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PURS: Personalized Unexpected Recommender System for Improving User Satisfaction

, , , , and . Fourteenth ACM Conference on Recommender Systems, page 279-288. ACM, (2020)

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On Characterization and Discovery of Minimal Unexpected Patterns in Rule Discovery., and . IEEE Trans. Knowl. Data Eng., 18 (2): 202-216 (2006)On Completeness of Historical Relational Query Languages., , and . ACM Trans. Database Syst., 19 (1): 64-116 (1994)Querying multiple sets of discovered rules., and . KDD, page 52-60. ACM, (2002)A Belief-Driven Method for Discovering Unexpected Patterns., and . KDD, page 94-100. AAAI Press, (1998)Context-Aware Recommender Systems., and . Recommender Systems Handbook, Springer, (2011)Latent Unexpected and Useful Recommendation., and . CoRR, (2019)Discovering Unexpected Patterns in Temporal Data Using Temporal Logic., and . Temporal Databases, Dagstuhl, volume 1399 of Lecture Notes in Computer Science, page 281-309. Springer, (1997)Recommendation strategies in personalization applications, , and . Information & Management, 56 (6): 103143 (2019)Validation Sequence Optimization: A Theoretical Approach., and . INFORMS J. Comput., 19 (2): 185-200 (2007)On Temporal Grouping, , , and . Temporal Databases, page 194-213. Springer, (1995)