Conference Proceeding Article
Collaborative Filtering (CF) is one of the most successful learning techniques in building real-world recommender systems. Traditional CF algorithms are often based on batch machine learning methods which suffer from several critical drawbacks, e.g., extremely expensive model retraining cost whenever new samples arrive, unable to capture the latest change of user preferences over time, and high cost and slow reaction to new users or products extension. Such limitations make batch learning based CF methods unsuitable for real-world online applications where data often arrives sequentially and user preferences may change dynamically and rapidly. To address these limitations, we investigate online collaborative filtering techniques for building live recommender systems where the CF model can evolve on-the-fly over time. Unlike the regular first order CF algorithms (e.g., online gradient descent for CF) that converge slowly, in this paper, we present a new framework of second order online collaborative filtering, i.e., Confidence Weighted Online Collaborative Filtering (CWOCF), which applies the second order online optimization methodology to tackle the online collaborative filtering task. We conduct extensive experiments on several large-scale datasets, in which the encouraging results demonstrate that the proposed algorithms obtain significantly lower errors (both RMSE and MAE) than the state-of-the-art first order CF algorithms when receiving the same amount of training data in the online learning process.
Computer Sciences | Databases and Information Systems
Data Management and Analytics
JMLR: Workshop and Conference Proceedings: ACML 2013, November 13-15, Canberra
City or Country
Lu, Jing; HOI, Steven C. H.; Wang, Jialei; and Zhao, Peilin.
Second Order Online Collaborative Filtering. (2013). JMLR: Workshop and Conference Proceedings: ACML 2013, November 13-15, Canberra. 29, 325-340. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2288
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.