Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
4-2019
Abstract
Studying recommender systems with implicit feedback has become increasingly important. However, most existing works are designed in an offline setting while online recommendation is quite challenging due to the one-class nature of implicit feedback. In this paper, we propose an online collaborative filtering method for implicit feedback. We highlight three critical issues of existing works. First, when positive feedback arrives sequentially, if we treat all the other missing items for this given user as the negative samples, the mis-classified items will incur a large deviation since some items might appear as the positive feedback in the subsequent rounds. Second, the cost of missing a positive feedback should be much higher than that of having a false-positive. Third, the existing works usually assume that a fixed model is given prior to the learning task, which could result in poor performance if the chosen model is inappropriate. To address these issues, we propose a unified framework for Online Collaborative Filtering with Implicit Feedback (OCFIF). Motivated by the regret aversion, we propose a divestiture loss to heal the bias derived from the past mis-classified negative samples. Furthermore, we adopt cost-sensitive learning method to efficiently optimize the implicit MF model without imposing a heuristic weight restriction on missing data. By leveraging meta-learning, we dynamically explore a pool of multiple models to avoid the limitations of a single fixed model so as to remedy the drawback of manual/heuristic model selection. We also analyze the theoretical bounds of the proposed OCFIF method and conduct extensive experiments to evaluate its empirical performance on real-world datasets.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Database Systems for Advanced Applications: 24th International Conference, DASFAA 2019, Chiang Mai, Thailand, April 22-25, Proceedings
Volume
11447
First Page
433
Last Page
448
ISBN
9783030185794
Identifier
10.1007/978-3-030-18579-4_26
Publisher
Springer
City or Country
Cham
Citation
YIN, Jianwen; LIU, Chenghao; LI, Jundong; DAI, Bing Tian; CHEN, Yun-Chen; WU, Min; and SUN, Jianling.
Online collaborative filtering with implicit feedback. (2019). Database Systems for Advanced Applications: 24th International Conference, DASFAA 2019, Chiang Mai, Thailand, April 22-25, Proceedings. 11447, 433-448.
Available at: https://ink.library.smu.edu.sg/sis_research/4420
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-030-18579-4_26