Incremental Matrix Co-factorization for Recommender Systems with Implicit Feedback

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2018

Autores

Anyosa, SC (Autor) Outra; Vinagre, J (Autor) Outra; Jorge, AM (Autor) FCUP

Ata de Conferência Internacional

Título: Companion of the The Web Conference 2018 on The Web Conference 2018, WWW 2018, Lyon , France, April 23-27, 2018

Páginas: 1413-1418

Indexação

Scopus - 13 Citações

Outras Informações

ID Authenticus: P-00N-X4P

DOI: 10.1145/3184558.3191585

Abstract (EN): Recommender systems try to predict which items a user will prefer. Traditional models for recommendation only take into account the user-item interaction, usually expressed by explicit ratings. However, in these days, web services continuously generate auxiliary data from users and items that can be incorporated into the recommendation model to improve recommendations. In this work, we propose an incremental Matrix Co-factorization model with implicit user feedback, considering a real-world data-stream scenario. This model can be seen as an extension of the conventional Matrix Factorization that includes additional dimensions to be decomposed in the common latent factor space. We test our proposal against a baseline algorithm that relies exclusively on interaction data, using prequential evaluation. Our experimental results show a significant improvement in the accuracy of recommendations, after incorporating an additional dimension in three music domain datasets. © 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License.

Idioma: Inglês

Tipo (Avaliação Docente): Científica