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Discovering What You're Known For: A Contextual Poisson Factorization Approach.

, , and . RecSys, page 253-260. ACM, (2016)

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Large Language Models as Data Augmenters for Cold-Start Item Recommendation., , , , and . CoRR, (2024)Community-based geospatial tag estimation., , , and . ASONAM, page 279-286. IEEE Computer Society, (2016)Learning Geo-Social User Topical Profiles with Bayesian Hierarchical User Factorization., , and . SIGIR, page 205-214. ACM, (2018)Long-Term Value of Exploration: Measurements, Findings and Algorithms., , , , , , , , , and 5 other author(s). WSDM, page 636-644. ACM, (2024)Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation., , , , , , , , , and 3 other author(s). KDD, page 5082-5091. ACM, (2023)Quality-aware neural complementary item recommendation., , , and . RecSys, page 77-85. ACM, (2018)Neural Personalized Ranking for Image Recommendation., , and . WSDM, page 423-431. ACM, (2018)TAPER: A Contextual Tensor-Based Approach for Personalized Expert Recommendation, , and . Proceedings of the 10th ACM Conference on Recommender Systems, page 261--268. New York, NY, USA, ACM, (2016)Location-Sensitive User Profiling Using Crowdsourced Labels., , and . AAAI, page 386-393. AAAI Press, (2018)Discovering What You're Known For: A Contextual Poisson Factorization Approach., , and . RecSys, page 253-260. ACM, (2016)