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Using Implicit Preference Relations to Improve Recommender Systems.

, and . J. Data Semant., 6 (1): 15-30 (2017)

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Comparing Interactive Retrieval Approaches at the Lifelog Search Challenge 2021., , , , , , , , , and 9 other author(s). IEEE Access, (2023)Interpreting Web Shop User's Behavioral Patterns as Fictitious Explicit Rating for Preference Learning., and . RuleML, volume 8620 of Lecture Notes in Computer Science, page 251-265. Springer, (2014)LODBookRec: Linked Open Data for Books Recommendation., , and . WIMS, page 12:1-12:6. ACM, (2019)Using Implicit Preference Relations to Improve Recommender Systems., and . J. Data Semant., 6 (1): 15-30 (2017)Off-line vs. On-line Evaluation of Recommender Systems in Small E-commerce., and . HT, page 291-300. ACM, (2020)Lyrics or Audio for Music Recommendation?, and . WIMS, page 190-194. ACM, (2020)Linking Content Information with Bayesian Personalized Ranking via Multiple Content Alignments.. HT, page 175-183. ACM, (2017)Towards Results-level Proportionality for Multi-objective Recommender Systems., and . SIGIR, page 1963-1968. ACM, (2022)LiGAN: Recommending Artificial Fillers for Police Photo Lineups (Short paper)., and . KaRS/ComplexRec@RecSys, volume 2960 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)SpotifyExplained: User-centric Mobile Application for Music Exploration., and . UMAP (Adjunct Publication), page 92-95. ACM, (2023)