Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김상욱 | - |
dc.date.accessioned | 2016-11-10T06:09:59Z | - |
dc.date.available | 2016-11-10T06:09:59Z | - |
dc.date.issued | 2015-04 | - |
dc.identifier.citation | Proceedings of the 30th Annual ACM Symposium on Applied Computing, Pages 1148-1153 | en_US |
dc.identifier.isbn | 978-1-4503-3196-8 | - |
dc.identifier.uri | http://dl.acm.org/citation.cfm?doid=2695664.2695840 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11754/24277 | - |
dc.description.abstract | Recommender systems have been an active research topic for the past decade. Previous studies have primarily focused on recommendations to a single user. Recently, several interesting approaches to make group recommendations have been proposed. However, the accuracy of existing approaches is significantly affected by the size and cohesiveness of a group. In this paper, we present a novel approach that makes effective group recommendations regardless of the group size or cohesiveness. We first model the relationships between a set of users and a set of items as a bipartite graph from the ratings information. On this graph, we employ the belief propagation to determine probabilistically the target user group’s preference on items. We also propose a new group type that reflects real-life groups effectively and helps better evaluation of group recommendation approaches. Through extensive experiments on a real-life data set, we show that the proposed approach is more accurate than the existing ones up to 20%. | en_US |
dc.description.sponsorship | This research was supported by (1) Business (Grants No. C0191469) for Cooperative R&D between Industry, Academy, and Research Institute funded Korea Small and Medium Business Administration in 2014, (2) the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2014-H0301-14-1022) supervised by the NIPA (National IT Industry Promotion Agency), (3) the ICT R&D program of MSIP/IITP (14-824-09-001, Development of High Performance Visual BigData Discovery Platform for Large-Scale Realtime Data Analysis), and (4) Semiconductor Industry Collaborative Project between Hanyang University and Samsung Electronics Co. Ltd. | en_US |
dc.language.iso | en | en_US |
dc.publisher | ACM SAC | en_US |
dc.subject | Recommender systems | en_US |
dc.subject | group recommendation | en_US |
dc.subject | belief propagation | en_US |
dc.title | An Effective Approach to Group Recommendation Based on Belief Propagation | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1145/2695664.2695840 | - |
dc.relation.page | 13-17 | - |
dc.contributor.googleauthor | Ali, Irfan | - |
dc.contributor.googleauthor | Kim, Sang-Wook | - |
dc.relation.code | 20150201 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF COMPUTER SCIENCE | - |
dc.identifier.pid | wook | - |
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