Recommendation of Activity Sequences during Distributed Events by Diana Nurbakova Supervisors : Prof. Sylvie Calabretto, Prof. Jérôme Gensel, Dr. Léa Laporte Examiners : Prof. Patrice Bellot (reporter) Prof. Anne Boyer (reporter) Prof. Josiane Mothe Dr. Ilya Markov 13 December 2018
Motivation 2
Motivation 3
Motivation 4
Motivation 5
Motivation Very high density of events Average number of simultaneous events = 37 Max number of simultaneous events = 112 ▼ Need for Recommender System 6 Scenario 1: Overlapping events at Comic-Con w.r.t. 15 min long timeslots
Motivation Scenario 2: Program of activities on board of Disney Fantasy cruise 7
Motivation Desired Sequence of Activities Scenario 2: Program of activities on board of Disney Fantasy cruise 8
Motivation Desired Sequence of Activities Use of a Recommender System Scenario 2: Program of activities on board of Disney Fantasy cruise 9
Objectives & Research Questions Provide an integrated support for users to create a personalised itinerary of activities, in order to facilitate their decision making process during distributed events 10
Objectives & Research Questions Provide an integrated support for users to create a personalised itinerary of activities, in order to facilitate their decision making process during distributed events Conceptual Direction: Practical Direction: Analyse the field of recommendation of sequences of spatial items for a Provide an integrated framework to better understanding , conceptual address the defined problem definition and modelling of the field 11
Defense Itinerary Fantasy_db & DEvIR : datasets for RSSI You are here! and evaluation of Objectives & 5 min ANASTASIA Research Questions 10 min 13 min ANASTASIA : approach for solving RSSI RSSI : 12 min 3 min Recommendation of Conclusions & Sequences of Spatial Perspectives Items 12
Fantasy_db & DEvIR : datasets for RSSI and evaluation of Objectives & 5 min ANASTASIA Research Questions 10 min 13 min You are here! ANASTASIA : approach for solving RSSI RSSI : Recommendation 12 min 3 min of Sequences of Spatial Conclusions & Items Perspectives Recommendation of Sequences of Spatial Items (RSSI): From Recommender Systems to RSSI 13
Recommender Systems User Interaction log time Ordered list of top- 𝑙 item_id user_id items value ... Items 14 G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of- the-art and possible extensions. IEEE Trans. on Knowl. And Data Eng. , 17(6):734 – 749, June 2005
Recommender Systems User Interaction log Users time ? Ordered list Items of top- 𝑙 item_id user_id items value ... Items 15 G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of- the-art and possible extensions. IEEE Trans. on Knowl. And Data Eng. , 17(6):734 – 749, June 2005
Recommender Systems User Interaction log Users time ? Ordered list Items of top- 𝑙 item_id user_id items value ... Items Matrix completion problem (find missing values) 16 G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of- the-art and possible extensions. IEEE Trans. on Knowl. And Data Eng. , 17(6):734 – 749, June 2005
Recommender Systems: Spatial Items - Activity Activity on board of Disney Fantasy Identifier, 𝑗𝑒 0001 Name, 𝑜 Sailing Away Location, 𝑚 Deck Stage Start Time, 𝑢 𝑡 20.06.2015, 16h30 End Time, 𝑢 𝑓 20.06.2015, 17h15 Duration, 𝜀 45 min Categories, 𝒟 Characters , Fun for All Ages Description, d It’s time to go Sailing Away! Join Mickey and Minnie along with Tinker Bell and the rest of the gang as they welcome you aboard the Disney Fantasy. 17
Recommender Systems: Spatial Items - Activity Activity on board of Disney Fantasy Identifier, 𝑗𝑒 0001 Name, 𝑜 Sailing Away Location, 𝑚 Deck Stage Start Time, 𝑢 𝑡 20.06.2015, 16h30 End Time, 𝑢 𝑓 20.06.2015, 17h15 Duration, 𝜀 45 min Categories, 𝒟 Characters , Fun for All Ages Description, d It’s time to go Sailing Away! Join Mickey and Minnie along with Tinker Bell and the rest of the gang as they welcome you aboard the Disney Fantasy. 18
Recommender Systems: Spatial Items - Activity Activity on board of Disney Fantasy Identifier, 𝑗𝑒 0001 Name, 𝑜 Sailing Away Location, 𝑚 Deck Stage Start Time, 𝑢 𝑡 20.06.2015, 16h30 End Time, 𝑢 𝑓 20.06.2015, 17h15 Duration, 𝜀 45 min Categories, 𝒟 Characters , Fun for All Ages Description, d It’s time to go Sailing Away! Join Mickey and Minnie along with Tinker Bell and the rest of the gang as they welcome you aboard the Disney Fantasy. 19
Recommender Systems: Spatial Items - Activity An activity 𝑏 represents an action of a certain duration that a user 𝑣 can attend or take at some geographically located point in a particular time window ( e.g., conference session, concert during the festival, activity/entertainment during the holidays). • Name, 𝑜 • Location, 𝑚 = (𝑦, 𝑧) • Time windows (start and end time), 𝑢 𝑡 , 𝑢 𝑓 • Duration (execution time), 𝜀 • Vector of categories, 𝑑 = (𝑑 1 , …, 𝑑 𝑙 ) • Description, 𝑒 Activity, 𝑏 = (id, 𝑜, 𝑚, 𝑢 𝑡 , 𝑢 𝑓 , 𝜀, 𝑑, 𝑒) 20
Recommender Systems: Spatial Items Single Items: Locations 21 Y. Zheng. Location-Based Social Networks: Users , pages 243 – 276. Springer New York, NY, 2011.
Recommender Systems: Spatial Items Single Items: Locations POIs H. Yin, X. Zhou, Y. Shao, H. Wang, and S. Sadiq. Joint modeling of user check-in behaviors for point-of-interest recommendation. In Proc. of the 24th ACM CIKM ’15, pages 1631– 1640, 2015. 22 Z. Yu, H. Xu, Z. Yang, and B. Guo. Personalized travel package with multi-point-of-interest recommendation based on crowdsourced user footprints. IEEE Trans Hum Mach Syst. , 46(1):151 – 158, 2016. I.R. Brilhante, J.A.F. de Macêdo, F.M. Nardini, R. Perego, and C. Renso. On planning sightseeing tours with tripbuilder. Inf. Process. Manage. , 51(2):1 – 15, 2015.
Recommender Systems: Spatial Items Single Items: 𝑢 2 𝑢 1 Locations POIs Events X. Ji, Z Qiao, M. Xu, P. Zhang, C. Zhou, and L. Guo. Online event recommendation for event-based social networks. In Proceedings of the 24th WWW ’15 Companion, pages 45– 46, 2015. 23 K. Kaneiwa, M. Iwazume, and K. Fukuda. An upper ontology for event classifications and relations. In Proc. of the 20th Australian Joint Conference on Advances in Artificial Intelligence , AI’07, pages 394– 403, 2007. E. Minkov, B. Charrow, J. Ledlie, S. Teller, and T. Jaakkola. Collaborative future eventrecommendation. In Proc. of the 19th ACM CIKM ’10, pages 819– 828, 2010.
Recommender Systems: Spatial Items Single Items: 𝑢 2 𝑢 1 Locations POIs Events/Activity 24
Recommender Systems: Spatial Items Single Items: 𝑢 2 𝑢 1 Locations POIs Events/Activity Sequential Items: t Trajectory 25 Y. Zheng. Trajectory data mining: An overview. ACM Trans. Intell. Syst. Technol. , 6(3):29:1 – 29:41, 2015.
Recommender Systems: Spatial Items Single Items: 𝑢 2 𝑢 1 Locations POIs Events/Activity Sequential Items: 𝑢 2 t 𝑢 1 Trajectory 26 Itinerary (activity sequence)
Recommendation of Sequences of Activities during Distributed Events and Other RSSI POI Event Trip Activity Sequence [Yin 2015] [Minkov 2010, [Brilhante 2015, during Distributed Macedo 2015] Yu 2016, Events Zheng 2015] List (sequence) recommendation Unique Items Unique Visit Item Limited Availability Travel Time Future oriented (lack of collaborative data) I.R. Brilhante, J.A.F. de Macêdo, F.M. Nardini, R. Perego, and C. Renso. On planning sightseeing tours with H. Yin, X. Zhou, Y. Shao, H. Wang, and S. Sadiq. Joint modeling of user check-in behaviors for point-of-interest tripbuilder. Inf. Process. Manage. , 51(2):1 – 15, 2015. recommendation. In Proc. of the 24th ACM CIKM ’15, pages 1631– 1640, 2015. Z. Yu, H. Xu, Z. Yang, and B. Guo. Personalized travel package with multi-point-of-interest E. Minkov, B. Charrow, J. Ledlie, S. Teller, and T. Jaakkola. Collaborative future eventrecommendation. In Proc. of recommendation based on crowdsourced user footprints. IEEE Trans Hum Mach Syst. , 46(1):151 – 158, 2016. the 19th ACM CIKM ’10, pages 819– 828, 2010. 27 Y. Zheng. Trajectory data mining: An overview. ACM Trans. Intell. Syst. Technol. , 6(3):29:1 – 29:41, 2015. A.Q. Macedo, L.B. Marinho, and R.L.T. Santos. Context-aware event recommendation in event-based social networks. In Proc. of the 9th ACM RecSys ’15, pages 123– 130. 2015.
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