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A Study on the Context-Aware Hybrid Bayesian Recommender System on the Mobile Devices
| Content Provider | Semantic Scholar |
|---|---|
| Author | Lee, Hak-Min Um, Jong-Seok |
| Copyright Year | 2017 |
| Abstract | The objective is to develop recommender system in mobile device to recommend proper items by combining context information obtained from mobile device, user’s preference ratings, and features of items. A Bayesian hybrid recommender system is constructed by combining content-based filtering and collaborative filtering. Context information acquired from mobile devices such as GPS, whether, and time are transformed into usable data. Combining usable context information and the Bayesian hybrid recommender system, a context-aware hybrid Bayesian recommender is proposed. MovieLens data is used for simulation which contains movies with genres, user ratings, and time. Time is transformed to usable context information. This paper proposed a context-aware Bayesian hybrid recommender system which combines context information collected from mobile devices and user preference. By using canonical weights which are introduced by Campos, complex problem of computing conditional distribution is changed into simple linear sum of weights. This algorithm saves storage space and computing time, which is good for developing recommender system on the mobile devices. The objective is to develop a recommender system on the mobile device which improves accuracy of prediction by using context information. We use context information as season and time of the day for evaluating the proposed recommender. Simulation result shows that accuracy of the proposed recommender is lower than the existing recommender with small number of similar users. However, the proposed recommender improves the accuracy on predicting user preference as the number of similar users increase. Context information usable to recommender system has various types depending on the application domain. More precise prediction is possible if we use context information with a great impact on the user preference. We show that the proposed recommender system using context information has improved the accuracy on predicting user preference with moderate number of similar users. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.iaeng.org/IJCS/issues_v45/issue_1/IJCS_45_1_08.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
