User-centered recommendation using US-ELM based on dynamic graph model in E-commerce View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Linlin Ding, Baishuo Han, Shu Wang, Xiaoguang Li, Baoyan Song

ABSTRACT

The recommender systems can gain the needs and interests of users by analyzing the user history data and then help the users making decisions on appropriate choices in E-commerce. However, with the increasing of data volume and the popularization of information network, the participation of users in E-commerce activities is growing deeply. How to analyze the user preferences and make a user-centered efficient recommendation is an urgent problem to be further researched. In this paper, we first propose the user-centered recommendation based on dynamic graph model to express the user preferences and gain the user preference vectors for recommendation. Then, after gaining the user preferences vectors, we propose the user clustering algorithm using US-ELM to cluster the users into different clusters. Last, we provide two recommendation algorithms, which can present top-k recommendation, respectively the group recommendation and personal recommendation. With the extensive experiments, our recommendation algorithms can effectively express the user preferences and reach a good performance. More... »

PAGES

693-703

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13042-017-0751-z

DOI

http://dx.doi.org/10.1007/s13042-017-0751-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1099691068


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