Learning edge importance in bipartite graph-based recommendations

• Autorzy: Kwieciński Robert , Górecki Tomasz , Filipowska Agata

Identyfikatory

DOI

10.15439/2022F191

• Konferencja: Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

In this work, we propose the P3 Learning to Rank (P3LTR) model, a generalization of the RP3Beta graph-based recommendation method. In our approach, we learn the importance of user-item relations based on features that are usually available in online recommendations (such as types of user-item past interactions and timestamps). We keep the simplicity and explainability of RP3Beta predictions. We report the improvements of P3LTR over RP3Beta on the OLX Jobs Interactions dataset, which we published.

Słowa kluczowe

EN

computer science; bipartite graph; interaction; recommendation method; loss function; training set

PL

informatyka; graf dwudzielny; interakcja; metody rekomendacji; funkcja straty; zestaw treningowy

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 227--233
• Opis fizyczny: Bibliogr. 39 poz., wz., tab., il.

Twórcy

autor

Kwieciński Robert

• Faculty of Mathematics and Computer Science Adam Mickiewicz University Uniwersytetu Poznańskiego 4 61-614 Poznań, Poland
• OLX Group ul. Królowej Jadwigi 43, 61-872 Poznań, Poland

autor

Górecki Tomasz

• Faculty of Mathematics and Computer Science Adam Mickiewicz University Uniwersytetu Poznańskiego 4 61-614 Poznań, Poland

autor

Filipowska Agata

• Poznań University of Economics and Business Al. Niepodległości 10, 61-875 Poznań, Poland
• OLX Group ul. Królowej Jadwigi 43, 61-872 Poznań, Poland

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Uwagi

1. Track 4: 1st Workshop on Personalization and Recommender Systems

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).