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A Review of the Role of Sensors in Mobile Context-Aware Recommendation Systems.

, , , and . Int. J. Distributed Sens. Networks, (2015)

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A Review of the Role of Sensors in Mobile Context-Aware Recommendation Systems., , , and . Int. J. Distributed Sens. Networks, (2015)Pull-based recommendations in mobile environments., and . Comput. Stand. Interfaces, (2016)An Experimental Evaluation of Content-based Recommendation Systems: Can Linked Data and BERT Help?, , , , and . AICCSA, page 1-8. IEEE, (2020)Push-Based Recommendations in Mobile Computing Using a Multi-Layer Contextual Approach., , , and . MoMM, page 149-158. ACM, (2015)JCLAL: A Java Framework for Active Learning., , , , and . J. Mach. Learn. Res., (2016)DataGenCARS: A generator of synthetic data for the evaluation of context-aware recommendation systems., , , and . Pervasive Mob. Comput., (2017)Location-Aware Recommendation Systems: Where We Are and Where We Recommend to Go., , , and . LocalRec@RecSys, volume 1405 of CEUR Workshop Proceedings, page 1-8. CEUR-WS.org, (2015)Towards a Structured Representation of Results in an Information Retrieval System for Public Examination Calls., , , and . CERI, page 4:1-4:8. ACM, (2018)Towards a Context-Aware Mobile Recommendation Architecture., and . MobiWIS, volume 8640 of Lecture Notes in Computer Science, page 56-70. Springer, (2014)MotorIA: Automatic E-Learning Course Generation System., , , , , and . ICCTA, page 92-96. ACM, (2020)