Title: Recommender resources based on acquiring user's requirement and exploring user's preference with Word2Vec model in web service

Authors: Jiwei Qin; Yunpeng Jiang

Addresses: School of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China; Center of Network and Information Technology, Xinjiang University, Urumqi, 830046, China ' School of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China

Abstract: Traditional recommender algorithms mainly use structured data (resource tag, user feature etc.) to depict the user preference and ignore the semantic relations of resources. In this paper, we present a new idea for acquiring user's requirement and exploring user's preference with Word2Vec model (RP-Word2Vec) to find the interested and personal resource in the web service. We use Word2Vec model to measure the sentiment among keywords and acquire user's requirement as accurately as possible; and we treat resources as the input of Word2Vec model based on history behaviours and adopt a semantic similarity measuring process to recommend interested and personal resource for the user. The experiments results that the presented RP-Word2Vec supports more effective.

Keywords: user's requirement; user's preference; Word2Vec model; web service; semantic.

DOI: 10.1504/IJIPT.2019.101362

International Journal of Internet Protocol Technology, 2019 Vol.12 No.3, pp.144 - 152

Received: 16 Jul 2018
Accepted: 07 Oct 2018
Published online: 05 Aug 2019 *

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