|
Amatriain, X., Pujol, J. M., & Oliver, N. (2009). I like it... I like it not: Evaluating user ratings noise in recommender systems. In International Conference on User Modeling, Adaptation, and Personalization (pp. 247–258). Springer. Ariely, D. (2016). Time pressure: Behavioral science considerations for mobile marketing. Baltrunas, L., Kaminskas, M., Ludwig, B., Moling, O., Ricci, F., Aydin, A., & Schwaiger, R. (2011). Incarmusic: Context-aware music recommendations in a car. In International Conference on Electronic Commerce and Web Technologies, 89–100. Chen, J. W. (2016). A study on the selection of fast food restaurant by Utar Kampar students using analytic hierarchy process (AHP). Doctoral Dissertation, UTAR. Chen, L., & Pu, P. (2007a). Hybrid critiquing-based recommender systems, 22–31. Chen, L., & Pu, P. (2007b). Preference-based organization interfaces: Aiding user critiques in recommender systems. In International Conference on User Modeling (pp. 77–86). Springer. Chen, L., & Pu, P. (2012). Critiquing-based recommenders: Survey and emerging trends. User Modeling and User-Adapted Interaction, 22(1–2), 125–150. Chernev, A. (2003). When more is less and less is more: The role of ideal point a vailability and assortment in consumer choice. Journal of Consumer Research, 30(2), 170–183. Christakopoulou, K., Radlinski, F., & Hofmann, K. (2016). Towards conversational recommender systems. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-Augu(3), 815–824. Coombs, L. C. (1974). The measurement of family size preferences and subsequent fertility. Demography, 11(4), 587–611. Costa, H., Furtado, B., Pires, D., Macedo, L., & Cardoso, A. (2012). Context and intention-awareness in POIs recommender systems. CEUR Workshop Proceedings, 889, 1–5. Dali Betzalel, N., Shapira, B., & Rokach, L. (2015). “Please, not now!” A model for timing recommendations. In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 297–300). DelCarmen Rodríguez-Hernández, M., & Ilarri, S. (2014). Towards a context-aware mobile recommendation architecture. International Conference on Mobile Web and Information Systems, 56–70. Ekstrand, M. D., & Willemsen, M. C. (2016). Behaviorism is not enough: Better recommendations through listening to users. RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems, 221–224. Esmeli, R., Bader-El-Den, M., & Mohasseb, A. (2019). Context and short term user intention aware hybrid session based recommendation system. IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings, 1–6. Ghose, A., Han, S. P., & Xu, K. (2013). Mobile commerce in the new tablet economy. International Conference on Information Systems (ICIS 2013): Reshaping Society Through Information Systems Design, 3, 2591–2608. Han, J., & Yamana, H. (2017). A survey on recommendation methods beyond accuracy. IEICE TRANSACTIONS on Information and Systems, 100(12), 2931–2944. Hendrianto, A. (2017). Analysis of students preferences in choosing restaurant around campus area. Inzunza, S., Juárez-Ramírez, R., Jiménez, S., & Licea, G. (2018). GUMCARS: General user model for context-aware recommender systems, 37, 1149–1183. Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating: Can one desire too much of a good thing? Journal of Personality and Social Psychology, 79(6), 995–1006. Jannach, D., Resnick, P., Tuzhilin, A., & Zanker, M. (2016). Recommender systems-beyond matrix completion. Communications of the ACM, 59(11), 94–102. Jin, Y., Cai, W., Chen, L., Htun, N. N., & Verbert, K. (2019). MusicBot: Evaluating critiquing-based music recommenders with conversational interaction. International Conference on Information and Knowledge Management, Proceedings, 951–960. Jugovac, M., Jannach, D., & Dortmund, T. (2017). Interacting with recommenders—overview and research. ACM Transactions on Interactive Intelligent Systems (TiiS), 7(3), 10. Kaminskas, M., & Bridge, D. (2016). Diversity, serendipity, novelty, and coverage: A survey and empirical analysis of beyond-Accuracy objectives in recommender systems. ACM Transactions on Interactive Intelligent Systems, 7(1), 1–42. Kilinc, C. C., Semiz, M., Katircioglu, E., & Unusan, Ç. (2013). Choosing restaurant for lunch in campus area by the compromise decision via AHP. International Journal of Economic Perspectives, 7(2). Knijnenburg, B. P., Reijmer, N. J. M., & Willemsen, M. C. (2011). Each to his own: how different users call for different interaction methods in recommender systems. In Proceedings of the fifth ACM conference on Recommender systems (pp. 141–148). Knijnenburg, B. P., Sivakumar, S., & Wilkinson, D. (2016). Recommender systems for self-actualization. RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems, 11–14. Lai, J. Y., Debbarma, S., & Ulhas, K. R. (2012). An empirical study of consumer switching behaviour towards mobile shopping: A Push-Pull-Mooring model. International Journal of Mobile Communications, 10(4), 386–404. Levene, H. (1960). Contributions to probability and statistics. Essays in Honor of Harold Hotelling, 278–292. Lo, C. C., Kuo, T. H., Kung, H. Y., Kao, H. T., Chen, C. H., Wu, C. I., & Cheng, D. Y. (2011). Mobile merchandise evaluation service using novel information retrieval and image recognition technology. Computer Communications, 34(2), 120–128. Loepp, B., Hussein, T., & Ziegler, J. (2014). Choice-based preference elicitation for collaborative filtering recommender systems. Conference on Human Factors in Computing Systems - Proceedings, 3085–3094. Maneth, S., & Poulovassilis, A. (2017). A framework of mobile context-aware. Computer Journal, 60(3), 285–286. Min, H., & Min, H. (2013). Cross-cultural competitive benchmarking of fast-food restaurant services. Benchmarking, 20(2), 212–232. Panniello, U., Tuzhilin, A., Gorgoglione, M., Palmisano, C., & Pedone, A. (2009). Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems. RecSys’09 - Proceedings of the 3rd ACM Conference on Recommender Systems, 265–268. Park, M. H., Park, H. S., & Cho, S. B. (2008). Restaurant recommendation for group of people in mobile environments using probabilistic multi-criteria decision making. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5068 LNCS, 114–122. Patrick Rau, P. L., Zhou, J., Chen, D., & Lu, T. P. (2014). The influence of repetition and time pressure on effectiveness of mobile advertising messages. Telematics and Informatics, 31(3), 463–476. Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker. Cambridge university press. Piron, F. (1991). Defining impulse purchasing. ACR North American Advances. Ramnani, R. R., Sengupta, S., Ravilla, T. R., & Patil, S. G. (2018). Smart entertainment - A critiquing based dialog system for eliciting user preferences and making recommendations. International Conference on Applications of Natural Language to Information Systems (Vol. June). Saaty, T. L. (2008). Relative measurement and its generalization in decision making why pairwise comparisons are central in mathematics for the measurement of intangible factors the analytic hierarchy/network process. Revista de La Real Academia de Ciencias Exactas, Fisicas y Naturales - Serie A: Matematicas, 102(2), 251–318. Shimazu, H. (2001). ExpertClerk: Navigating shoppers’ buying process with the combination of asking and proposing. In IJCAI’01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2 (pp. 1443–1448). Simran, S., Pande, A., & Desai, P. (2019). Preference-search based recommendation system for accommodation facilitator : A Review. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 5(2), 951–956. Thompson, C. A., Göker, M. H., & Langley, P. (2004). A personalized system for conversational recommendations. Journal of Artificial Intelligence Research, 21,393–428. Van derHeijden, H. (2006). Mobile decision support for in-store purchase decisions. Decision Support Systems, 42(2), 656–663. Villegas, N. M., Sánchez, C., Díaz-Cely, J., & Tamura, G. (2018). Characterizing context-aware recommender systems: A systematic literature review. Knowledge-Based Systems, 140, 173–200. Vuckovac, D., Wamsler, J., Ilic, A., & Natter, M. (2016). Getting the timing right : Leveraging category inter-purchase times to improve recommender systems. Proceedings of the 10th ACM Conference on Recommender Systems, 277–280. Wang, J., & Zhang, Y. (2013). Opportunity model for e-commerce recommendation: right product; right time. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, 303–312. Yang, L., Chen, J., Dell, N., Sobolev, M., Dunne, D., Naaman, M., …Estrin, D. (2019). How intention informed recommendations modulate choices: A field study of spoken word content. The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, 2169–2180. Yu, C.-H. (2020). A Micro-moments recommender system: A restaurant recommendation study. Zhang, S., Tay, Y., Yao, L., & Sun, A. (2018). Next item recommendation with self-attention. ArXiv Preprint ArXiv:1808.06414. Zhao, X., Zhang, W., & Wang, J. (2013). Interactive collaborative filtering. International Conference on Information and Knowledge Management, Proceedings, 1411–1420. |