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題名 微時刻推薦系統：以餐廳推薦為例
A Micro-moments recommender system: A restaurant recommendation study作者 余嘉翔
Yu, Chia-Hsiang貢獻者 林怡伶
Lin, Yi-Ling
余嘉翔
Yu, Chia-Hsiang關鍵詞 餐廳推薦
聊天機器人
微時刻
互動式推薦
restaurant recommendation
chatbot
micro-moments
interactive recommendation日期 2020 上傳時間 2-Sep-2020 11:45:58 (UTC+8) 摘要 隨著智慧型手機的發展與普及，愈來愈多使用者頃向使用智慧型手機來獲取最即時的資訊。這種稱為「微時刻（Micro-Moments）」的使用者行為，通常伴隨著鮮明的使用者偏好、決策條件以及必須要在極短的時間內做出決定。使用者每次拿起手機的平均使用時間約為5分鐘，換句話說，系統必須要很快且精確瞭解使用者的需求，並快速提供合適的資訊。本研究透過聊天機器人建構一個以滿足使用者微時刻需求的互動式情境感知推薦系統，並以推薦餐廳為主題，探討如何獲取使用者的偏好以及當下的情境與意圖，並與推薦演算法結合，產生推薦給使用者。研究結果指出，本研究提出的微時刻推薦系統設計可以有效的獲得使用者偏好與意圖以及有考慮使用者當下意圖的演算法可以幫助使用者更快的找到最合適的餐廳並且是符合使用者的偏好。
More and more users tend to use their smartphones to support their micro-moment decisions. Micro-moments can be regards as an intent-rich moment when preferences and decision priorities are expressed clearly. Furthermore, the average time users spent on one moment is less than 5 minutes and they usually need to make a decision in a short time. The traditional information retrieval might not able to meet users’ need. Hence, the context-aware recommender is one of key solution to meet users’ need. Some studies have point out an interactive recommender design can better elicit user preference and contextual information. The emergence of chatbot which mimics a conversation with a real person has been regarded as an ideal conversational agent to build recommender systems. In this study, we proposed a micro-moments recommender system aims to recommend restaurants based on the combination of user’s long-term and short-term intention and is built on a chatbot. The result shows that the proposed micro-moments recommender system is able to let user find a restaurant at moment with less search effort and higher efficiency and help the user bring out their inner intention to get the best choice of restaurants, which is in line with his/her interest.參考文獻 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. 描述 碩士
國立政治大學
資訊管理學系
107356015資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107356015 資料類型 thesis dc.contributor.advisor 林怡伶 zh_TW dc.contributor.advisor Lin, Yi-Ling en_US dc.contributor.author (Authors) 余嘉翔 zh_TW dc.contributor.author (Authors) Yu, Chia-Hsiang en_US dc.creator (作者) 余嘉翔 zh_TW dc.creator (作者) Yu, Chia-Hsiang en_US dc.date (日期) 2020 en_US dc.date.accessioned 2-Sep-2020 11:45:58 (UTC+8) - dc.date.available 2-Sep-2020 11:45:58 (UTC+8) - dc.date.issued (上傳時間) 2-Sep-2020 11:45:58 (UTC+8) - dc.identifier (Other Identifiers) G0107356015 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131492 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 107356015 zh_TW dc.description.abstract (摘要) 隨著智慧型手機的發展與普及，愈來愈多使用者頃向使用智慧型手機來獲取最即時的資訊。這種稱為「微時刻（Micro-Moments）」的使用者行為，通常伴隨著鮮明的使用者偏好、決策條件以及必須要在極短的時間內做出決定。使用者每次拿起手機的平均使用時間約為5分鐘，換句話說，系統必須要很快且精確瞭解使用者的需求，並快速提供合適的資訊。本研究透過聊天機器人建構一個以滿足使用者微時刻需求的互動式情境感知推薦系統，並以推薦餐廳為主題，探討如何獲取使用者的偏好以及當下的情境與意圖，並與推薦演算法結合，產生推薦給使用者。研究結果指出，本研究提出的微時刻推薦系統設計可以有效的獲得使用者偏好與意圖以及有考慮使用者當下意圖的演算法可以幫助使用者更快的找到最合適的餐廳並且是符合使用者的偏好。 zh_TW dc.description.abstract (摘要) More and more users tend to use their smartphones to support their micro-moment decisions. Micro-moments can be regards as an intent-rich moment when preferences and decision priorities are expressed clearly. Furthermore, the average time users spent on one moment is less than 5 minutes and they usually need to make a decision in a short time. The traditional information retrieval might not able to meet users’ need. Hence, the context-aware recommender is one of key solution to meet users’ need. Some studies have point out an interactive recommender design can better elicit user preference and contextual information. The emergence of chatbot which mimics a conversation with a real person has been regarded as an ideal conversational agent to build recommender systems. In this study, we proposed a micro-moments recommender system aims to recommend restaurants based on the combination of user’s long-term and short-term intention and is built on a chatbot. The result shows that the proposed micro-moments recommender system is able to let user find a restaurant at moment with less search effort and higher efficiency and help the user bring out their inner intention to get the best choice of restaurants, which is in line with his/her interest. en_US dc.description.tableofcontents Chapter 1 Introduction 11.1 Background and Motivation 11.2 Research Goal 41.3 Content Organization 5Chapter 2 Literature Review 72.1 Micro-moments 72.2 Context-aware Recommendation 82.3 Interactive Recommender System 10Chapter 3 The Proposed Framework 113.1 Long-term Preference 113.2 User Intention 133.3 Restaurant Dataset 133.4 Micro-moments Recommender 153.4.1. Context-aware recommendation 153.4.2. Spatial-temporal filtering 183.5 Chatbot 18Chapter 4 Experimental Design 214.1 Experimental Setup 214.2 Experiment Process 224.3 Evaluation 224.3.1. Performance metrics & algorithms for comparison 224.3.2. System logs 234.3.3. System usability test 244.4 Factors considering in the micro-moments 24Chapter 5 Analysis and Results 255.1 Algorithms Performance 265.1.1. MM-based approaches evaluation 265.1.2. Context-aware recommendation evaluation 275.2 Log-based Analysis 315.2.1. General Usage Patterns 315.2.2. Patterns of Restaurant Exploration 345.2.3. Exploration and User’s Interest 365.3 System Usability and User Satisfaction 37Chapter 6 Discussion 396.1 Search Efforts and Efficiency 396.2 Intention Sharpening and Restaurant Exploration 406.3 User’s Satisfaction and Chatbot Design 42Chapter 7 Conclusion 43Appendix 1 – Chatbot Questions 45Appendix 2 – CSUQ Questionnaire 45Reference 46 zh_TW dc.format.extent 9015886 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107356015 en_US dc.subject (關鍵詞) 餐廳推薦 zh_TW dc.subject (關鍵詞) 聊天機器人 zh_TW dc.subject (關鍵詞) 微時刻 zh_TW dc.subject (關鍵詞) 互動式推薦 zh_TW dc.subject (關鍵詞) restaurant recommendation en_US dc.subject (關鍵詞) chatbot en_US dc.subject (關鍵詞) micro-moments en_US dc.subject (關鍵詞) interactive recommendation en_US dc.title (題名) 微時刻推薦系統：以餐廳推薦為例 zh_TW dc.title (題名) A Micro-moments recommender system: A restaurant recommendation study en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 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. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202001509 en_US
