Hybrid recommendations by content-aligned Bayesian personalized ranking
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10381222" target="_blank" >RIV/00216208:11320/18:10381222 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1080/13614568.2018.1489002" target="_blank" >https://doi.org/10.1080/13614568.2018.1489002</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/13614568.2018.1489002" target="_blank" >10.1080/13614568.2018.1489002</a>
Alternative languages
Result language
angličtina
Original language name
Hybrid recommendations by content-aligned Bayesian personalized ranking
Original language description
In many application domains of recommender systems, content-based (CB) information are available for users, objects or both. CB information plays an important role in the process of recommendation, especially in cold-start scenarios, where the volume of feedback data is low. However, CB information may come from several, possibly external, sources varying in reliability, coverage or relevance to the recommending task. Therefore, each content source or attribute possess a different level of informativeness, which should be taken into consideration during the process of recommendation. In this paper, we propose a Content-Aligned Bayesian Personalized Ranking Matrix Factorization method (CABPR), extending Bayesian Personalized Ranking Matrix Factorization (BPR) by incorporating multiple sources of content information into the BPR's optimization procedure. The working principle of CABPR is to calculate user-to-user and object-to-object similarity matrices based on the content information and penalize differences in latent factors of closely related users' or objects'. CABPR further estimates relevance of similarity matrices as a part of the optimization procedure. CABPR method is a significant extension of a previously published BPR_MCA method, featuring additional variants of optimization criterion and improved optimization procedure. Four variants of CABPR were evaluated on two publicly available datasets: MovieLens 1M dataset, extended by data from IMDB, DBTropes and ZIP code statistics and LOD-RecSys dataset extended by the information available from DBPedia. Experiments shown that CABPR significantly improves over standard BPR as well as BPR_MCA method w.r.t. several cold-start scenarios.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
New Review of Hypermedia and Multimedia
ISSN
1361-4568
e-ISSN
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Volume of the periodical
24
Issue of the periodical within the volume
2
Country of publishing house
GB - UNITED KINGDOM
Number of pages
22
Pages from-to
88-109
UT code for WoS article
000442014800003
EID of the result in the Scopus database
2-s2.0-85048750618