Improving the accuracy of top-N recommendation using a preference model
- Title
- Improving the accuracy of top-N recommendation using a preference model
- Author
- 김상욱
- Keywords
- Preference Model; Collaborative filtering; Top-N Recomendation; Recommender Systems; Accuracy
- Issue Date
- 2016-06
- Publisher
- ELSEVIER SCIENCE INC
- Citation
- INFORMATION SCIENCES, v. 348, Page. 290-304
- Abstract
- In this paper, we study the problem of retrieving a ranked list of top-N items to a target user in recommender systems. We first develop a novel preference model by distinguishing different rating patterns of users, and then apply it to existing collaborative filtering (CF) algorithms. Our preference model, which is inspired by a voting method, is well suited for representing qualitative user preferences. In particular, it can be easily implemented with less than 100 lines of codes on top of existing CF algorithms such as user based, item-based, and matrix-factorization-based algorithms. When our preference model is combined to three kinds of CF algorithms, experimental results demonstrate that the preference model can improve the accuracy of all existing CF algorithms such as ATOP and NDCG@25 by 3-24% and 6-98%, respectively. (C) 2016 Elsevier Inc. All rights reserved.
- URI
- https://www.sciencedirect.com/science/article/pii/S0020025516300524?via%3Dihubhttps://repository.hanyang.ac.kr/handle/20.500.11754/72659
- ISSN
- 0020-0255; 1872-6291
- DOI
- 10.1016/j.ins.2016.02.005
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML