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    题名: 探討偏好啟發對微時刻推薦的影響：互動式微時刻推薦系統
    The influence of preference elicitation to micro-moment recommendations: An interactive MMRS
    作者: 王詩堯
    Wang, Shih-Yao
    贡献者: 林怡伶
    Lin, Yi-Ling
    王詩堯
    Wang, Shih-Yao
    关键词: 微時刻
    推薦系統
    意圖
    偏好啟發
    互動式設計
    聊天機器人
    Micro-moments
    Recommendation system
    Intention
    Preference elicitation
    Interactive design
    Chatbot
    日期: 2020
    上传时间: 2020-09-02 11:48:05 (UTC+8)
    摘要: 先前的研究指出推薦系統不僅應根據用戶的行為數據或受歡迎的項目進行推薦，還應符合用戶的偏好。部分推薦系統設計在使用者首次加入時調查其長期偏好。然而當在微時刻情境下，必須在限時內做出決策的壓力會導致注意力的限縮，最終會因此做出跟平時不同的選擇。這使我們相信作為決策輔助的推薦系統也應考慮短期意圖，並透過與使用者的互動來捕捉。這項研究進行了為期三週的使用者研究，以根據熱門程度、長期偏好和短期意圖來比較推薦的效果。本實驗設計了三個階段，包括進入前調查、使用聊天機器人、實驗後調查和訪談。總共招募了120名大學生，並將他們平均分配到四組之中。實驗的主要任務為透過與聊天機器人進行互動，在微時刻的各種情境下選擇一間餐廳。實驗結果顯示，MIX組（同時考慮長期偏好和短期意圖）會話的成功率比LTP組（僅捕獲長期偏好）高21.8%，並且利用更少的動作完成一輪推薦流程。另外，MIX組的所選項目在推薦列表上的平均排名最低，且推薦的點擊率最高。結果證明，這是四組中能支持使用者以較少的努力做出有效決策的最佳設計，而且該設計也是最適合支持微時刻的情境。透過證明MIX組優於LTP組，證明了在微時刻捕捉短期意圖的重要性。
    Previous studies pointed out that recommendation systems should not only recommend by user`s behavioral data or popular items but should conform to user preferences. Some recommendation systems investigate users’ long-term preferences when they first join. However, in micro-moments, giving limited available time to make decisions leads to a narrowing of attentional focus, eventually comes up with different choices. It convinces us that short-term intentions should also be taken into consideration and obtained through interactions with users. This research conducts a three-week user study to compare the effects of recommendations based on popularity, long-term preferences, and short-term intentions. Three phases including onboarding survey, chatbot use, post-experiment survey and interview were designed in this experiment. A total of 120 university students were recruited and assigned to one out of four groups. The main tasks focused on interacting with the chatbot then making choices of restaurants under various situations of micro-moments. The result shows that the sessions of the MIX group (considering both long-term preferences and short-term intentions) have a more 21.8% success ratio than the LTP group ones (capturing only the long-term preferences) and spent fewer actions in the recommendation processes. In addition, the mean of the MIX group` s selected position is the lowest, and the click-through of the MIX group is the highest. The results proved that it is the best design among four groups supporting users to make effective decisions with fewer efforts, moreover, this design is most suitable for the situation under micro-moments. Comparing the design of the LTP group, it also shows the importance of capturing short-term intentions at micro-moments.
    參考文獻: 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.
    描述: 碩士
    國立政治大學
    資訊管理學系
    107356034
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0107356034
    数据类型: thesis
    DOI: 10.6814/NCCU202001650
    显示于类别:[資訊管理學系] 學位論文

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