Abstract
Item-based approaches based on SLIM (Sparse LInear Methods) have demonstrated very good performance for top-N recommendation; however they only estimate a single model for all the users. This work is based on the intuition that not all users behave in the same way - instead there exist subsets of like-minded users. By using different item-item models for these user subsets, we can capture differences in their preferences and this can lead to improved performance for top-N recommendations. In this work, we extend SLIM by combining global and local SLIM models. We present a method that computes the prediction scores as a user-specific combination of the predictions derived by a global and local itemitem models. We present an approach in which the global model, the local models, their user-specific combination, and the assignment of users to the local models are jointly optimized to improve the top-N recommendation performance. Our experiments show that the proposed method improves upon the standard SLIM model and outperforms competing top-N recommendation approaches.
Original language | English (US) |
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Title of host publication | RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems |
Publisher | Association for Computing Machinery, Inc |
Pages | 67-74 |
Number of pages | 8 |
ISBN (Electronic) | 9781450340359 |
DOIs | |
State | Published - Sep 7 2016 |
Event | 10th ACM Conference on Recommender Systems, RecSys 2016 - Boston, United States Duration: Sep 15 2016 → Sep 19 2016 |
Publication series
Name | RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems |
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Other
Other | 10th ACM Conference on Recommender Systems, RecSys 2016 |
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Country/Territory | United States |
City | Boston |
Period | 9/15/16 → 9/19/16 |
Bibliographical note
Publisher Copyright:© 2016 ACM.