in the related algorithms and their extensions even though the entity is combined with multiple attributes in entity recommendation. In this paper, on the basis of the classification method, we delve into physical properties of the recommended entities, divide entity’s attribute information network into multiple sub ones. In sub information network, bounded by the amount of attributes, the single attribute and even multiple attributes can be diverted into diverse paths of entity similarity, combining with entity similarity and related algorithm, where we can get the recommended results. This study not only refers to various attributes, but also improves the precision of recommendation.

Key words: entity recommendation, multiple attribution, classification, similarity path

CLC Number:

TP391.9

Song Meina, Zhao Xuejun, E Haihong. Multiple-attribute Entity Recommendation Based on Classification[J]. Journal of System Simulation, 2018, 30(2): 405-413.

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