Seminar in Winter Semester 2021/22:

Interactive Recommender Systems

(Dr. Wolfgang Wörndl)


[15.12.2021] Please find the schedule for the presentations below. We will meet online today (Dec. 15th) at 16:00 in this virtual room. The presentations in January will also be conducted virtually, due to the ongoing COVID situation.

[18.11.2021] I have added a paragraph on how to find relevant resarch articles with Google Scholar below. I have also registered the participants to the course in TUM-Online, exam registration will be done later, you do not have to do anything in this regard. The corresponding Moodle course will only be used to share the paper and presentation files among participants.

[04.08.2021] Matching completed, please find the assignment of topics to participants below.

[25.06.2021] Web page online. The information meeting in the first week of the winter semester will be conducted online/virtual, we will decide later if we can do presence sessions at least for the presentations.


  • 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 ( 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 this format:
    • You can either use the LaTex or Word template, but make sure it is the new 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 for support or an appointment (possibly as Skype audio call)
  • 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, 20.10.2021, 16:00 (virtual) 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, 14.12.2021, 23:59 (no extensions!)
    • Submit the PDF and also the source file(s) (TeX or Word) via Email to (sending a Dropbox link or something similar is also possible)
  • Information meeting on Wed, 15.12.2021, 16:00 (virtual) with focus on the presentations
  • The presentations will be held on 25.01., 26.01. and 27.01.2022 each starting at 16:00 (virtual)


  • 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 woerndl[AT] (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] (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 25.01.2022 from 16:00 (virtual)

  • Vladislav Katsarov: Collecting implicit feedback. The cold start problem
  • Lutian Zhang: Conversational and critiquing Recommenders for the wine product domain
  • Davide Alessi: Tourism Mobile Recommender Systems

Wednesday 26.01.2022 from 16:00 (virtual)

  • Oliver Beck: Instrument to Evaluate the Interactivity of Recommender Systems for the Internet of Things
  • Yizhen Li: Multi-Stakeholder Recommender Systems: Factors to Consider and Their Relationship

Thursday 27.01.2022 from 16:00 (virtual)

  • Manu Maheshwar Puthiyadath: Explanations and User Control in Recommender Systems
  • Aly Kamel: Recommendations for Groups
  • Zixin He: Evaluating the user experience of recommender systems in daily life

Topics and Literature

Foundation articles for all topics:

* Konstan and Riedl (2011): Recommender Systems: From Algorithms to User Experience
* Jugovac and Jannach (2017): Interacting with Recommenders - Overview and Research Directions

1. Collecting Implicit and Explicit Feedback for Recommendations (Vladislav Katsarov)

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.

* Schafer et al. (2007): Collaborative Filtering Recommender Systems (Section 9.4)
* Jawaheer (2010): Comparison of Implicit and Explicit Feedback from an Online Music Recommendation Service
* Jannach et al. (2018): Recommending based on Implicit Feedback

2. Visualization Challenges of Recommender Systems (Mohammad Shoaib)

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 et al. (2016): Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities
* Keck and Kramer (2018): Exploring Visualization Challenges for Interactive Recommender Systems
* Kunkel et al. (2017): A 3D Item Space Visualization for Presenting and Manipulating User Preferences in Collaborative Filtering

3. Conversational and Critique-Based Recommender Systems (Lutian Zhang)

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.

* Chen and Pu (2011): Critiquing-based recommenders: survey and emerging trends
* Xie et al. (2016): Incorporating user experience into critiquing-based recommender systems: a collaborative approach based on compound critiquing
* Jannach et al. (2020): A Survey on Conversational Recommender Systems

4. Proactive Recommendation in Mobile Guides (Yaqi Zhang)

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.

* Wörndl et al. (2011): A Model for Proactivity in Mobile, Context-aware Recommender Systems
* Braunhofer et al. (2015): A Context-aware Model for Proactive Recommender Systems in the Tourism Domain
* Sabic and Zanker (2015): Investigating User's Information Needs and Attitudes Towards Proactivity in Mobile Tourist Guides

5. Tourism Recommender Systems (Davide Alessi)

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.

* Ben Messaoud et al. (2017): SemCoTrip: A Variety-Seeking Model for Recommending Travel Activities in a Composite Trip
* Chaudhari and Thakkar (2019): A Comprehensive Survey on Travel Recommender Systems
* Gavalas et al. (2016): Scenic route planning for tourists

6. Recommending and Presenting Sequences of Items (Saad Ahmed)

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 et al. (2017): Recommending a Sequence of Interesting Places for Tourist Trips
* Lim et al. (2018): Tour recommendation and trip planning using location-based social media: a survey
* Gavalas et al. (2014): A survey on algorithmic approaches for solving tourist trip design problems

7. Human Decision Making in Interactive Recommender Systems (Ilhami Kayacan Kaya)

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. The sequence of items plays a role, adding images may increase sale, or adding an inferior item may actually persuade customers to buy a target product. This topic should discuss some aspects of the interplay between user interface design and human decision making in recommender systems.

* Jameson et al. (2015): Human Decision Making and Recommender Systems
* Bollen at al. (2010): Understanding Choice Overload in Recommender Systems
* Ekstrand and Willemsen (2016): Behaviorism is Not Enough: Better Recommendations Through Listening to Users

8. Interactive Recommender Systems for the Internet of Things (Oliver Beck)

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 et al. (2018): An overview of recommender systems in the internet of things
* Palaiokrassas et al. (2017): An IoT Architecture for Personalized Recommendations over Big Data Oriented Applications
* Bergman et al. (2018): An Exploratory Study on How Internet of Things Developing Companies Handle User Experience Requirements

9. Multi-Stakeholder Recommender Systems (Yizhen Li)

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 et al. (2020): Multistakeholder Recommendation: Survey and Research Directions
* Zheng (2017): Multi-Stakeholder Recommendation: Applications and Challenges
* Milano et. al (2020): Recommender Systems and their Ethical Challenges

10. Recommendations for Groups (Aly Kamel)

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.

* Yu et al. (2006): TV Program Recommendation for Multiple Viewers Based on User Profile Merging
* Jameson and Smyth (2007): Recommendation to Groups
* Masthoff (2015): Group Recommender Systems: Aggregation, Satisfaction and Group Attributes

11. Explanations and User Control in Recommender Systems (Manu Maheshwar Puthiyadath)

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 et al. (2019): Explanations and User Control in Recommender Systems
* Tintarev and Masthoff (2015): Explaining Recommendations: Design and Evaluation
* Jin et al. (2019): ContextPlay: Evaluating User Control for Context-Aware Music Recommendation

12. Evaluating the User Experience of Recommender Systems (Zixin He)

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 et al. (2012): Explaining the user experience of recommender systems
* Pu, Chen and Hu (2011): A User-Centric Evaluation Framework for Recommender Systems
* Champiri et al. (2019): User Experience and Recommender Systems