Linking Content Information with Bayesian Personalized Ranking via Multiple Content Alignments

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Linking Content Information with Bayesian Personalized Ranking via Multiple Content Alignments

Popis výsledku v původním jazyce

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&apos; and items&apos; 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.

Název v anglickém jazyce

Linking Content Information with Bayesian Personalized Ranking via Multiple Content Alignments

Popis výsledku anglicky

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&apos; and items&apos; 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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA17-22224S" target="_blank" >GA17-22224S: Analytika uživatelských preferencí v modelech multimediální explorace</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 28th ACM Conference on Hypertext and Social Media

ISBN

978-1-4503-4708-2

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

9

Strana od-do

175-183

Název nakladatele

ACM

Místo vydání

New York, NY, USA

Místo konání akce

Prague, Czech Republic

Datum konání akce

4. 7. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—