| Title | Implicit feedback recommender system based on matrix factorization |
| Creator | |
| Date Issued | 2018-01-08 |
| Source Publication | Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
 |
| ISSN | 1867-8211 |
| Volume | 2017-September |
| Abstract | With the development of the internet age, information overload problem is imminent. At now, almost of recommended models use the explicit feedback. But lots of implicit feedback data are missing. The paper explores the area of recommendation based on large-scale implicit feedback, Where only positive feedback is available. Further, the paper carried on the empirical research on the Implicit Feedback Recommendation Model. By maximized the probability of the user’s choices, IFR mean the progress task into optimization problems In the way, the experiment results confirm the superiority of the model. However, the model is insufficient about online research and a lack of details. |
| Keyword | Implicit feedback Matrix decomposition Recommendation system |
| DOI | 10.4108/eai.28-9-2017.2273855 |
| URL | View source |
| Language | 英语English |
| Scopus ID | 2-s2.0-85051018192 |
| Citation statistics | |
| Document Type | Conference paper |
| Identifier | http://repository.uic.edu.cn/handle/39GCC9TT/12870 |
| Collection | Research outside affiliated institution |
| Corresponding Author | Jiang，Na |
| Affiliation | Zhaotong University,China |
| Recommended Citation GB/T 7714 | Jiang，Na. Implicit feedback recommender system based on matrix factorization[C], 2018. |
| Files in This Item: | There are no files associated with this item. | |||||
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