In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Chen:2020:2020/5206087,
author = {Chen, J and Wang, Z and Zhu, T and Rosas, FE},
doi = {2020/5206087},
journal = {Complexity},
pages = {1--19},
title = {Recommendation algorithm in double-layer network based on vector dynamic evolution clustering and attention mechanism},
url = {http://dx.doi.org/10.1155/2020/5206087},
volume = {2020},
year = {2020}
}
TY - JOUR
AB - The purpose of recommendation systems is to help users find effective information quickly and conveniently and also to present the items that users are interested in. While the literature of recommendation algorithms is vast, most collaborative filtering recommendation approaches attain low recommendation accuracies and are also unable to track temporal changes of preferences. Additionally, previous differential clustering evolution processes relied on a single-layer network and used a single scalar quantity to characterise the status values of users and items. To address these limitations, this paper proposes an effective collaborative filtering recommendation algorithm based on a double-layer network. This algorithm is capable of fully exploring dynamical changes of user preference over time and integrates the user and item layers via an attention mechanism to build a double-layer network model. Experiments on Movielens, CiaoDVD, and Filmtrust datasets verify the effectiveness of our proposed algorithm. Experimental results show that our proposed algorithm can attain a better performance than other state-of-the-art algorithms.
AU - Chen,J
AU - Wang,Z
AU - Zhu,T
AU - Rosas,FE
DO - 2020/5206087
EP - 19
PY - 2020///
SN - 1076-2787
SP - 1
TI - Recommendation algorithm in double-layer network based on vector dynamic evolution clustering and attention mechanism
T2 - Complexity
UR - http://dx.doi.org/10.1155/2020/5206087
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000553511500002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://www.hindawi.com/journals/complexity/2020/5206087/
UR - http://hdl.handle.net/10044/1/84138
VL - 2020
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
Email us.
Sign up to our mailing list.
Follow us on Twitter, LinkedIn and Instagram.
