| Reference: | Abowd, G. D., Dey, A. K., Brown, P. J., Davies, N., Smith, M., & Steggles, P. (1999). Towards a better understanding of context and context-awareness. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Adomavicius, G., Mobasher, B., Ricci, F., & Tuzhilin, A. (2011). Context-aware recommender systems. AI Magazine, 32(3), 67–80.
Baltrunas, L., Ludwig, B., & Ricci, F. (2011). Matrix factorization techniques for context aware recommendation. RecSys’11 - Proceedings of the 5th ACM Conference on Recommender Systems.
Bangor, A., Kortum, P., & Miller, J. (2009). Determining what individual SUS scores mean. Journal of Usability Studies.
Basten, F., Ham, J., Midden, C., Gamberini, L., & Spagnolli, A. (2015). Does trigger location matter? The influence of localization and motivation on the persuasiveness of mobile purchase recommendations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9072, pp. 121–132).
Bennett, J., & Lanning, S. (2007). The Netflix Prize. KDD Cup and Workshop.
Chen, J. (2016). A Study on the selection of fast food restaurant by Utar Kampar students using analytic hierarchy process (AHP) [Universiti Tunku Abdul Rahman]. http://eprints.utar.edu.my/2281/
Christakopoulou, K., Radlinski, F., & Hofmann, K. (2016). Towards conversational recommender systems. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’16, 3, 815–824.
Colombo-Mendoza, L. O., Valencia-García, R., Rodríguez-González, A., Alor-Hernández, G., & Samper-Zapater, J. J. (2015). RecomMetz: A context-aware knowledge-based mobile recommender system for movie showtimes. Expert Systems with Applications, 42(3), 1202–1222.
Deshpande, M., & Karypis, G. (2004). Item-based top-N recommendation algorithms. ACM Transactions on Information Systems.
Fogg, B. (2009). A behavior model for persuasive design. ACM International Conference Proceeding Series.
Hermoso, R., Dunkel, J., & Krause, J. (2016). Situation awareness for push-based recommendations in mobile devices. In W. Abramowicz, R. Alt, & B. Franczyk (Eds.), Lecture Notes in Business Information Processing (Vol. 255, pp. 117–129). Springer International Publishing.
Hu, Y., Volinsky, C., & Koren, Y. (2008). Collaborative filtering for implicit feedback datasets. Proceedings - IEEE International Conference on Data Mining, ICDM.
Ikemoto, Y., Asawavetvutt, V., Kuwabara, K., & Huang, H.-H. (2019). Tuning a conversation strategy for interactive recommendations in a chatbot setting. Journal of Information and Telecommunication, 3(2), 180–195.
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.
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. RecSys’11 - Proceedings of the 5th ACM Conference on Recommender Systems.
Konstan, J. A., & Riedl, J. (2012). Recommender systems: From algorithms to user experience. In User Modeling and User-Adapted Interaction.
Lewis, J. R. (1995). IBM computer usability satisfaction questionnaires: Psychometric evaluation and instructions for use. International Journal of Human-Computer Interaction, 7(1), 57–78.
Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., & Rui, Y. (2014). GeoMF: Joint geographical modeling and matrix factorization for point-of-interest recommendation. In KDD ’14 Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 831–840).
Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing.
Loepp, B., Herrmanny, K., & Ziegler, J. (2015). Blended recommending: Integrating interactive information filtering and algorithmic recommender techniques. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 975–984.
Missaoui, S., Kassem, F., Viviani, M., Agostini, A., Faiz, R., & Pasi, G. (2019). LOOKER: a mobile, personalized recommender system in the tourism domain based on social media user-generated content. Personal and Ubiquitous Computing, 23(2), 181–197.
Narducci, F., de Gemmis, M., Lops, P., & Semeraro, G. (2018). Improving the user experience with a conversational recommender system. In AI*IA 2018 -- Advances in Artificial Intelligence: Vol. 11298 LNAI (pp. 528–538).
Ng, D. (2006). Ranking Internet Search Results Based on Number of Mobile Device Visits to Physical Locations Related to the Search Results. Google Patents.
Ning, X., & Karypis, G. (2011). SLIM: Sparse Linear Methods for Top-N Recommender Systems. 2011 IEEE 11th International Conference on Data Mining, 497–506.
Oku, K., Nakajima, S., Miyazaki, J., & Uemura, S. (2006). Context-aware SVM for context-dependent information recommendation. Proceedings - IEEE International Conference on Mobile Data Management, 2006, 5–8.
Pu, P., & Chen, L. (2008). User-involved preference elicitation for product search and recommender systems. AI Magazine, 29(4), 93–103.
Pu, P., Chen, L., & Hu, R. (2012). Evaluating recommender systems from the user’s perspective: Survey of the state of the art. User Modeling and User-Adapted Interaction.
Ramirez-Garcia, X., & García-Valdez, M. (2014). Post-filtering for a restaurant context-aware recommender system. Studies in Computational Intelligence, 547, 695–707.
Ricci, F., & Quang, N. (2006). MobyRek: a conversational recommender system for on-the-move travellers. In Destination recommendation systems: behavioural foundations and applications (pp. 281–294). CABI.
Sun, Y., Yuan, N. J., Wang, Y., Xie, X., McDonald, K., & Zhang, R. (2016). Contextual intent tracking for personal assistants. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-Augu, 273–282.
Trattner, C., Oberegger, A., Eberhard, L., Parra, D., & Marinho, L. (2016). Understanding the impact of weather for POI recommendations. CEUR Workshop Proceedings.
Vakeel, K. A., & Ray, S. (2019). Points of interest recommendations based on check-in motivations. Tourism Analysis.
Villegas, N. M., & Müller, H. A. (2010). Managing dynamic context to optimize smart interactions and services. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
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.
Wang, X., Rosenblum, D., & Wang, Y. (2012). Context-aware mobile music recommendation for daily activities. MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia, 99–108.
Wobbrock, J. O., Findlater, L., Gergle, D., & Higgins, J. J. (2011). The Aligned Rank Transform for nonparametric factorial analyses using only ANOVA procedures. Conference on Human Factors in Computing Systems - Proceedings.
Yang, L., Chen, J., Dell, N., Sobolev, M., Dunne, D., Naaman, M., Wang, Y., Tsangouri, C., & 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.
Yang, L., Hsieh, C.-K., Yang, H., Pollak, J. P., Dell, N., Belongie, S., Cole, C., & Estrin, D. (2017). Yum-Me. ACM Transactions on Information Systems, 36(1), 1–31.
Yuan, Q., Cong, G., Ma, Z., Sun, A., & Magnenat-Thalmann, N. (2013). Time-aware point-of-interest recommendation. SIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval.
Zhang, Y., & Chen, X. (2018). Explainable recommendation: A survey and new perspectives. FATREC 2018 Workshop: Responsible Recommendation.
Zhao, G., Fu, H., Song, R., Sakai, T., Xie, X., & Qian, X. (2019). Why you should listen to this song: Reason generation for explainable recommendation. IEEE International Conference on Data Mining Workshops, ICDMW, 2018-Novem, 1316–1322.
Zheng, Y., & Jose, A. A. (2019). Context-aware recommendations via sequential predictions. Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing - SAC ’19, April, 2525–2528.
Zheng, Y., Mobasher, B., & Burke, R. (2014). CSLIM: Contextual SLIM recommendation algorithms. RecSys 2014 - Proceedings of the 8th ACM Conference on Recommender Systems, 0(1), 301–304. |
