A location-query-browse graph for contextual recommendation

Ren, Y, Tomko, M, Salim, F, Chan, J, Clarke, C and Sanderson, M 2018, 'A location-query-browse graph for contextual recommendation', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 2, pp. 204-218.

Document type: Journal Article
Collection: Journal Articles

Attached Files
Name Description MIMEType Size
n2006079757.pdf Accepted Manuscript application/pdf 984.94KB
Title A location-query-browse graph for contextual recommendation
Author(s) Ren, Y
Tomko, M
Salim, F
Chan, J
Clarke, C
Sanderson, M
Year 2018
Journal name IEEE Transactions on Knowledge and Data Engineering
Volume number 30
Issue number 2
Start page 204
End page 218
Total pages 15
Publisher IEEE
Abstract Traditionally, recommender systems modelled the physical and cyber contextual influence on people's moving, querying, and browsing behaviours in isolation. Yet, searching, querying and moving behaviours are intricately linked, especially indoors. Here, we introduce a tripartite location-query-browse graph (LQB) for nuanced contextual recommendations. The LQB graph consists of three kinds of nodes: locations, queries and Web domains. Directed connections only between heterogeneous nodes represent the contextual influences, while connections of homogeneous nodes are inferred from the contextual influences of the other nodes. This tripartite LQB graph is more reliable than any monopartite or bipartite graph in contextual location, query and Web content recommendations. We validate this LQB graph in an indoor retail scenario with extensive dataset of three logs collected from over 120,000 anonymized, opt-in users over a 1-year period in a large inner-city mall in Sydney, Australia. We characterize the contextual influences that correspond to the arcs in the LQB graph, and evaluate the usefulness of the LQB graph for location, query, and Web content recommendations. The experimental results show that the LQB graph successfully captures the contextual influence and significantly outperforms the state of the art in these applications.
Subject Information Retrieval and Web Search
Keyword(s) Location-query-browse graph
contextual recommendation
query log analysis
information retrieval
DOI - identifier 10.1109/TKDE.2017.2766059
Copyright notice © 2017 IEEE
ISSN 1041-4347
Additional Notes Personal use is permitted, but republication/redistribution require IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 7 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 74 Abstract Views, 111 File Downloads  -  Detailed Statistics
Created: Wed, 19 Sep 2018, 13:27:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us