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Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings.

, , and . ECIR (2), volume 12036 of Lecture Notes in Computer Science, page 215-222. Springer, (2020)

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Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings., , and . ECIR (2), volume 12036 of Lecture Notes in Computer Science, page 215-222. Springer, (2020)The wisdom of the rankers: a cost-effective method for building pooled test collections without participant systems., , and . SAC, page 672-680. ACM, (2021)BDI-Sen: A Sentence Dataset for Clinical Symptoms of Depression., , , and . SIGIR, page 2996-3006. ACM, (2023)Beaver: Efficiently Building Test Collections for Novel Tasks., , and . CIRCLE, volume 2621 of CEUR Workshop Proceedings, CEUR-WS.org, (2020)Computing Neighbourhoods with Language Models in a Collaborative Filtering Scenario., , and . IIR, volume 1653 of CEUR Workshop Proceedings, CEUR-WS.org, (2016)LiMe: linear methods for pseudo-relevance feedback., , and . SAC, page 678-687. ACM, (2018)Shallow Neural Models for Top-N Recommendation., , , and . ECAI, volume 325 of Frontiers in Artificial Intelligence and Applications, page 2911-2912. IOS Press, (2020)PRIN: A Probabilistic Recommender with Item Priors and Neural Models., , , and . ECIR (1), volume 11437 of Lecture Notes in Computer Science, page 133-147. Springer, (2019)Query Expansion as a Matrix Factorization Problem: Extended Abstract., , and . CERI, page 3:1-3:4. ACM, (2018)Testing the tests: simulation of rankings to compare statistical significance tests in information retrieval evaluation., , and . SAC, page 655-664. ACM, (2021)