Second order online collaborative filtering
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 ex- pensive model retraining cost whene...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
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2014
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| Online Access: | https://hdl.handle.net/10356/103092 http://hdl.handle.net/10220/24401 http://jmlr.org/proceedings/papers/v29/Lu13.html |
| _version_ | 1811692652605734912 |
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| author | Lu, Jing Hoi, Steven Wang, Jialei Zhao, Peilin |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Lu, Jing Hoi, Steven Wang, Jialei Zhao, Peilin |
| author_sort | Lu, Jing |
| collection | NTU |
| description | 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 ex- pensive 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-y 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 On- line Collaborative Filtering (CWOCF), which applies the second order online optimization methodology to tackle the online collaborative giltering 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. |
| first_indexed | 2024-10-01T06:39:11Z |
| format | Conference Paper |
| id | ntu-10356/103092 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:39:11Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | ntu-10356/1030922020-05-28T07:18:57Z Second order online collaborative filtering Lu, Jing Hoi, Steven Wang, Jialei Zhao, Peilin School of Computer Engineering Asian Conference on Machine Learning, ACML (5th : 2013) DRNTU::Engineering::Computer science and engineering 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 ex- pensive 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-y 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 On- line Collaborative Filtering (CWOCF), which applies the second order online optimization methodology to tackle the online collaborative giltering 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. Published version 2014-12-09T08:17:33Z 2019-12-06T21:05:26Z 2014-12-09T08:17:33Z 2019-12-06T21:05:26Z 2013 2013 Conference Paper Lu, J., Hoi, S., & Wang, J. (2013). Second order online collaborative filtering. Journal of Machine Learning Research, 29, 325-340. https://hdl.handle.net/10356/103092 http://hdl.handle.net/10220/24401 http://jmlr.org/proceedings/papers/v29/Lu13.html en © 2013 The Authors(Journal of Machine Learning Research). This paper was published in Journal of Machine Learning Research and is made available as an electronic reprint (preprint) with permission of The Authors(Journal of Machine Learning Research). The paper can be found at the following official URL: [http://jmlr.org/proceedings/papers/v29/Lu13.html]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Lu, Jing Hoi, Steven Wang, Jialei Zhao, Peilin Second order online collaborative filtering |
| title | Second order online collaborative filtering |
| title_full | Second order online collaborative filtering |
| title_fullStr | Second order online collaborative filtering |
| title_full_unstemmed | Second order online collaborative filtering |
| title_short | Second order online collaborative filtering |
| title_sort | second order online collaborative filtering |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/103092 http://hdl.handle.net/10220/24401 http://jmlr.org/proceedings/papers/v29/Lu13.html |
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