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Content Provider | IEEE Xplore Digital Library |
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Author | Deqing Yang Yanghua Xiao Yangqiu Song Wei Wang |
Copyright Year | 2015 |
Description | Author affiliation: Shanghai Key Lab. of Data Sci., Fudan Univ., Shanghai, China (Deqing Yang; Yanghua Xiao; Wei Wang) || Dept. of Comput. Sci., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA (Yangqiu Song) |
Abstract | Cross-domain recommendation has attracted wide research interest which generally aims at improving the recommendation performance by alleviating the cold start problem in collaborative filtering based recommendation or generating a more comprehensive user profiles from multiple domains. In most previous cross-domain recommendation settings, explicit or implicit relationships can be easily established across different domains. However, many real applications belong to a more challenging setting: recommendation across heterogeneous domains without explicit relationships, where neither explicit user-item relations nor overlapping features exist between different domains. In this new setting, we need to (1) enrich the sparse data to characterize users or items and (2) bridge the gap caused by the heterogenous features in different domains. To overcome the first challenge, we proposed an optimized local tag propagation algorithm to generate descriptive tags for user profiling. For the second challenge, we proposed a semantic relatedness metric by mapping the heterogenous features onto their concept space derived from online encyclopedias. We conducted extensive experiments on two real datasets to justify the effectiveness of our solution. |
Starting Page | 1075 |
Ending Page | 1080 |
File Size | 654076 |
Page Count | 6 |
File Format | |
ISSN | 15504786 |
e-ISBN | 9781467395045 |
DOI | 10.1109/ICDM.2015.35 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2015-11-14 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Semantics Media Motion pictures Twitter Computer science Electronic mail Bridges user profiling cross-domain recommendation heterogenous domains semantic matching |
Content Type | Text |
Resource Type | Article |
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