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Technical University of Munich

Seminar in Winter Semester 2022/23:

Interactive Recommender Systems

(Dr. Wolfgang Wörndl)

News

[14.12.2022] You can find the presentation schedule below

[07.09.2022] Seminar topics assignment to students, see below

[01.07.2022] Web page online. We will do the seminar with in-presence meetings but may have to switch to online if COVID-19 measures require that.

Information

  • 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 an 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 focussed, concentrating on a (small) subset of the given area. You can for example first give an 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 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 (scholar.google.com) 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 newer ACM format with a single column, and not the older 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 woerndl(at)in.tum.de 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

Procedure

  • Information meeting on Wed, 19.10.2022, 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 complete and you have been assigned a place and topic!)
  • Submission of your paper in the correct format until Tue, 13.12.2022, 23:59 (no extensions!)
    • Submit the PDF and also the source file(s) (TeX or Word) via Email to woerndl(at)in.tum.de (sending a Dropbox link or something similar is also possible)
  • Information meeting on Wed, 14.12.2022, 15:00 (room 01.07.023) with focus on the presentations
  • The presentations will be held on 24.01., 25.01. and 26.01.2023 each starting at 16:00 (room tbd)

Registration

  • Registration is done using the Matching System of the department: http://www.in.tum.de/en/current-students/modules-and-courses/practical-courses-and-seminar-courses.html (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 woerndl(at)in.tum.de (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 woerndl(at)in.tum.de (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 24.01.2023 (from 16:00, room 01.07.023)

  • You-shin Tsai: Collecting Implicit and Explicit Feedback for Recommendations
  • Shuang Qu: Conversational and Critique-Based Recommender Systems
  • S M Ahasanul Haque: Tourism Recommender Systems

Wednesday 25.01.2023 (from 16:00, room 01.07.023)

  • Horia Turcuman: Attentive Score Aggregation for Group Recommender Systems
  • Nicolas Gehring: Explanations for Recommender Systems with a Focus on Knowledge Graph
  • Saad Ahmed: Drug Recommendation System for Epileptic Patients

Thursday 26.01.2023 (from 16:00, room 01.07.023)

  • Yufei Yi: Visualization Challenges of Recommender Systems
  • Jingyi Jia: Recommending and Presenting Sequences of Videos
  • Ioan-Daniel Crăciun: Recommender Systems based on Semantics and Knowledge Graphs

Topics and Literature

Foundation articles for all topics:

* Konstan, J. A., & Riedl, J. (2012). Recommender systems: from algorithms to user experience. User modeling and user-adapted interaction, 22(1), 101-123.
* Jugovac, M., & Jannach, D. (2017). Interacting with recommenders—overview and research directions. ACM Transactions on Interactive Intelligent Systems (TiiS), 7(3), 1-46.

1. Collecting Implicit and Explicit Feedback for Recommendations (You-shin Tsai)

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 (Yufei Yi)

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 Applications, 56, 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 (Shuang Qu)

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 Cybernetics, 9(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 (Berkay Ugur Senocak)

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 Systems, 54(1), 79-100.

5. Tourism Recommender Systems (S M Ahasanul Haque)

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 Engineering, 27(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 (Jingyi Jia)

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 & Tourism, 17(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 Systems, 60(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 Heuristics, 20(3), 291-328.

7. Interactive Recommender Systems for the Internet of Things (Saad Ahmed)

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 Systems, 52(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 (Oliver Jung)

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.
* Milano, S., Taddeo, M., & Floridi, L. (2020). Recommender systems and their ethical challenges. Ai & Society, 35(4), 957-967.

9. Recommendations for Groups (Horia Turcuman)

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 (Nicolas Gehring)

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. (2019, September). Explanations and user control in recommender systems. In Proceedings of the 23rd International Workshop on Personalization and Recommendation on the Web and Beyond (pp. 31-31).
* 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 (Janit Grover)

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. Recommender Systems based on Semantics and Knowledge Graphs (Ioan-Daniel Crăciun)

Content- and knowledge-based recommender systems have long taken meta data and additional information about the item space into account to generate recommendations. Possible advantages inlclude improving the explainability of recommendations or mitigating the cold-start problem. A promising yet underexplored approach in this regard is to utilize knowledge graphs for recommender systems, for example in the travel domain.

* Musto, C., Gemmis, M. D., Lops, P., Narducci, F., & Semeraro, G. (2022). Semantics and content-based recommendations. In Recommender systems handbook (pp. 251-298). Springer, New York, NY.
* Lu, C., Laublet, P., & Stankovic, M. (2016, November). Travel attractions recommendation with knowledge graphs. In European knowledge acquisition workshop (pp. 416-431). Springer, Cham.
* Wang, M., Qiu, L., & Wang, X. (2021). A survey on knowledge graph embeddings for link prediction. Symmetry, 13(3), 485.

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