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Modelling User Preferences from Implicit Preference Indicators via Compensational Aggregations., and . EC-Web, volume 188 of Lecture Notes in Business Information Processing, page 138-145. Springer, (2014)Using Implicit Preference Relations to Improve Recommender Systems, and . Journal on Data Semantics, 6 (1): 15--30 (2017)Hybrid Recommending Exploiting Multiple DBPedia Language Editions., and . SemWebEval@ESWC, volume 475 of Communications in Computer and Information Science, page 144-149. Springer, (2014)User Feedback and Preferences Mining.. UMAP, volume 7379 of Lecture Notes in Computer Science, page 382-386. Springer, (2012)Testing and Evaluating Software in a Social Network Creating Baseline Knowledge., , , , , , , and . EJC, volume 251 of Frontiers in Artificial Intelligence and Applications, page 127-141. IOS Press, (2012)Towards web semantization and user understanding., , , , , and . EJC, volume 251 of Frontiers in Artificial Intelligence and Applications, page 63-81. IOS Press, (2012)Rank-sensitive proportional aggregations in dynamic recommendation scenarios., , and . User Model. User Adapt. Interact., 32 (4): 685-746 (2022)Biased k-NN Similarity Content Based Prediction of Movie Tweets Popularity., and . DATESO, volume 1343 of CEUR Workshop Proceedings, page 101-110. CEUR-WS.org, (2015)Advanced Behavioral Analyses Using Inferred Social Networks: A Vision., , , , and . DEXA Workshops, volume 1062 of Communications in Computer and Information Science, page 210-219. Springer, (2019)Personalized Recommendations in Police Photo Lineup Assembling Task., and . ITAT, volume 2203 of CEUR Workshop Proceedings, page 157-160. CEUR-WS.org, (2018)