Sruthi Viswanathan
Behrooz Omidvar-Tehrani
ABSTRACT
Is recommendation the new search? Recommender systems have shortened the search for information in everyday activities such as following the news, media, and shopping. In this paper, we address the challenges of capturing the situational needs of the user and linking them to the available datasets with the concept of Mindsets. Mindsets are categories such as “I’m hungry” and “Surprise me” designed to lead the users to explicitly state their intent, control the recommended content, save time, get inspired, and gain shortcuts for a satisficing exploration of POI recommendations. In our methodology, we first compiled Mindsets with a card sorting workshop and a formative evaluation. Using the insights gathered from potential end users, we then quantified Mindsets by linking them to POI utility measures using approximated lexicographic multi-objective optimisation. Finally, we ran a summative evaluation of Mindsets and derived guidelines for designing novel categories for recommender systems.
Supplemental Material
- Gregory D. Abowd, Anind K. Dey, P. Brown, N. Davies, M. Smith, and Pete Steggles. 1999. Towards a Better Understanding of Context and Context-Awareness. In HUC.Google Scholar
- Gediminas Adomavicius and Alexander Tuzhilin. 2011. Context-Aware Recommender Systems. Springer US, Boston, MA, 217–253. https://doi.org/10.1007/978-0-387-85820-3_7Google ScholarCross Ref
- P. Aksenov, Astrid Kemperman, and T. Arentze. 2014. Tourists’ Dynamic Needs and Affects in Personalised Travel Route Recommendations. In UMAP Workshops.Google Scholar
- Saleema Amershi, Maya Cakmak, William Bradley Knox, and Todd Kulesza. 2014. Power to the People: The Role of Humans in Interactive Machine Learning. AI Magazine 35, 4 (Dec. 2014), 105–120. https://doi.org/10.1609/aimag.v35i4.2513Google ScholarDigital Library
- Alia Amin, Sian Townsend, Jacco van Ossenbruggen, and Lynda Hardman. 2009. Fancy a Drink in Canary Wharf?: A User Study on Location-Based Mobile Search. In Human-Computer Interaction – INTERACT 2009, Tom Gross, Jan Gulliksen, Paula Kotzé, Lars Oestreicher, Philippe Palanque, Raquel Oliveira Prates, and Marco Winckler (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 736–749.Google ScholarDigital Library
- Sarabjot Singh Anand and Bamshad Mobasher. 2007. From Web to Social Web: Discovering and Deploying User and Content Profiles. Springer-Verlag, Berlin, Heidelberg, Chapter Contextual Recommendation, 142–160. https://doi.org/10.1007/978-3-540-74951-6_8Google ScholarDigital Library
- Hiteshwar Kumar Azad and Akshay Deepak. 2019. Query expansion techniques for information retrieval: a survey. Information Processing & Management 56, 5 (2019), 1698–1735.Google ScholarDigital Library
- Jie Bao, Yu Zheng, David Wilkie, and Mohamed Mokbel. 2015. Recommendations in location-based social networks: a survey. GeoInformatica 19, 3 (2015), 525–565.Google ScholarDigital Library
- Betim Berjani and Thorsten Strufe. 2011. A recommendation system for spots in location-based online social networks. In Proceedings of the 4th workshop on social network systems. ACM, 4.Google ScholarDigital Library
- [10] Map Box.2010. Retrieved August 17, 2021 from https://www.mapbox.comGoogle Scholar
- Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology 3, 2 (2006), 77–101. https://doi.org/10.1191/1478088706qp063oa arXiv:https://www.tandfonline.com/doi/pdf/10.1191/1478088706qp063oaGoogle ScholarCross Ref
- David J Brenes, Daniel Gayo-Avello, and Kilian Pérez-González. 2009. Survey and evaluation of query intent detection methods. In Proceedings of the 2009 Workshop on Web Search Click Data. 1–7.Google ScholarDigital Library
- Nela Brown and Tony Stockman. 2013. Examining the Use of Thematic Analysis As a Tool for Informing Design of New Family Communication Technologies. In Proceedings of the 27th International BCS Human Computer Interaction Conference (London, UK) (BCS-HCI ’13). British Computer Society, Swinton, UK, UK, Article 21, 6 pages. http://dl.acm.org/citation.cfm?id=2578048.2578078Google ScholarDigital Library
- Huanhuan Cao, Derek Hao Hu, Dou Shen, Daxin Jiang, Jian-Tao Sun, Enhong Chen, and Qiang Yang. 2009. Context-aware query classification. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. 3–10.Google ScholarDigital Library
- Claudio Carpineto, Stefano Mizzaro, Giovanni Romano, and Matteo Snidero. 2009. Mobile information retrieval with search results clustering: Prototypes and evaluations. Journal of the American Society for Information Science and Technology (2009).Google Scholar
- Joseph Chee Chang, Nathan Hahn, Adam Perer, and Aniket Kittur. 2019. SearchLens: Composing and Capturing Complex User Interests for Exploratory Search. In Proceedings of the 24th International Conference on Intelligent User Interfaces (Marina del Ray, California) (IUI ’19). Association for Computing Machinery, New York, NY, USA, 498–509. https://doi.org/10.1145/3301275.3302321Google ScholarDigital Library
- Chen Cheng, Haiqin Yang, Irwin King, and Michael R Lyu. 2012. Fused matrix factorization with geographical and social influence in location-based social networks. In Twenty-Sixth AAAI Conference on Artificial Intelligence.Google Scholar
- Keith Cheverst, Nigel Davies, Keith Mitchell, and Christos Efstratiou. 2001. Using Context as a Crystal Ball: Rewards and Pitfalls. Personal Ubiquitous Comput. 5, 1 (Jan. 2001), 8–11. https://doi.org/10.1007/s007790170020Google ScholarDigital Library
- Keith Cheverst, Nigel Davies, Keith Mitchell, and Adrian Friday. 2000. Experiences of Developing and Deploying a Context-Aware Tourist Guide: The GUIDE Project. In Proceedings of the 6th Annual International Conference on Mobile Computing and Networking (Boston, Massachusetts, USA) (MobiCom ’00). Association for Computing Machinery, New York, NY, USA, 20–31. https://doi.org/10.1145/345910.345916Google ScholarDigital Library
- Eloise Coupey, Julie R Irwin, and John W Payne. 1998. Product category familiarity and preference construction. Journal of Consumer Research 24, 4 (1998), 459–468.Google ScholarCross Ref
- Paolo Cremonesi, Antonio Donatacci, Franca Garzotto, and Roberto Turrin. 2012. Decision-Making in Recommender Systems: The Role of User’s Goals and Bounded Resources.. In Decisions@ RecSys. Citeseer, 1–7.Google Scholar
- Susan Dumais. 2013. Personalized Search: Potential and Pitfalls. https://www.microsoft.com/en-us/research/publication/personalized-search-potential-pitfalls/ NIPS 2013 Workshop on Personalization, Keynote Talk.Google Scholar
- Malin Eiband, Sarah Theres Völkel, Daniel Buschek, Sophia Cook, and Heinrich Hussmann. 2019. When People and Algorithms Meet: User-reported Problems in Intelligent Everyday Applications. In Proceedings of the 24th International Conference on Intelligent User Interfaces (Marina del Ray, California) (IUI ’19). ACM, New York, NY, USA, 96–106. https://doi.org/10.1145/3301275.3302262Google ScholarDigital Library
- Mehdi Elahi, Matthias Braunhofer, Francesco Ricci, and Marko Tkalcic. 2013. Personality-Based Active Learning for Collaborative Filtering Recommender Systems. In AI*IA 2013: Advances in Artificial Intelligence, Matteo Baldoni, Cristina Baroglio, Guido Boella, and Roberto Micalizio (Eds.). Springer International Publishing, Cham, 360–371.Google Scholar
- Michael TM Emmerich and André H Deutz. 2018. A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Natural computing 17, 3 (2018), 585–609.Google Scholar
- Laura Faulkner. 2003. Beyond the five-user assumption: Benefits of increased sample sizes in usability testing. Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc 35 (09 2003), 379–83. https://doi.org/10.3758/BF03195514Google ScholarCross Ref
- Gregory Ference, Mao Ye, and Wang-Chien Lee. 2013. Location recommendation for out-of-town users in location-based social networks. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management. ACM, 721–726.Google ScholarDigital Library
- FourSquare. 2016. Foursquare Venue Category Hierarchy.Retrieved August 17, 2021 from https://developer.foursquare.com/docs/build-with-foursquare/categories/Google Scholar
- Huiji Gao, Jiliang Tang, Xia Hu, and Huan Liu. 2013. Exploring temporal effects for location recommendation on location-based social networks. In Proceedings of the 7th ACM conference on Recommender systems. ACM, 93–100.Google ScholarDigital Library
- Liqiang Geng and Howard J Hamilton. 2006. Interestingness measures for data mining: A survey. ACM Computing Surveys (CSUR) 38, 3 (2006), 9.Google ScholarDigital Library
- Jonna Häkkilä and Jani Mäntyjärvi. 2006. Developing Design Guidelines for Context-Aware Mobile Applications. In Proceedings of the 3rd International Conference on Mobile Technology, Applications and Systems(Bangkok, Thailand) (Mobility ’06). Association for Computing Machinery, New York, NY, USA, 24–es. https://doi.org/10.1145/1292331.1292358Google ScholarDigital Library
- Marti Hearst. 2009. Search user interfaces. Cambridge university press.Google Scholar
- Marti A. Hearst. 2006. Clustering Versus Faceted Categories for Information Exploration. Commun. ACM 49, 4 (April 2006), 59–61. https://doi.org/10.1145/1121949.1121983Google ScholarDigital Library
- Tomi Heimonen. 2008. Mobile findex: Facilitating information access in mobile web search with automatic result clustering. Advances in Human-Computer Interaction 2008 (2008).Google Scholar
- Tomi Heimonen. 2015. Emerging Perspectives on the Design, Use, and Evaluation of Mobile and Handheld Devices.IGI Global. https://doi.org/10.4018/978-1-4666-8583-3.ch004Google ScholarCross Ref
- Tobias Hesselmann, Stefan Flöring, and Marwin Schmitt. 2009. Stacked Half-Pie Menus: Navigating Nested Menus on Interactive Tabletops. In Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces (Banff, Alberta, Canada) (ITS ’09). Association for Computing Machinery, New York, NY, USA, 173–180. https://doi.org/10.1145/1731903.1731936Google ScholarDigital Library
- Jian Hu, Gang Wang, Fred Lochovsky, Jian-tao Sun, and Zheng Chen. 2009. Understanding user’s query intent with wikipedia. In Proceedings of the 18th international conference on World wide web. 471–480.Google ScholarDigital Library
- Liesbeth Huybrechts, Cristiano Storni, Yanki Lee, Selina Schepers, Jessica Schoffelen, and Katrien Dreessen. 2014. Participation is Risky. Approaches to Joint Creative Processes (1 ed.). Vol. 13. Valiz; Amsterdam, Netherlands.$$Uhttps://lirias.kuleuven.be/retrieve/257649$$DParticipation is Risky book details [freely available]Google Scholar
- [39] User Interviews.2015. Retrieved August 17, 2021 from https://www.userinterviews.comGoogle Scholar
- [40] InVision.2011. Retrieved August 17, 2021 from https://www.invisionapp.comGoogle Scholar
- Rolf Jagerman, Ilya Markov, and Maarten de Rijke. 2019. When People Change Their Mind: Off-Policy Evaluation in Non-Stationary Recommendation Environments. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (Melbourne VIC, Australia) (WSDM ’19). Association for Computing Machinery, New York, NY, USA, 447–455. https://doi.org/10.1145/3289600.3290958Google ScholarDigital Library
- Mohsen Jamali and Martin Ester. 2009. Trustwalker: a random walk model for combining trust-based and item-based recommendation. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 397–406.Google ScholarDigital Library
- Mohsen Jamali and Martin Ester. 2010. A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the fourth ACM conference on Recommender systems. ACM, 135–142.Google ScholarDigital Library
- Shagun Jhaver, Yoni Karpfen, and Judd Antin. 2018. Algorithmic anxiety and coping strategies of Airbnb hosts. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 421.Google ScholarDigital Library
- Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, and Pushmeet Kohli. 2019. Degenerate Feedback Loops in Recommender Systems. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (Honolulu, HI, USA) (AIES ’19). Association for Computing Machinery, New York, NY, USA, 383–390. https://doi.org/10.1145/3306618.3314288Google ScholarDigital Library
- Alita Joyce. 2019. NN/g Nielsen Norman Group’s Formative vs. Summative Evaluations. Retrieved August 17, 2021 from https://www.nngroup.com/articles/formative-vs-summative-evaluations/Google Scholar
- Michael Jugovac and Dietmar Jannach. 2017. Interacting with Recommenders—Overview and Research Directions. ACM Trans. Interact. Intell. Syst. 7, 3, Article 10 (Sept. 2017), 46 pages. https://doi.org/10.1145/3001837Google ScholarDigital Library
- Michael Jugovac, Ingrid Nunes, and Dietmar Jannach. 2018. Investigating the decision-making behavior of maximizers and satisficers in the presence of recommendations. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization. 279–283.Google ScholarDigital Library
- Claire Kayacik, Sherol Chen, Signe Noerly, Jess Holbrook, Adam Roberts, and Douglas Eck. 2019. Identifying the Intersections: User Experience + Research Scientist Collaboration in a Generative Machine Learning Interface. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI EA ’19). ACM, New York, NY, USA, Article CS09, 8 pages. https://doi.org/10.1145/3290607.3299059Google ScholarDigital Library
- Gloria Omale Kelly Blum. 2019. Gartner Predicts 80% of Marketers Will Abandon Personalization Efforts by 2025. Retrieved August 17, 2021 from https://www.gartner.com/en/newsroom/press-releases/2019-12-02-gartner-predicts-80--of-marketers-will-abandon-personGoogle Scholar
- Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer8(2009), 30–37.Google Scholar
- Hoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho, and Sehee Chung. 2019. MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation. arxiv:1908.00413 [cs.IR]Google Scholar
- Defu Lian, Cong Zhao, Xing Xie, Guangzhong Sun, Enhong Chen, and Yong Rui. 2014. GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 831–840.Google ScholarDigital Library
- Xiao Lin, Hongjie Chen, Changhua Pei, Fei Sun, Xuanji Xiao, Hanxiao Sun, Yongfeng Zhang, Wenwu Ou, and Peng Jiang. 2019. A Pareto-efficient Algorithm for Multiple Objective Optimization in e-Commerce Recommendation. In Proceedings of the 13th ACM Conference on Recommender Systems (Copenhagen, Denmark) (RecSys ’19). ACM, New York, NY, USA, 20–28. https://doi.org/10.1145/3298689.3346998Google ScholarDigital Library
- Qi Liu, Haiping Ma, Enhong Chen, and Hui Xiong. 2013. A survey of context-aware mobile recommendations. International Journal of Information Technology and Decision Making 12 (03 2013). https://doi.org/10.1142/S0219622013500077Google ScholarCross Ref
- Yiding Liu, Tuan-Anh Nguyen Pham, Gao Cong, and Quan Yuan. 2017. An experimental evaluation of point-of-interest recommendation in location-based social networks. VLDB 10, 10 (2017), 1010–1021.Google ScholarDigital Library
- Takashi Nicholas Maeda, Kota Tsubouchi, and Fujio Toriumi. 2017. Next Place Prediction in Unfamiliar Places Considering Contextual Factors. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (Redondo Beach, CA, USA) (SIGSPATIAL ’17). Association for Computing Machinery, New York, NY, USA, Article 76, 4 pages. https://doi.org/10.1145/3139958.3139970Google ScholarDigital Library
- [58] React Native.2015. Retrieved August 17, 2021 from https://reactnative.devGoogle Scholar
- Don Norman. 2009. Technology First, Needs Last.Retrieved August 17, 2021 from https://jnd.org/technology_first_needs_last/Google Scholar
- Ingrid Nunes and Dietmar Jannach. 2017. A systematic review and taxonomy of explanations in decision support and recommender systems. User Modeling and User-Adapted Interaction 27, 3 (2017), 393–444.Google ScholarDigital Library
- Behrooz Omidvar-Tehrani. 2021. Interactive Region-of-Interest Discovery using Exploratory Feedback. arXiv e-prints (2021).Google Scholar
- Behrooz Omidvar-Tehrani, Sruthi Viswanathan, and Jean-Michel Renders. 2020. Interactive and Explainable Point-of-Interest Recommendation using Look-alike Groups. In SIGSPATIAL ’20: 28th International Conference on Advances in Geographic Information Systems, Seattle, WA, USA, November 3-6, 2020. ACM, 389–392.Google ScholarDigital Library
- Antti Oulasvirta, Sakari Tamminen, Virpi Roto, and Jaana Kuorelahti. 2005. Interaction in 4-second Bursts: The Fragmented Nature of Attentional Resources in Mobile HCI. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Portland, Oregon, USA) (CHI ’05). ACM, New York, NY, USA, 919–928. https://doi.org/10.1145/1054972.1055101Google ScholarDigital Library
- Jeni Paay, Jesper Kjeldskov, Mikael B Skov, Per M Nielsen, and Jon Pearce. 2016. Discovering activities in your city using transitory search. In Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM, 387–393.Google ScholarDigital Library
- Jeni Paay, Jesper Kjeldskov, Mikael B. Skov, Per M. Nielsen, and Jon Pearce. 2016. Discovering Activities in Your City Using Transitory Search. In Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services (Florence, Italy) (MobileHCI ’16). ACM, New York, NY, USA, 387–393. https://doi.org/10.1145/2935334.2935378Google ScholarDigital Library
- Mark J Rentmeesters, Wei K Tsai, and Kwei-Jay Lin. 1996. A theory of lexicographic multi-criteria optimization. In Proceedings of ICECCS’96: 2nd IEEE International Conference on Engineering of Complex Computer Systems (held jointly with 6th CSESAW and 4th IEEE RTAW). IEEE, 76–79.Google ScholarCross Ref
- Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011. Introduction to recommender systems handbook. In Recommender systems handbook. Springer, 1–35.Google Scholar
- Mario Rodriguez, Christian Posse, and Ethan Zhang. 2012. Multiple Objective Optimization in Recommender Systems. In Proceedings of the Sixth ACM Conference on Recommender Systems (Dublin, Ireland) (RecSys ’12). ACM, New York, NY, USA, 11–18. https://doi.org/10.1145/2365952.2365961Google ScholarDigital Library
- Simon Schaffer. 1999. Enlightened automata.Google Scholar
- Tobias Schnabel, Paul N. Bennett, Susan T. Dumais, and Thorsten Joachims. 2016. Using Shortlists to Support Decision Making and Improve Recommender System Performance. In Proceedings of the 25th International Conference on World Wide Web (Montréal, Québec, Canada) (WWW ’16). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 987–997. https://doi.org/10.1145/2872427.2883012Google ScholarDigital Library
- Barry Schwartz, Andrew Ward, John Monterosso, Sonja Lyubomirsky, Katherine White, and Darrin R Lehman. 2002. Maximizing versus satisficing: happiness is a matter of choice.Journal of personality and social psychology 83, 5(2002), 1178.Google Scholar
- [72] Elastic Search.2010. Retrieved August 17, 2021 from https://www.elastic.co/products/elasticsearchGoogle Scholar
- Julie Anne Séguin, Alec Scharff, and Kyle Pedersen. 2019. Triptech: A Method for Evaluating Early Design Concepts. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI EA ’19). ACM, New York, NY, USA, Article CS24, 8 pages. https://doi.org/10.1145/3290607.3299061Google ScholarDigital Library
- Dou Shen, Rong Pan, Jian-Tao Sun, Jeffrey Junfeng Pan, Kangheng Wu, Jie Yin, and Qiang Yang. 2006. Query enrichment for web-query classification. ACM Transactions on Information Systems (TOIS) 24, 3 (2006), 320–352.Google ScholarDigital Library
- [75] Sketch.2010. Retrieved August 17, 2021 from https://www.sketch.comGoogle Scholar
- Jonathan Strahl, Jaakko Peltonen, and Patrik Floréen. 2021. Directing and Combining Multiple Queries for Exploratory Search by Visual Interactive Intent Modeling. In Human-Computer Interaction – INTERACT 2021, Carmelo Ardito, Rosa Lanzilotti, Alessio Malizia, Helen Petrie, Antonio Piccinno, Giuseppe Desolda, and Kori Inkpen (Eds.). Springer International Publishing, Cham, 514–535.Google ScholarDigital Library
- Simone Stumpf, Vidya Rajaram, Lida Li, Margaret Burnett, Thomas Dietterich, Erin Sullivan, Russell Drummond, and Jonathan Herlocker. 2007. Toward Harnessing User Feedback for Machine Learning. In Proceedings of the 12th International Conference on Intelligent User Interfaces (Honolulu, Hawaii, USA) (IUI ’07). ACM, New York, NY, USA, 82–91. https://doi.org/10.1145/1216295.1216316Google ScholarDigital Library
- Yu Sun, Nicholas Jing Yuan, Yingzi Wang, Xing Xie, Kieran McDonald, and Rui Zhang. 2016. Contextual Intent Tracking for Personal Assistants. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (KDD ’16). Association for Computing Machinery, New York, NY, USA, 273–282. https://doi.org/10.1145/2939672.2939676Google ScholarDigital Library
- M. Tkalcic, A. Kosir, and Jurij F. Tasic. 2011. Affective recommender systems: The role of emotions in recommender systems.Google Scholar
- Wataru Tsukahara and Nigel Ward. 2001. Responding to Subtle, Fleeting Changes in the User’s Internal State. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Seattle, Washington, USA) (CHI ’01). Association for Computing Machinery, New York, NY, USA, 77–84. https://doi.org/10.1145/365024.365047Google ScholarDigital Library
- Tuomas Vaittinen and David Mcgookin. 2018. Uncover: supporting city exploration with egocentric visualizations of location-based content. Personal and Ubiquitous Computing 22, 4 (2018), 807–824.Google ScholarDigital Library
- Sruthi Viswanathan, Cecile Boulard, Adrien Bruyat, and Antonietta Maria Grasso. 2022. Situational Recommender: Are You On the Spot, Refining Plans, or Just Bored?. In CHI Conference on Human Factors in Computing Systems (CHI ’22), April 29-May 5, 2022, New Orleans, LA, USA (New Orleans, LA, USA) (CHI ’22). https://doi.org/10.1145/3491102.3501909Google ScholarDigital Library
- Sruthi Viswanathan, Behrooz Omidvar-Tehrani, Adrien Bruyat, Frédéric Roulland, and Antonietta Maria Grasso. 2020. Designing Ambient Wanderer: Mobile Recommendations for Urban Exploration. In Proceedings of the 2020 ACM Designing Interactive Systems Conference (Eindhoven, Netherlands) (DIS ’20). Association for Computing Machinery, New York, NY, USA, 1405–1418. https://doi.org/10.1145/3357236.3395518Google ScholarDigital Library
- Shoujin Wang, Longbing Cao, Yan Wang, Quan Z Sheng, Mehmet Orgun, and Defu Lian. 2019. A survey on session-based recommender systems. arXiv preprint arXiv:1902.04864(2019).Google Scholar
- WHO. 2020. Coronavirus. Retrieved August 17, 2021 from https://www.who.int/health-topics/coronavirusGoogle Scholar
- Chauncey Wilson. 2013. Brainstorming and beyond: a user-centered design method. Newnes.Google Scholar
- Ben Wolford. 2018. Complete guide to GDPR compliance. Retrieved August 17, 2021 from https://gdpr.euGoogle Scholar
- Jed R. Wood and Larry E. Wood. 2008. Card Sorting: Current Practices and Beyond. J. Usability Studies 4, 1 (Nov. 2008), 1–6. http://dl.acm.org/citation.cfm?id=2835577.2835578Google ScholarDigital Library
- Liang Xiang, Quan Yuan, Shiwan Zhao, Li Chen, Xiatian Zhang, Qing Yang, and Jimeng Sun. 2010. Temporal recommendation on graphs via long-and short-term preference fusion. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 723–732.Google ScholarDigital Library
- Dingqi Yang, Daqing Zhang, and Bingqing Qu. 2016. Participatory cultural mapping based on collective behavior data in location-based social networks. ACM Transactions on Intelligent Systems and Technology (TIST) 7, 3(2016), 30.Google ScholarDigital Library
- Dingqi Yang, Daqing Zhang, Vincent W Zheng, and Zhiyong Yu. 2014. Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. IEEE Transactions on Systems, Man, and Cybernetics: Systems 45, 1(2014), 129–142.Google ScholarCross Ref
- Mao Ye, Peifeng Yin, and Wang-Chien Lee. 2010. Location recommendation for location-based social networks. In SIGSPATIAL. ACM, 458–461.Google Scholar
- Mao Ye, Peifeng Yin, Wang-Chien Lee, and Dik-Lun Lee. 2011. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. ACM, 325–334.Google ScholarDigital Library
- Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, and Nadia Magnenat Thalmann. 2013. Time-aware point-of-interest recommendation. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. ACM, 363–372.Google ScholarDigital Library
- Jia-Dong Zhang, Chi-Yin Chow, and Yanhua Li. 2014. iGeoRec: A personalized and efficient geographical location recommendation framework. IEEE Transactions on Services Computing 8, 5 (2014), 701–714.Google ScholarCross Ref
- Zhiqian Zhang, Chenliang Li, Zhiyong Wu, Aixin Sun, Dengpan Ye, and Xiangyang Luo. 2019. NEXT: a neural network framework for next POI recommendation. Frontiers of Computer Science 14, 2 (Aug 2019), 314–333. https://doi.org/10.1007/s11704-018-8011-2Google ScholarDigital Library
- Yong Zheng, Bamshad Mobasher, and Robin Burke. 2016. User-Oriented Context Suggestion. In Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization (Halifax, Nova Scotia, Canada) (UMAP ’16). Association for Computing Machinery, New York, NY, USA, 249–258. https://doi.org/10.1145/2930238.2930252Google ScholarDigital Library
- [98] Zoom.2011. Retrieved August 17, 2021 from https://www.zoom.usGoogle Scholar
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