A Location-Query-Browse Graph for Contextual Recommendation

Publication Year: 2018 Publication Type : JournalArticle


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.


    author = {Yongli Ren and Martin Tomko and Flora Dilys Salim and Jeffrey Chan and Charles L. A. Clarke and Mark Sanderson},
    bibsource = {dblp computer science bibliography, https://dblp.org},
    biburl = {https://dblp.org/rec/journals/tkde/RenTSCCS18.bib},
    doi = {10.1109/TKDE.2017.2766059},
    journal = {IEEE Trans. Knowl. Data Eng.},
    number = {2},
    pages = {204--218},
    timestamp = {Sat, 19 Oct 2019 01:00:00 +0200},
    title = {A Location-Query-Browse Graph for Contextual Recommendation},
    url = {https://doi.org/10.1109/TKDE.2017.2766059},
    volume = {30},
    year = {2018}


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