TRANSFER LEARNING FOR RESOLVING SPARSITY PROBLEM IN RECOMMENDER SYSTEMS: HUMAN VALUES APPROACH
With the rapid rise in popularity of ecommerce application, Recommender Systems are being widely used by them to predict the response that a user will give to a given item. This prediction helps in cross selling, upselling and to increase the loyalty of their customers. However due to lack of suffic...
| Autores: | , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | Brasil |
| Recursos: | Universidade de São Paulo (USP) |
| Repositorio: | Journal of Information Systems and Technology Management (Online) |
| Idioma: | inglés |
| OAI Identifier: | oai:revistas.usp.br:article/143732 |
| Acesso em linha: | https://revistas.usp.br/jistem/article/view/143732 |
| Access Level: | acceso abierto |
| Palavra-chave: | Recommender systems Collaborative filtering Sparsity problem Transfer learning Basic Human Values |
| Resumo: | With the rapid rise in popularity of ecommerce application, Recommender Systems are being widely used by them to predict the response that a user will give to a given item. This prediction helps in cross selling, upselling and to increase the loyalty of their customers. However due to lack of sufficient feedback data these systems suffer from sparsity problem which leads to decline in their prediction efficiency. In this work, we have proposed and empirically demonstrated how the Transfer Learning approach using five dimensions of basic human values can be successfully used to alleviate the sparsity problem and increase the efficiency of recommender system algorithms. |
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