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This repository contains all the material supporting the tutorial Psychology-informed Recommender Systems: A Human-centric Perspective on Recommender Systems. The tutorial is based on a recent long article published in Foundations and Trends in Information Retrieval

Lex, E., Kowald, D., Seitlinger, P., Tran, T.N.T., Felfernig, A., and Schedl, M. Psychology-informed Recommender Systems, Foundations and Trends in Information Retrieval, 15(2):134-242, 2021. Preprint available here:

The tutorial was previously held at the ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR’22) 2022, at the 11th Italian Information Retrieval Workshop (IIR) 2021, and at the Complex Networks and their Application conference 2021.

Topic and Relevance

Personalized recommender systems are essential tools to facilitate human decision making. Most state-of-the-art recommender systems use advanced machine learning techniques to model and predict user preferences from behavioral data. While such systems can provide useful recommendations, their algorithmic design does not incorporate the underlying psychological mechanisms that shape user preferences and behavior. In this interdisciplinary tutorial, we guide the attendees through the state-of-the-art in Psychology-informed Recommender Systems (PIRS), i.e., recommender systems that consider extrinsic and intrinsic human factors. We cover cognition-inspired, personality-aware, and affect-aware recommendation approaches; and we show how such systems can improve the recommendation process in a highly human-centric manner.

Tutorial Materials

The ACM Web Conference 2022: presentation slides