Linking Content Information with Bayesian Personalized Ranking via Multiple Content Alignments
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10360924" target="_blank" >RIV/00216208:11320/17:10360924 - isvavai.cz</a>
Result on the web
<a href="http://dl.acm.org/citation.cfm?id=3078732&CFID=788882522" target="_blank" >http://dl.acm.org/citation.cfm?id=3078732&CFID=788882522</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3078714.3078732" target="_blank" >10.1145/3078714.3078732</a>
Alternative languages
Result language
angličtina
Original language name
Linking Content Information with Bayesian Personalized Ranking via Multiple Content Alignments
Original language description
In many application domains of recommender systems, content-based information are available for users, objects or both. Such information can be processed during recommendation and significantly decrease the cold-start problem. However, content information may come from several, possibly external, sources. Some sources may be incomplete, less reliable or less relevant for the purpose of recommendation. Thus, each content source or attribute possess different level of informativeness, which should be taken into consideration during the process of recommendation. In this paper, we propose a multiple content alignments extension to the Bayesian Personalized Ranking Matrix Factorization (BPR-MCA). The proposed method incorporates multiple sources of content information in the form of user-to-user or object-to-object similarity matrices and aligns users' and items' latent factors ac-cording to these similarities. During the training phase, BPR-MCA also learns the relevance weight of each similarity matrix. BPR-MCA was evaluated on the MovieLens 1M dataset, extended by the content information from IMDB, DBTropes and ZIP code statistics. The experiment shows that BPR-MCA can help to significantly improve recommendation w.r.t. nDCG and AUPR over standard BPR under several cold-start scenarios.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA17-22224S" target="_blank" >GA17-22224S: User preference analytics in multimedia exploration models</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 28th ACM Conference on Hypertext and Social Media
ISBN
978-1-4503-4708-2
ISSN
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e-ISSN
neuvedeno
Number of pages
9
Pages from-to
175-183
Publisher name
ACM
Place of publication
New York, NY, USA
Event location
Prague, Czech Republic
Event date
Jul 4, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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