Context-Aware Recommender Systems for Real-World Applications Marie Al-Ghossein

Context-aware recommender systems for real-world applications Marie Al-Ghossein To cite this version: Marie Al-Ghossein. Context-aware recommender systems for real-world applications. Artificial Intel- ligence [cs.AI]. Université Paris-Saclay, 2019. English. NNT : 2019SACLT008. tel-02275811 HAL Id: tel-02275811 https://pastel.archives-ouvertes.fr/tel-02275811 Submitted on 2 Sep 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Context-Aware Recommender Systems for Real-World Applications These` de doctorat de l’Universite´ Paris-Saclay prepar´ ee´ a` Tel´ ecom´ ParisTech Ecole doctorale n◦580 Sciences et Technologies de l’Information et de la Communication (STIC) Specialit´ e´ de doctorat : Informatique NNT : 2019SACLT008 These` present´ ee´ et soutenue a` Paris, le 11 fevrier´ 2019, par MARIE AL-GHOSSEIN Composition du Jury : Talel Abdessalem Professeur, Tel´ ecom´ ParisTech Directeur de these` Anthony Barre´ Directeur Data, JCDecaux SA Examinateur Dario Colazzo Professeur, Universite´ Paris-Dauphine Rapporteur Antoine Cornuejols´ Professeur, AgroParisTech President´ Noam Koenigstein Chercheur, Microsoft Examinateur Nuria Oliver Directrice de recherche, Vodafone Examinateur Fabrice Rossi Professeur, Universite´ Paris 1 Pantheon-Sorbonne´ Rapporteur ` ese de doctorat Th Abstract Recommender systems have proven to be valuable tools to help users overcome the information overload, and significant advances have been made in the field over the last two decades. In particular, contextual information has been leveraged to model the dynamics occurring within users and items. Context is a complex notion and its traditional definition, which is adopted in most recommender systems, fails to cope with several issues occurring in real-world applications. In this thesis, we address the problems of partially observable and unobservable contexts in two particular applications, hotel recommendation and online recommendation, challenging several aspects of the traditional definition of context, including accessibility, relevance, acquisition, and modeling. The first part of the thesis investigates the problem of hotel recommendation which suffers from the continuous cold-start issue, limiting the performance of classical approaches for recommendation. Traveling is not a frequent activity and users tend to have multifaceted behaviors depending on their specific situation. Following an analysis of the user behavior in this domain, we propose novel recommendation approaches integrating partially observable context affecting users and we show how it contributes in improving the recommendation quality. The second part of the thesis addresses the problem of online adaptive recommendation in streaming environments where data is continuously generated. Users and items may depend on some unobservable context and can evolve in different ways and at different rates. We propose to perform online recommendation by actively detecting drifts and updating models accordingly in real-time. We design novel methods adapting to changes occurring in user preferences, item perceptions, and item descriptions, and show the importance of online adaptive recommendation to ensure a good performance over time. iii Acknowledgements This thesis was accomplished with the help of many amazing people that I would like to thank from the bottom of my heart. Thank you to my supervisor Talel, for your wise guidance, for your valuable advices, for your support during these years, and for always lifting me up when I needed it. This whole adventure started on a day of spring when I entered your office with a few questions. If it were not for our discussion then, I would have been on a completely different path. Thank you to my (other) supervisor Anthony, for making me feel welcome at Accor and for ensuring that I have an excellent work environment. Most of all, I am grateful for your continuous presence until the very end and for not leaving me hanging when circumstances suggested so. Thank you for always being available, positive, thoughtful, and encouraging. Thank you to my reviewers, Dario Colazzo and Fabrice Rossi, for taking the time to review my thesis and for providing me with constructive feedback. Thank you to the mem- bers of my jury, Antoine Cornuéjols,Noam Koenigstein, and Nuria Oliver, for doing me the honor of being part of it. Thank you to the entire DIG (ex-DBWeb) team, always gathering amazing people, for setting an incredible and welcoming atmosphere. I have had the pleasure to spend my days at Télécomwith you and have learned so much from our discussions and from our diversity. Thank you to Albert, Antoine, Atef, Fabian, Jacob, Jean-Benoit, Jean-Louis, Jonathan, Julien, Luis, Marc, Mauro, Maximilien, Mikael, Ned, Oana, Pierre, Thomas, and Thomas. Special thanks to Maroua, our sweetest footballer; to Pierre-Alexandre: even though I keep on repeating that you are an old tourist, you are still my youngest co-author with whom I had the craziest deadlines; to Quentin for taking charge of being our sociologist; and to Ziad, my compatriot, for your help and for our numerous discussions. Thank you to the colleagues at AccorHotels and to the Data Science team that took too many different forms since my first day there, the only constant thing being the fun and pleasant atmosphere that was reigning. Thank you for the efforts you all did to keep me involved and up-to-date, while filling me in on the exciting news that were happening during my absence. Special thanks to Agnès,Allan, Amaury, Assitan, Julien, Matthieu, Nicolas, Romain, Vincent, and Zyed. Far away from the world of research, I am very lucky to have incredible friends that have been filling my life since forever or only more recently. I do not say it much, which cost me my reputation, but I am extremely grateful for your devotion, for all of your efforts, and for always being here for me without me even asking. Special thanks to my date every Friday night, to my best shortfriend, to the one who was bad influence during our youth, to our tourist guide in our own hometown, and to my neighbor during my first few years in Paris. Special thanks also to those who took a day off, missed welcoming their first intern, or even had to cross the sea to attend my defense. v vi Contents Thank you to my amazing mentors who have guided me in accomplishing things outside the world of science. You taught me so much, contributed in making me who I am today, and were a great source of inspiration. Thank you to all of my relatives, mainly spread across the US, France, Lebanon, and Dubai, for showering me with love and for sharing so many unforgettable moments. We have followed different paths but we have always stayed close in our hearts. Those who left us have had the biggest contributions and will never be forgotten. Most importantly, thank you to my parents, Eliane and Imad. You have been my rock since day one and the greatest role models. Watching you excel in your respective domains has always inspired me to be a better person. Thank you for teaching me strong values, for encouraging me to set high ambitions, and for always being there for me even when it involves getting on the plane as many times as needed. Thank you for the privilege of a fulfilled life and for sacrificing yours in the process. You have gifted me with the three treasures I cherish most, Najib, Nabil, and Rima. They have always been looking over my shoulder, even when I did not deserve it, and provided tremendous emotional support. You are all source of an immeasurable joy in my life. Contents List of Figures xi List of Tables xiii List of Abbreviations xv I Introduction and Background1 1 Introduction3 1.1 Context in Real-World Recommender Systems.................4 1.2 Contributions....................................7 1.3 Organization of the Thesis.............................9 1.3.1 Publications................................. 10 2 Recommender Systems 13 2.1 The Recommendation Problem.......................... 14 2.1.1 Problem Formulation........................... 14 2.1.2 Types of Feedback............................. 15 2.1.3 Challenges and Limitations........................ 15 2.1.4 Data Representation and Notations................... 17 2.2 Evaluation of Recommender Systems....................... 19 2.2.1 Evaluation Methods............................ 19 2.2.1.1 Offline Evaluation........................ 19 2.2.1.2 User Studies........................... 20 2.2.1.3 Online Evaluation........................ 20 2.2.2 Evaluation Criteria............................. 21 2.2.3 Evaluation Metrics............................. 22 2.3 Recommendation Approaches........................... 24 2.4 Content-Based Filtering Approaches....................... 24 2.4.1 Example of a Content-Based Filtering Approach............ 25 2.4.2 Advantages and Disadvantages...................... 26 2.4.3 Related Recommendation Approaches.................. 26 2.5 Collaborative Filtering Approaches........................ 27 2.5.1 Memory-Based Approaches........................ 28 vii viii Contents 2.5.1.1

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