The wisdom of the few: A collaborative filtering approach based on expert opinions from the web
Nearest-neighbor collaborative filtering provides a successful means of generating recommendations for web users. However, this approach suffers from several shortcomings, including data sparsity and noise, the cold-start problem, and scalability. In this work, we present a novel method for recommen...
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sg-smu-ink.sis_research-71062021-09-29T12:39:21Z The wisdom of the few: A collaborative filtering approach based on expert opinions from the web AMATRIAIN, Xavier LATHIA, Neal PUJOL, Josep M. KWAK, Haewoon OLIVER, Nuria. Nearest-neighbor collaborative filtering provides a successful means of generating recommendations for web users. However, this approach suffers from several shortcomings, including data sparsity and noise, the cold-start problem, and scalability. In this work, we present a novel method for recommending items to users based on expert opinions. Our method is a variation of traditional collaborative filtering: rather than applying a nearest neighbor algorithm to the user-rating data, predictions are computed using a set of expert neighbors from an independent dataset, whose opinions are weighted according to their similarity to the user. This method promises to address some of the weaknesses in traditional collaborative filtering, while maintaining comparable accuracy. We validate our approach by predicting a subset of the Netflix data set. We use ratings crawled from a web portal of expert reviews, measuring results both in terms of prediction accuracy and recommendation list precision. Finally, we explore the ability of our method to generate useful recommendations, by reporting the results of a user-study where users prefer the recommendations generated by our approach. 2009-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6103 info:doi/10.1145/1571941.1572033 https://ink.library.smu.edu.sg/context/sis_research/article/7106/viewcontent/1571941.1572033.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Recommender Systems Collaborative Filtering Experts Cosine Similarity Nearest Neighbors Top-N Recommendations Numerical Analysis and Scientific Computing Theory and Algorithms |
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Recommender Systems Collaborative Filtering Experts Cosine Similarity Nearest Neighbors Top-N Recommendations Numerical Analysis and Scientific Computing Theory and Algorithms AMATRIAIN, Xavier LATHIA, Neal PUJOL, Josep M. KWAK, Haewoon OLIVER, Nuria. The wisdom of the few: A collaborative filtering approach based on expert opinions from the web |
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Nearest-neighbor collaborative filtering provides a successful means of generating recommendations for web users. However, this approach suffers from several shortcomings, including data sparsity and noise, the cold-start problem, and scalability. In this work, we present a novel method for recommending items to users based on expert opinions. Our method is a variation of traditional collaborative filtering: rather than applying a nearest neighbor algorithm to the user-rating data, predictions are computed using a set of expert neighbors from an independent dataset, whose opinions are weighted according to their similarity to the user. This method promises to address some of the weaknesses in traditional collaborative filtering, while maintaining comparable accuracy. We validate our approach by predicting a subset of the Netflix data set. We use ratings crawled from a web portal of expert reviews, measuring results both in terms of prediction accuracy and recommendation list precision. Finally, we explore the ability of our method to generate useful recommendations, by reporting the results of a user-study where users prefer the recommendations generated by our approach. |
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text |
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AMATRIAIN, Xavier LATHIA, Neal PUJOL, Josep M. KWAK, Haewoon OLIVER, Nuria. |
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AMATRIAIN, Xavier LATHIA, Neal PUJOL, Josep M. KWAK, Haewoon OLIVER, Nuria. |
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AMATRIAIN, Xavier |
title |
The wisdom of the few: A collaborative filtering approach based on expert opinions from the web |
title_short |
The wisdom of the few: A collaborative filtering approach based on expert opinions from the web |
title_full |
The wisdom of the few: A collaborative filtering approach based on expert opinions from the web |
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The wisdom of the few: A collaborative filtering approach based on expert opinions from the web |
title_full_unstemmed |
The wisdom of the few: A collaborative filtering approach based on expert opinions from the web |
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wisdom of the few: a collaborative filtering approach based on expert opinions from the web |
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Institutional Knowledge at Singapore Management University |
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2009 |
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https://ink.library.smu.edu.sg/sis_research/6103 https://ink.library.smu.edu.sg/context/sis_research/article/7106/viewcontent/1571941.1572033.pdf |
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