Incorporating Aggregate Diversity in Recommender Systems Using Scalable Optimization Approaches
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DOI: 10.1287/ijoc.2016.0741
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References listed on IDEAS
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Cited by:
- Lawrence Bunnell & Kweku-Muata Osei-Bryson & Victoria Y. Yoon, 0. "RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers," Information Systems Frontiers, Springer, vol. 0, pages 1-42.
- Xuan Bi & Gediminas Adomavicius & William Li & Annie Qu, 2022. "Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1644-1660, May.
- Lawrence Bunnell & Kweku-Muata Osei-Bryson & Victoria Y. Yoon, 2020. "RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers," Information Systems Frontiers, Springer, vol. 22(6), pages 1377-1418, December.
- Ethem Çanakoğlu & İbrahim Muter & Tevfik Aytekin, 2021. "Integrating Individual and Aggregate Diversity in Top- N Recommendation," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 300-318, January.
- Qian Wang & Jijun Yu & Weiwei Deng, 2019. "An adjustable re-ranking approach for improving the individual and aggregate diversities of product recommendations," Electronic Commerce Research, Springer, vol. 19(1), pages 59-79, March.
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Keywords
recommender systems; multiobjective optimization; integer programming; column generation;All these keywords.
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