Clustering-Based Collaborative Filtering Using an Incentivized/Penalized User Model
- Title
- Clustering-Based Collaborative Filtering Using an Incentivized/Penalized User Model
- Author
- 김상욱
- Keywords
- Clustering; collaborative filtering; F-1 score; incentivized/penalized user model; Pearson correlation coefficient; recommender system
- Issue Date
- 2019-05
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Citation
- IEEE ACCESS, v. 7, Page. 62115-62125
- Abstract
- Giving or recommending appropriate content based on the quality of experience is the most important and challenging issue in recommender systems. As collaborative filtering (CF) is one of the most prominent and popular techniques used for recommender systems, we propose a new clustering-based CF (CBCF) method using an incentivized/penalized user (IPU) model only with the ratings given by users, which is thus easy to implement. We aim to design such a simple clustering-based approach with no further prior information while improving the recommendation accuracy. To be precise, the purpose of CBCF with the IPU model is to improve recommendation performance such as precision, recall, and F-1 score by carefully exploiting different preferences among users. Specifically, we formulate a constrained optimization problem in which we aim to maximize the recall (or equivalently F-1 score) for a given precision. To this end, users are divided into several clusters based on the actual rating data and Pearson correlation coefficient. Afterward, we give each item an incentive/penalty according to the preference tendency by users within the same cluster. Our experimental results show a significant performance improvement over the baseline CF scheme without clustering in terms of recall or F-1 score for a given precision.
- URI
- https://ieeexplore.ieee.org/document/8704936/https://repository.hanyang.ac.kr/handle/20.500.11754/151226
- ISSN
- 2169-3536
- DOI
- 10.1109/ACCESS.2019.2914556
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
- Files in This Item:
- Clustering-Based Collaborative Filtering Using an Incentivized Penalized User Model.pdfDownload
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