Interactive intelligent systems use Artificial Intelligence technologies (e.g., from natural language processing) for building so-called intelligent systems with which people can interact to enhance or extend machine's capabilities (e.g., classifying images manually).

This semester we will deal with the topic "Interactive Recommender Systems". Interactive Recommender Systems extend the traditional approach of Recommender Systems in the field of Human-Computer Interaction with the realization of a dialogical interaction in which textual or visual explanations are given to the system-generated recommendations. The interaction with or the adaptation of the given recommendations take place through feedback to initiate such "dialogue". Explanations are generated, and feedback is stored using a user model for which different approaches (e.g., feature-based models) exist. Interactive Recommender Systems enable a better understanding of the relationship between the input of the system, i.e., the individual preferences and the recommendations given, allowing people to interact predictably and effectively.

In this proseminar, you will get an introduction to the topic Recommender Systems, with an emphasis on the interaction perspective, to contextualize these Intelligent Systems in the field of human-computer interaction. First, an introduction to Recommender Systems will be given. Building on this, we will open up existing approaches, methods, and implementations in the form of presentations. Each participant is expected to independently prepare, present, and discuss their presentation with the class. Based on the results of the discussion, a written summary elaboration needs to be carried out.

Topics

	topic	talk date	name	outline date	report date
1	Supporting Bias through Recommender UI Design	09.12.2019	Jannis Hamann	25.11.2019	13.01.2020
2	User Preferences in Preference Elicitation	09.12.2019	Daniia Vergazova	25.11.2019	13.01.2020
3	Analytic Hierarchy Process for Preference Elicitation	16.12.2019	Christian Schult	02.12.2019	20.01.2020
4	Compound Critique Generation	16.12.2019	Viktoria Andres	02.12.2019	20.01.2020
5	Explanations in Recommender Systems	06.01.2020	Philip Wälde	09.12.2019	27.01.2020
6	Explanations in Conversational RS/Compound Critique	06.01.2020	Björn Benedict Heyder	09.12.2019	27.01.2020
7	Interactive Explanations	13.01.2020	Martin Dreher	16.12.2019	03.02.2020
8	Visualization Interface for Recommendations	13.01.2020	Joel Heuer	16.12.2019	03.02.2020
9	2/3D representation of recommendations/preference elicitation	20.01.2020	Stefanie Bosch	06.01.2019	10.02.2020
10	Dynamically adjusted recommendations	20.01.2020	Justus Richard Tartz	06.01.2019	10.02.2020
11	Choosing Recommendation Algorithm	27.01.2020	Laila Melis Atarim	13.01.2020	17.02.2020
12	User Control in Recommendations	27.01.2020	Laura Pysz	13.01.2020	17.02.2020
13	Proactive Visualizations	03.02.2020	Philip Thiele	20.01.2020	24.02.2020
14	Personalized Explanations in Recommender System	03.02.2020	David Bauer	20.01.2020	24.02.2020

Literature

Jugovac, M., & Jannach, D. (2017). Interacting with Recommenders—Overview and Research Directions. ACM Transactions on Interactive Intelligent Systems (TiiS), 7(3), 10–46. http://doi.org/10.1145/3001837

He, C., Parra, D., & Verbert, K. (2016). Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities. Expert Systems with Applications, 56, 9–27. http://doi.org/10.1016/j.eswa.2016.02.013