File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1137/1.9781611974973.42
- Scopus: eid_2-s2.0-85027884808
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Conference Paper: HBGG: a Hierarchical Bayesian Geographical Model for Group Recommendation
Title | HBGG: a Hierarchical Bayesian Geographical Model for Group Recommendation |
---|---|
Authors | |
Issue Date | 2017 |
Publisher | Society for Industrial and Applied Mathematics (SIAM). |
Citation | 2017 SIAM International Conference on Data Mining, Houston, TX, 27-29 April 2017. In Proceedings of the 2017 SIAM International Conference on Data Mining, 2017, p. 372-380 How to Cite? |
Abstract | Location-based social networks such as Foursquare and Plancast have gained increasing popularity. On those sites, users can organize and participate in group activities; hence, recommending venues to a group is of practical importance. In this paper, we study the problem of recommending venues to groups of users and propose a Hierarchical Bayesian Model (HBGG) for this purpose. First, a generative group geographical topic model (GG) which exploits group membership, group mobility regions and group preferences is proposed. And we integrate social structure into one-class collaborative filtering as social-based collaborative filtering (SOCF) to leverage social wisdom. Through the shared latent group features, HBGG connects the group geographical model with SOCF framework for group recommendation. Experimental results on two real datasets show that our methods outperforms the state-of-the-art group recommenders, especially on cold-start user groups. |
Persistent Identifier | http://hdl.handle.net/10722/246602 |
ISBN |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lu, Z | - |
dc.contributor.author | Li, H | - |
dc.contributor.author | Mamoulis, N | - |
dc.contributor.author | Cheung, DWL | - |
dc.date.accessioned | 2017-09-18T02:31:22Z | - |
dc.date.available | 2017-09-18T02:31:22Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | 2017 SIAM International Conference on Data Mining, Houston, TX, 27-29 April 2017. In Proceedings of the 2017 SIAM International Conference on Data Mining, 2017, p. 372-380 | - |
dc.identifier.isbn | 978-1-61197-497-3 | - |
dc.identifier.uri | http://hdl.handle.net/10722/246602 | - |
dc.description.abstract | Location-based social networks such as Foursquare and Plancast have gained increasing popularity. On those sites, users can organize and participate in group activities; hence, recommending venues to a group is of practical importance. In this paper, we study the problem of recommending venues to groups of users and propose a Hierarchical Bayesian Model (HBGG) for this purpose. First, a generative group geographical topic model (GG) which exploits group membership, group mobility regions and group preferences is proposed. And we integrate social structure into one-class collaborative filtering as social-based collaborative filtering (SOCF) to leverage social wisdom. Through the shared latent group features, HBGG connects the group geographical model with SOCF framework for group recommendation. Experimental results on two real datasets show that our methods outperforms the state-of-the-art group recommenders, especially on cold-start user groups. | - |
dc.language | eng | - |
dc.publisher | Society for Industrial and Applied Mathematics (SIAM). | - |
dc.relation.ispartof | Proceedings of the 2017 SIAM International Conference on Data Mining | - |
dc.title | HBGG: a Hierarchical Bayesian Geographical Model for Group Recommendation | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Mamoulis, N: nikos@cs.hku.hk | - |
dc.identifier.email | Cheung, DWL: dcheung@cs.hku.hk | - |
dc.identifier.authority | Mamoulis, N=rp00155 | - |
dc.identifier.authority | Cheung, DWL=rp00101 | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1137/1.9781611974973.42 | - |
dc.identifier.scopus | eid_2-s2.0-85027884808 | - |
dc.identifier.hkuros | 276652 | - |
dc.identifier.spage | 372 | - |
dc.identifier.epage | 380 | - |
dc.publisher.place | Houston, TX | - |