Issue No. 02 - Feb. (2018 vol. 30)
Yongli Ren , School of Science, RMIT University, Melbourne, Victoria, Australia
Martin Tomko , Department of Infrastructure Engineering, University of Melbourne, Melbourne, Victoria, Australia
Flora Dilys Salim , School of Science, RMIT University, Melbourne, Victoria, Australia
Jeffrey Chan , School of Science, RMIT University, Melbourne, Victoria, Australia
Charles L. A. Clarke , School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
Mark Sanderson , School of Science, RMIT University, Melbourne, Victoria, Australia
Traditionally, recommender systems modelled the physical and cyber contextual influence on people’s moving, querying, and browsing behaviors in isolation. Yet, searching, querying, and moving behaviors 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.
Mobile communication, Search engines, Web search, Context modeling, Mobile handsets, Bipartite graph, Electronic mail
Y. Ren, M. Tomko, F. D. Salim, J. Chan, C. L. Clarke and M. Sanderson, "A Location-Query-Browse Graph for Contextual Recommendation," in IEEE Transactions on Knowledge & Data Engineering, vol. 30, no. 2, pp. 204-218, 2018.