/PsychologyRS_Paper_List

This is a paper list for Psychology-based RecSys.

MIT LicenseMIT

Psychology-based RecSys

This is a paper list for Psychology-based Recommender Systems.

Survey papers

  1. Psychology-informed Recommender Systems. Foundations and Trends in Information Retrieval, 2021. paper

    Elisabeth Lex, Dominik Kowald, Paul Seitlinger, Thi Ngoc Trang Tran, Alexander Felfernig, Markus Schedl

  2. Towards Cognitive Recommender Systems. Algorithms, 2020. paper

    Amin Beheshti, Shahpar Yakhchi, Salman Mousaeirad, Seyed Mohssen Ghafari, Srinivasa Reddy Goluguri, Mohammad Amin Edrisi

  3. Personality and Recommender Systems. Recommender Systems Handbook, 2015. paper

    Marko Tkalcic, Li Chen

  4. A Survey of Personality-aware Recommendation Systems. arxiv, 2021. paper

    Dhelim Sahraoui，Aung Nyothir, Bouras Mohammed Amine, Ning Huansheng, Cambria Erik

  5. Diversity in Recommender Systems – A Survey. Knowledge-Based Systems, 2017. paper

    Matevž Kunavera,Tomaž Požrl

  6. A Survey of Serendipity in Recommender Systems. Knowledge-Based Systems, 2017. paper

    Kotkov, Denis, Shuaiqiang Wang, Jari Veijalainen.

  7. Serendipity in Recommender Systems: A Systematic Literature Review. Journal of Computer Science and Technology, 2021. paper

    Ziarani Reza Jafari, Reza Ravanmehr

  8. Digital Nudging with Recommender Systems: Survey and Future Directions. Computers in Human Behavior Reports, 2021. paper

    Mathias Jesse, Dietmar Jannach

Personality-based Recommender Systems

  1. Personality Traits and Music Genres: What Do People Prefer to Listen To? UMAP, 2017. paper

    Bruce Ferwerda, Marko Tkalcic, Markus Schedl

  2. User Personality and User Satisfaction with Recommender Systems. Information Systems Frontiers, 2018. paper

    Tien T. Nguyen, F. Maxwell Harper, Loren Terveen, Joseph A. Konstan

  3. Personality, User Preferences and Behavior in Rcommender System. Information Systems Frontiers, 2018. paper

    Raghav Pavan Karumur, Tien T. Nguyen, Joseph A. Konstan

  4. Detecting Personality Traits Using Eye-tracking Data. CHI, 2019. paper

    Berkovsky Shilomo, Ronnie Taib, Irena Koprinska, Eileen Wang, Yucheng Zeng, Jingjie Li, Sabina Kleitman

  5. Inferring Students’ Personality from Their Communication Behavior in Web-based Learning Systems. International Journal of Artificial Intelligence in Education, 2019. paper

    Wen Wu, Li Chen, Qingchang Yang. You Li

  6. How Personality Affects Our Likes: Towards a Better Understanding of Actionable Images. Multimedia, 2017. paper

    Francesco Gelli, Xiangnan He, Tao Chen, Tat-Seng Chua

  7. Alleviating the New User Problem in Collaborative Filtering by Exploiting Personality Information. User Modeling and User Adapted Interaction, 2016. paper

    Ignacio Fernández-Tobías, Matthias Braunhofer, Mehdi Elahi, Francesco Ricci, Iván Cantador

  8. The 50/50 Recommender: A Method Incorporating Personality into Movie Recommender Systems. Engineering Applications of Neural Networks, 2017. paper

    Orestis Nalmpantis, Christos Tjortjis

  9. How Item Discovery Enabled by Diversity Leads to Increased Recommendation List Attractiveness. SAC, 2017. paper

    Bruce Ferwerda, Mark P. Graus, Andreu Vall, Marko Tkalcic, Markus Schedl

  10. Predicting Users’ Personality from Instagram Pictures: Using Visual and/or Content Features? UMAP, 2018. paper

    Bruce Ferwerda, Marko Tkalcic

  11. Social Utilities and Personality Traits for Group Recommendation: A Pilot User Study. ICAART, 2016. paper

    Silvia Rossi，Francesco Cervone

  12. Researching Individual Satisfaction with Group Decisions in Tourism: Experimental Evidence. Information and Communication Technologies in Tourism, 2017. paper

    Amra Delic，Julia Neidhardt，Laurens Rook，Hannes Werthner，Markus Zanker

  13. Improving Socially-Aware Recommendation Accuracy Through Personality. IEEE Transactions on Affective Computing, 2018. paper

    Nana Yaw Asabere, Amevi Acakpovi, Mathias Bennet Michael

  14. Personality Based Recipe Recommendation Using Recipe Network Graphs. Social Computing and Social Media, 2018. paper

    Ifeoma Adaji, Czarina Sharmaine, Simone Debrowney, Kiemute Oyibo, Julita Vassileva

  15. Personalizing Recommendation Diversity Based on User Personality. User Modeling and User-Adapted Interaction, 2018. paper

    Wen Wu, Li Chen，Yu Zhao

  16. A Diversity Adjusting Strategy with Personality for Music Recommendation. RecSys, 2018. paper

    Feng Lu, Nava Tintarev

  17. Mining Personality Traits From Social Messages For Game Recommender Systems. Knowledge-Based Systems, 2019. paper

    Hsin-Chang Yang, Zi-Rui Huang

  18. Conflict Resolution in Group Decision Making: Insights from a Simulation Study. UMAP, 2019. paper

    Thuy Ngoc Nguyen, Francesco Ricci, Amra Delic, Derek Bridge

  19. What is the “Personality” of a Tourism Destination? Information Technology & Tourism, 2019. paper

    Mete Sertkan, Julia Neidhardt, Hannes Werthner

User Centric Evaluation

  1. How Do Users Interact with Algorithm Recommender Systems? The Interaction of Users, Algorithms, and Performance. Computers in Human Behavior, 2020. paper

    Donghee Shin

  2. A Flexible Framework for Evaluating User and Item Fairness in Recommender Systems. User Modeling and User-Adapted Interaction, 2021. paper

    Yashar Deldjoo1, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogin, Tommaso Di Noia

Datasets

  1. Taobao Serendipity Dataset: How Serendipity Improves User Satisfaction with Recommendations? A Large-Scale User Evaluation. The World Wide Web Conference, 2019. paper dataset

    Li Chen, Yonghua Yang, Ningxia Wang, Keping Yang, Quan Yuan

  2. Serendipity 2018: Investigating Serendipity in Recommender Systems Based on Real User Feedback. ACM Symposium on Applied Computing, 2018. paper dataset

    Denis Kotkov, Joseph A. Konstan, Qian Zhao, Jari Veijalainen

  3. Personality 2018: User personality and user satisfaction with recommender systems. Information Systems Frontiers, 2018. paper dataset

    Tien T. Nguyen, F. Maxwell Harper, Loren, Terveen, Joseph A. Konstan

  4. Emotion Classification: GoEmotions: A Dataset of Fine-Grained Emotions. ACL, 2020. paper dataset

    Dorottya Demszky, Dana Movshovitz-Attias, Jeongwoo Ko, Alan Cowen, Gaurav Nemade, Sujith Ravi