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)|
|Title of host publication||RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||8|
|State||Published - Sep 7 2016|
|Event||10th ACM Conference on Recommender Systems, RecSys 2016 - Boston, United States|
Duration: Sep 15 2016 → Sep 19 2016
|Name||RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems|
|Other||10th ACM Conference on Recommender Systems, RecSys 2016|
|Period||9/15/16 → 9/19/16|
Bibliographical noteFunding Information:
This work was supported in part by NSF (OCI-1048018, IIS-1247632, IIP-1414153, IIS-1447788), Army Research Office (W911NF-14-1-0316), Intel Software and Services Group, and the Digital Technology Center at the University of Minnesota. Access to research and computing facilities was provided by the Digital Technology Center and the Minnesota Supercomputing Institute.
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