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Conference Paper: P-LAG: Location-Aware Group Recommendation for Passive Users

TitleP-LAG: Location-Aware Group Recommendation for Passive Users
Authors
Issue Date2017
PublisherSpringer.
Citation
International Symposium on Spatial and Temporal Databases, Arlington, VA, 21-23 August 2017. In Gertz M. et al. (Eds). Advances in Spatial and Temporal Databases, 2017, v. 10411, p. 242-259 How to Cite?
AbstractConsider a group of users who would like to meet to a place in order to participate in an activity together (e.g., meet at a restaurant to dine). Such meeting point queries have been studied in the context of spatial databases, where typically the suggested points are the ones that minimize an aggregate traveling distance. Recently, meeting point queries have been enriched to take as input, besides the locations of users, also some preference criteria (e.g., expressed by some keywords). However, in many applications, a group of users may require a meeting point recommendation without explicitly specifying any preferences. Motivated by this, we study this scenario of group recommendation for such passive users. We use topic modeling to infer the preferences of the group on the different points of interest and combine these preferences with the aggregate spatial distance of the group members to the candidate points for recommendation in a unified search model. Then, we propose an extension of the R-tree index, called TAR-tree, that indexes the topic vectors of the places together with their spatial locations, in order to facilitate efficient group recommendation. We propose and compare three variants of the TAR-tree and a compression technique for the index, that improves its performance. The proposed techniques are evaluated on real data; the results demonstrate the efficiency and effectiveness of our methods.
Persistent Identifierhttp://hdl.handle.net/10722/245445
ISBN
ISI Accession Number ID
Series/Report no.Lecture Notes in Computer Science book series (LNCS, volume 10411)

 

DC FieldValueLanguage
dc.contributor.authorQian, Y-
dc.contributor.authorLu, Z-
dc.contributor.authorMamoulis, N-
dc.contributor.authorCheung, DWL-
dc.date.accessioned2017-09-18T02:10:51Z-
dc.date.available2017-09-18T02:10:51Z-
dc.date.issued2017-
dc.identifier.citationInternational Symposium on Spatial and Temporal Databases, Arlington, VA, 21-23 August 2017. In Gertz M. et al. (Eds). Advances in Spatial and Temporal Databases, 2017, v. 10411, p. 242-259-
dc.identifier.isbn978-3-319-64366-3-
dc.identifier.urihttp://hdl.handle.net/10722/245445-
dc.description.abstractConsider a group of users who would like to meet to a place in order to participate in an activity together (e.g., meet at a restaurant to dine). Such meeting point queries have been studied in the context of spatial databases, where typically the suggested points are the ones that minimize an aggregate traveling distance. Recently, meeting point queries have been enriched to take as input, besides the locations of users, also some preference criteria (e.g., expressed by some keywords). However, in many applications, a group of users may require a meeting point recommendation without explicitly specifying any preferences. Motivated by this, we study this scenario of group recommendation for such passive users. We use topic modeling to infer the preferences of the group on the different points of interest and combine these preferences with the aggregate spatial distance of the group members to the candidate points for recommendation in a unified search model. Then, we propose an extension of the R-tree index, called TAR-tree, that indexes the topic vectors of the places together with their spatial locations, in order to facilitate efficient group recommendation. We propose and compare three variants of the TAR-tree and a compression technique for the index, that improves its performance. The proposed techniques are evaluated on real data; the results demonstrate the efficiency and effectiveness of our methods.-
dc.languageeng-
dc.publisherSpringer.-
dc.relation.ispartofAdvances in Spatial and Temporal Databases-
dc.relation.ispartofseriesLecture Notes in Computer Science book series (LNCS, volume 10411)-
dc.rightsThe final publication is available at Springer via http://dx.doi.org/[insert DOI]-
dc.titleP-LAG: Location-Aware Group Recommendation for Passive Users-
dc.typeConference_Paper-
dc.identifier.emailMamoulis, N: nikos@cs.hku.hk-
dc.identifier.emailCheung, DWL: dcheung@cs.hku.hk-
dc.identifier.authorityMamoulis, N=rp00155-
dc.identifier.authorityCheung, DWL=rp00101-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-319-64367-0_13-
dc.identifier.hkuros276655-
dc.identifier.volume10411-
dc.identifier.spage242-
dc.identifier.epage259-
dc.identifier.isiWOS:000432081700013-

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