Hybrid recommendations by content-aligned Bayesian personalized ranking

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Hybrid recommendations by content-aligned Bayesian personalized ranking

Popis výsledku v původním jazyce

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&apos;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&apos; or objects&apos;. 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.

Název v anglickém jazyce

Hybrid recommendations by content-aligned Bayesian personalized ranking

Popis výsledku anglicky

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&apos;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&apos; or objects&apos;. 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

2018

Kód důvěrnosti údajů

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

Název periodika

New Review of Hypermedia and Multimedia

ISSN

1361-4568

e-ISSN

—

Svazek periodika

24

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

22

Strana od-do

88-109

Kód UT WoS článku

000442014800003

EID výsledku v databázi Scopus

2-s2.0-85048750618