Electronics | |
An Improved Recommender System Solution to Mitigate the Over-Specialization Problem Using Genetic Algorithms | |
Omar Bencharef1  Soulaimane Kaloun1  Oumaima Stitini1  | |
[1] Computer and System Engineering Laboratory, Faculty of Science and Technology, Cadi Ayyad University, Marrakech 40000, Morocco; | |
关键词: genetic algorithms; recommender system; over-specialization; content-based filtering; limited content; | |
DOI : 10.3390/electronics11020242 | |
来源: DOAJ |
【 摘 要 】
Nowadays, recommendation systems offer a method of facilitating the user’s desire. It is useful for recommending items from a variety of areas such as in the e-commerce, medical, education, tourism, and industry domains. The e-commerce area represents the most active research we found, which assists users in locating the things they want. A recommender system can also provide users with helpful knowledge about things that could be of interest. Sometimes, the user gets bored with recommendations which are similar to their profiles, which leads to the over-specialization problem. Over-specialization is caused by limited content data, under which content-based recommendation algorithms suggest goods directly related to the customer profile rather than new things. In this study, we are particularly interested in recommending surprising, new, and unexpected items that may likely be enjoyed by users and will mitigate this limited content. In order to recommend novel and serendipitous items along with familiar items, we need to introduce additional hacks and note of randomness, which can be achieved using genetic algorithms that brings diversity to recommendations being made. This paper describes a Revolutionary Recommender System using a Genetic Algorithm called
【 授权许可】
Unknown