URecommender: An API for Recommendation Systems
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Data
2020-01-01
Autores
Teruya, Haroldo Shigueaki [UNESP]
Marcal, Ingrid [UNESP]
Messias Correia, Ronaldo Celso [UNESP]
Garcia, Rogerio Eduardo [UNESP]
Eler, Danilo Medeiros [UNESP]
Rodrigues Nunes, Joao Osvaldo [UNESP]
Rocha, A.
Perez, B. E.
Penalvo, F. G.
Miras, M. D.
Título da Revista
ISSN da Revista
Título de Volume
Editor
Ieee
Resumo
Recommendation systems are intended to assist users in dealing with information overload by providing a content filtering mechanism. Content filtering is based on the user's preferences and interests. Current recommendation systems suffer from the problem of a lack of initial information about new users. This problem, known as the cold-start problem, is present both in existing systems and in new systems, in which any user is a new user. In addition, web application developers find it difficult to integrate recommendation systems into their applications, having to resort to third-party software or develop the recommendation system from scratch. In this work, URecommender is proposed, an API for web recommendation systems composed of a Middleware and a Framework capable of identifying the textual content of greatest interest to the user and recommending relevant related content. Such identification is done implicitly and based on the user's current behavior, which can solve the cold-start problem. In addition, URecommender gives the developer greater control over the recommendation system that will be integrated into the web application under development. The API was used for the development of a real web application and demonstrated good results in the recommendations generated.
Descrição
Palavras-chave
Recommendation systems, Web, information filtering, similarity measure, recommendations, cold-start
Como citar
2020 15th Iberian Conference On Information Systems And Technologies (cisti'2020). New York: Ieee, 6 p., 2020.