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Effectively identifying users' research interests for scholarly reference management and discovery.

, , , , , , and . SWM@WSDM, page 17-24. ACM, (2017)

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Term-by-Term Query Auto-Completion for Mobile Search., , and . WSDM, page 143-152. ACM, (2016)Scalable Hyperbolic Recommender Systems., , , , , and . CoRR, (2019)Novelty and diversity enhancement and evaluation in recommender systems and information retrieval.. SIGIR, page 1281. ACM, (2014)Building recommender systems for scholarly information., , , , , , and . SWM@WSDM, page 25-32. ACM, (2017)Academic Recommendations: The Mendeley Case., , , , and . Collaborative Recommendations, WorldScientific, (2018)Product Characterisation towards Personalisation: Learning Attributes from Unstructured Data to Recommend Fashion Products., , and . KDD, page 80-89. ACM, (2018)Mendeley: Recommendations for Researchers., , and . RecSys, page 365. ACM, (2016)Effectively identifying users' research interests for scholarly reference management and discovery., , , , , , and . SWM@WSDM, page 17-24. ACM, (2017)Novelty and Diversity in Recommender Systems., , and . Recommender Systems Handbook, Springer US, (2022)Learning Embeddings for Product Size Recommendations., , , , and . eCOM@SIGIR, volume 2410 of CEUR Workshop Proceedings, CEUR-WS.org, (2019)