Recency augmented hybrid collaborative movie recommendation system
| Autor: | B. Surendiran, R. Sujithra Alias Kanmani, S. P. Syed Ibrahim |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Information retrieval
Computer Networks and Communications business.industry Computer science Applied Mathematics Temporal context Recommender system Computer Science Applications Computational Theory and Mathematics Artificial Intelligence Hybrid system The Internet Electrical and Electronic Engineering business User needs Relevant information Information Systems |
| Zdroj: | International Journal of Information Technology. 13:1829-1836 |
| ISSN: | 2511-2112 2511-2104 |
| DOI: | 10.1007/s41870-021-00769-w |
| Popis: | As days rolls internet information grows exponentially and the personalized recommendation system has its vibrant area of research providing relevant information to the user needs. Hybrid recommender systems combines both implicit and explicit feedbacks from user by integrating collaborative and content based recommender system. However, the real time hybrid systems are not focusing on current temporal context of the user on providing the recommendation. This paper proposed a hybrid movie recommendation system that considers recent transactions and also demographic attributes for recommending a item to the target user. The demographic attributes aids in overcoming the cold start problem. The results of the experimental study on movie lens dataset clearly indicate that the proposed system was found to be effective by considering the recent transactions with higher ratings. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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