Seminar in Winter Semester 2023/24:

Human-Centered Recommender Systems

(Dr. Wolfgang Wörndl)


[13.12.2023] Presentation schedule posted below. Update: rooms also added

[08.12.2023] Added room for the information meeting Wed, 13.12.2023, 16:00: 01.07.023

[08.11.2023] Added Link to TUM Citation Guide. Exact time and room for intermediate meeting: Wed, 15.11.2023, 16:30 (room 01.05.058 - meeting room at Chair of Connected Mobility)

[18.10.2023] Added date of intermediate meeting: Wed, 15.11.2023, 16:00 (room to be determined)

[11.08.2023] Topics assigned to students, see below. There are no open spots in the seminar available.

[27.06.2023] Web page online


  • This seminar is for students in the Informatics Master program (module IN2107)
    • The seminar will be conducted in English language
    • Prerequisites are a Bachelor's degree in computer science or related field
  • Students are expected to write a paper and give a presentation about the given topic area
    • Just summarizing related work is only the foundation of your paper, you need to show your own contribution beyond the summarization of other work. This contribution can be a new classification scheme, assessment and/or comparison of existing work, coming up with some novel conceptual ideas, design of an algorithm idea, mock-up of a new user interface, sketching new application areas and/or similar contributions
    • Your paper can be very focused, concentrating on a (small) subset of the given area. You can for example first give a brief overview of the topic area and then dig deeper on a selected aspect
    • You need to search for suitable literature in addition to the stated references. Orient yourself to the given references or other research papers for the structure/contents of your own paper. You need to cite the literature your work is based on and clearly indicate when you are adopting or paraphrasing other work
    • To find additional literature, Google Scholar ( is a valuable source. You can not only find literature based on keywords, but also find papers that have cited a certain paper. To do so, follow the "cited by ..." links of your most relevant articles

  • Information for paper
    • 12-14 pages (including references) in English in the ACM format with a single column, not the old one with two columns
    • State your name, affiliation and email address (as the only author), include an abstract and selected CCS concepts and keywords
    • References and citations need to be in the correct format (but usage of a LaTex BIB file is optional)
    • Acknowledgments or appendix are optional and not expected
  • Information for presentation
    • Duration is 25-30 minutes, plus questions&answers
    • The talk should be given freely, i.e. not completely read out from a script (in English)
    • You should present slides electronically in any format (e.g. Powerpoint or PDF)
    • You can use the TUM powerpoint template or your own format for the slides
  • All topics are advised by Wolfgang Wörndl, please send an Email to for support or an appointment
  • Grading will be based on both the paper and the presentation (approximately equal weight)
  • Prerequisites for credits:
    • Submit the paper in acceptable quality until the stated deadline
    • Give a presentation of acceptable quality on the assigned date
    • Attend all presentation meetings and participate in the discussion


  • Information meeting on Wed, 18.10.2023, 16:00 (room 01.07.023) with focus on the paper (but you are free to start earlier working on your paper after the matching is completed, and you have been assigned a place and topic!)
  • Intermediate meeting with short presentations of paper outlines and informal discussion: Wed, 15.11.2023, 16:30 (room 01.05.058)
  • Submission of your paper in the correct format until Tue, 12.12.2023, 23:59 (no extensions!)
    • Submit the PDF and also the LaTeX source file(s) via Email to (sending a link to a repository, dropbox or something similar is also possible)
  • Information meeting on Wed, 13.12.2023, 16:00 (room 01.07.023) with focus on the presentations
  • The presentations will likely be held on 30.01., 31.01. and 01.02.2024 each starting at 16:00 (room tbd)


  • Registration is done using the Matching System of the department: (you have to use the matching system to participate in the seminar!)
  • You can optionally send a short motivation statement why you want to participate in this seminar via Email to (max. 150 words) (sending a motivation statement is optional, but may increase your chances of getting a place)
  • You can optionally also send a list of up to 3 preferred topics via Email to (sending a list of preferred topics is completely optional and I can not guarantee that you will get one of your preferences if you get a place in the seminar)

Presentation Schedule

Tuesday 30.01.2024 (from 16:00, room 02.07.014)

  • Ufuk Yarışan - Collecting Implicit and Explicit Feedback for Recommendations
  • Denado Rabeli - Ethical Challenges of Recommender Systems
  • Emre Demirci - Graph Based Conversational and Critique-Based Recommender Systems
  • Awayah Shahid - Proactive Recommendation in Mobile Guides

Wednesday 31.01.2024 (from 16:00, room 01.07.023)

  • Dabo Yang - Tourism Recommender Systems And Social Media
  • Stian Brekken Antonsen - Recommending and Presenting Sequences of Items
  • Emek Gözlüklü - Interactive Recommender Systems for the Internet of Things
  • Kaan Uslu - Is Achieving Fairness on Recommender Systems Possible?: A Case Study of Digital Distribution Platform Steam

Thursday 01.02.2024 (from 16:00, room 02.07.014)

  • Meikel Kokowski - Synergizing Group Recommendations: Unveiling the Potential of Large Language Models for Enhanced Personalized Experiences
  • Ayşe Betül Çetin - Reason Generation for Explainable Recommendations by using ChatGPT
  • Göksu Güz - Evaluating UX of Recommender Systems

Topics and Literature

Introduction to recommender systems (optional for all topics):

* Ricci, F., Rokach, L., & Shapira, B. (2021). Recommender systems: Techniques, applications, and challenges. Recommender Systems Handbook, 1-35. (

1. Collecting Implicit and Explicit Feedback for Recommendations (Ufuk Yarışan)

Many recommendation techniques such as collaborative filtering need user ratings to recommend items. User ratings can be created by collecting explicit and explicit feedback. Explicit feedback, e.g., a user rating on a scale from 1 to 5 is very accurate but it demands an effort from the user. On the other hand, collecting implicit feedback is a bigger challenge but observing the user's browsing behavior or eye movements when interacting with a recommender system allows to identify recommendations that better satisfy the user's needs without annoying the user. The focus should be on how to better use implicit feedback for recommendation.

* Rendle, S. (2022). Item recommendation from implicit feedback. In Recommender Systems Handbook (pp. 143-171). Springer, New York, NY.
* Jawaheer, G., Szomszor, M., & Kostkova, P. (2010, September). Comparison of implicit and explicit feedback from an online music recommendation service. In proceedings of the 1st international workshop on information heterogeneity and fusion in recommender systems (pp. 47-51).
* Jannach, D., Lerche, L., & Zanker, M. (2018). Recommending based on implicit feedback. In Social Information Access (pp. 510-569). Springer, Cham.

2. Visualization Challenges of Recommender Systems (Denado Rabeli)

The traditional focus in recommender systems research has been on the algorithms to predict ratings and generate recommendations, but has shifted more towards the user experience in recent years. So is increasingly important how users can interact with recommender systems. One particular challenge is how to visualize item spaces to assist users in exploring more complex domains such as travel recommendation.

* 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 Applications56, 9-27.
* Keck, M., & Kammer, D. (2018). Exploring Visualization Challenges for Interactive Recommender Systems. VisBIA@ AVI, 22-31.
* Kunkel, J., Loepp, B., & Ziegler, J. (2017, March). A 3D item space visualization for presenting and manipulating user preferences in collaborative filtering. In Proceedings of the 22nd international conference on intelligent user interfaces (pp. 3-15).

3. Conversational and Critique-Based Recommender Systems (Emre Demirci)

Structured dialogues between the user and the recommender system promise better recommendations than traditional recommendations based on a one-time request/response. Recommender system providing dialogues to iteratively learn the user's preferences and improve the recommendations are called conversational. A conversational recommender system can either suggest two or more alternatives for concrete recommendations and let the user indicate her or his preferences for one item over the others or iteratively ask the user questions about the most important features of the expected recommendations.

* Pramod, D., & Bafna, P. (2022). Conversational Recommender Systems Techniques, Tools, Acceptance, and Adoption: A State of the Art Review. Expert Systems with Applications, 117539.
* Xie, H., Wang, D. D., Rao, Y., Wong, T. L., Raymond, L. Y., Chen, L., & Wang, F. L. (2018). Incorporating user experience into critiquing-based recommender systems: a collaborative approach based on compound critiquing. International Journal of Machine Learning and Cybernetics9(5), 837-852.
* Jannach, D., Manzoor, A., Cai, W., & Chen, L. (2021). A survey on conversational recommender systems. ACM Computing Surveys (CSUR)54(5), 1-36.

4. Proactive Recommendation in Mobile Guides (Awayah Shahid)

Proactivity means that the systems pushes suitable items to the user, without explicit user request. So the question is not only which item to recommend, but also when. The decision can be made based on the current context, e.g. time and location. For example, a user visiting a city gets recommendations for nearby recommended restaurants around lunch time. The question is also when and how to communicate the items to the user. In addition, explanations of recommendations are important when recommendations are proactively delivered because the user may be unaware why she received recommendations.

* Woerndl, W., Huebner, J., Bader, R., & Gallego-Vico, D. (2011, October). A model for proactivity in mobile, context-aware recommender systems. In Proceedings of the fifth ACM conference on Recommender systems (pp. 273-276).
* Braunhofer, M., Ricci, F., Lamche, B., & Wörndl, W. (2015, August). A context-aware model for proactive recommender systems in the tourism domain. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct (pp. 1070-1075).
* Rook, L., Sabic, A., & Zanker, M. (2020). Engagement in proactive recommendations. Journal of Intelligent Information Systems54(1), 79-100.

5. Tourism Recommender Systems (Dabo Yang)

Recommendation for travel and tourism pose increased challenges for systems because of the complex nature of the domain. Recommender systems need to combine heterogenous data sources such as flights, hotels and attractions, take user preferences into account, but also consider context such as the best times to travel in a region. In addition, people do relatively few trips per year, so it hard to compile enough data for destination recommender systems.

* Massimo, D., & Ricci, F. (2022). Building effective recommender systems for tourists. AI Magazine.
* Chaudhari, K., & Thakkar, A. (2020). A comprehensive survey on travel recommender systems. Archives of Computational Methods in Engineering27(5), 1545-1571.
* Sarkar, J. L., Majumder, A., Panigrahi, C. R., Roy, S., & Pati, B. (2022). Tourism recommendation system: a survey and future research directions. Multimedia Tools and Applications, 1-45.

6. Recommending and Presenting Sequences of Items (Stian Brekken Antonsen)

Many recommender systems recommend single items such as movies or restaurants. Recommending a sequence of items, for example a music playlist or a tourist trip composed of multiple points of interest, is a more complicated issue. Not only the choice of items but also the sequence order influences the quality of the recommendation. For example, a strong ending with a very well-liked item at the end of a sequence might maximize the user satisfaction as the user tends to remember the end of recommendation most. When a recommendation is found, the system has to decide how to present the sequence to the user. The whole sequence could be recommended at a time but in certain scenarios, it might be appreciated if only the upcoming item is presented to the user at the right time.

* Wörndl, W., Hefele, A., & Herzog, D. (2017). Recommending a sequence of interesting places for tourist trips. Information Technology & Tourism17(1), 31-54.
* Lim, K. H., Chan, J., Karunasekera, S., & Leckie, C. (2019). Tour recommendation and trip planning using location-based social media: A survey. Knowledge and Information Systems60(3), 1247-1275.
* Gavalas, D., Konstantopoulos, C., Mastakas, K., & Pantziou, G. (2014). A survey on algorithmic approaches for solving tourist trip design problems. Journal of Heuristics20(3), 291-328.

7. Interactive Recommender Systems for the Internet of Things (Emek Gözlüklü)

The Internet of Things (IoT) is about connecting physical devices and smart object and thus enable a new range of services such as automating logistics. But is also important to consider how people can interact with these kind of smart objects. It is needed to come up with suitable interaction patterns both for administering and configurating IoT devices and also (end) users having to handle smart objects. One if the problems is that these devices often do not have a build-in user interface for interaction, such as a display, but have to be managed using a smartphone app, for example, that only shows an abstract representation of the object. Recommender systems could play an important role in supporting users discovering and interacting with IoT services.

* Felfernig, A., Polat-Erdeniz, S., Uran, C., Reiterer, S., Atas, M., Tran, T. N. T., ... & Dolui, K. (2019). An overview of recommender systems in the internet of things. Journal of Intelligent Information Systems52(2), 285-309.
* Palaiokrassas, G., Karlis, I., Litke, A., Charlaftis, V., & Varvarigou, T. (2017, July). An IoT architecture for personalized recommendations over big data oriented applications. In 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) (Vol. 2, pp. 475-480). IEEE.
* Bergman, J., Olsson, T., Johansson, I., & Rassmus-Gröhn, K. (2018, March). An exploratory study on how Internet of Things developing companies handle User Experience Requirements. In International Working Conference on Requirements Engineering: Foundation for Software Quality (pp. 20-36). Springer, Cham.

8. Fairness and Multi-Stakeholder Recommender Systems (Kaan Uslu)

Often, recommender systems are optimized for recommendations to best suit the users' interests and preferences only. However, other stakeholders are involved in real work recommender applications. For example, in e-Commerce, sellers may want to maximize their profit, and in tourism recommendation, suggesting the most popular places may lead to overtourism. In addition, objectives such as coverage, diversity and fairness play an important role when generating recommendations. Therefore, recommender systems need to balance possible conflicting interests of different parties and take ethical considerations into account.

* Abdollahpouri, H., & Burke, R. (2022). Multistakeholder recommender systems. In Recommender systems handbook(pp. 647-677). Springer, New York, NY.
* Ekstrand, M. D., Das, A., Burke, R., & Diaz, F. (2022). Fairness in recommender systems. In Recommender systems handbook (pp. 679-707). Springer, New York, NY.
* Wang, Y., Ma, W., Zhang, M., Liu, Y., & Ma, S. (2022). A survey on the fairness of recommender systems. ACM Journal of the ACM (JACM).

9. Recommendations for Groups (Meikel Kokowski)

When recommending items to a group of users instead of a single user, the preferences of all group members have to be taken into account. Different preference aggregation strategies exist for this purpose. Basic approaches such as calculating an average preference of all users are easy to realize but might not be optimal. For example, if one user really dislikes a certain item, this item should not be recommended even if the majority of group members likes the item. It is important to say that there is no perfect way to aggregate the individual preferences. Instead, the group's intrinsic characteristics and the problem's nature have to be considered. It is also important how users can interact with these group recommender systems.

* Masthoff, J., & Delić, A. (2022). Group Recommender Systems: Beyond Preference Aggregation. In Recommender Systems Handbook (pp. 381-420). Springer, New York, NY.
* Jameson, A., & Smyth, B. (2007). Recommendation to groups. In The adaptive web (pp. 596-627). Springer, Berlin, Heidelberg.
* Alvarado Rodriguez, O. L., Htun, N. N., Jin, Y., & Verbert, K. (2022). A systematic review of interaction design strategies for group recommendation systems. Proceedings of the ACM on Human-Computer Interaction.

10. Explanations and User Control in Recommender Systems (Ayşe Betül Çetin)

Recommender systems excel in providing personalized items recommendations to users but it is not always clear why certain items were recommended. Often, the system and corresponding algorithms are black boxes for users. Explanations and justifications can help making the recommendation more understandable for users. Furthermore, it is important to provide means for users to control and influence the recommendation process. This is not only important to facilitate more accurate recommendations but also to increase the trust of users in the system.

* Jannach, D., Jugovac, M. & Nunes, I. (2023). 6 Explanations and user control in recommender systems. In M. Augstein, E. Herder & W. Wörndl (Ed.), Personalized Human-Computer Interaction (pp. 129-152). Berlin, Boston: De Gruyter Oldenbourg.
* Tintarev, N., & Masthoff, J. (2022). Beyond explaining single item recommendations. In Recommender Systems Handbook (pp. 711-756). Springer, New York, NY.
* Kouki, P., Schaffer, J., Pujara, J., O’Donovan, J., & Getoor, L. (2020). Generating and understanding personalized explanations in hybrid recommender systems. ACM Transactions on Interactive Intelligent Systems (TiiS)10(4), 1-40.

11. Human-centered Recommender Systems and Decision Making (Christian Briegel)

The user interface influences how people make decision. For example, when searching for products it is not only important to list appropriate items but also consider how to present them. This topic should discuss some aspects of the interplay between user interface design and human decision making in recommender systems. In addition, psychological factors such as personality, emotions, and decision biases can significantly affect the outcome of a decision process.

* l. Jameson, A., Willemsen, M. C., & Felfernig, A. (2022). Individual and Group Decision Making and Recommender Systems. In Recommender Systems Handbook (pp. 789-832). Springer, New York, NY.
* Konstan, J., & Terveen, L. (2021). Human-centered recommender systems: Origins, advances, challenges, and opportunities. AI Magazine, 42(3), 31-42.
* Tran, T. N. T., Felfernig, A., & Tintarev, N. (2021). Humanized recommender systems: State-of-the-art and research issues. ACM Transactions on Interactive Intelligent Systems (TiiS), 11(2), 1-41.

12. Evaluating the User Experience of Recommender Systems (Göksu Güz)

Recommender systems recommend products and services such as movies or restaurants to users. The quality of a recommender system is not only determined by the accuracy of the recommendations. The overall user experience (UX) when interacting with the recommender system is a very important factor which decides if people like using the recommender system. According to Hassenzahl (2008), UX is "a momentary, primarily evaluative feeling (good-bad) while interacting with a product or service. Good UX is the consequence of fulfilling the human needs for autonomy, competence, stimulation (self-oriented) through interacting with the product or service (i.e. hedonic quality)." Many methods exist to evaluate the UX of recommender systems but also the usability of the system's user interfaces which is one very important aspect of UX.

* Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User modeling and user-adapted interaction, 22(4), 441-504.
* Pu, P., Chen, L., & Hu, R. (2011, October). A user-centric evaluation framework for recommender systems. In Proceedings of the fifth ACM conference on Recommender systems (pp. 157-164).
* Champiri, Z. D., Mujtaba, G., Salim, S. S., & Chong, C. Y. (2019, January). User experience and recommender systems. In 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) (pp. 1-5). IEEE.