Seminar in Winter Semester 2023/24:

Topics in Recommender Systems

(Ashmi Banerjee)


[13.11.2023]: Final presentation dates announced. The presentations will be held in person in Garching on 31.01.2024, 01.02.2024, 02.02.2024, each starting at 15:00 at FMI building -- 00.12.019, Seminarraum (5612.EG.019) in Garching.

[23.08.2023]: Topics assigned to students. See below.

[29.06.2023] Web page online. This seminar will be based on the new 3rd Edition of the Recommender Systems Handbook. You should be able to check out the electronic version of the handbook from TUM networks or by logging in with your TUM account.


  • Pre-meeting
    • Date and time: Mon 16.10.2023, 16:00
    • Format: Zoom
    • Slides: TBD
  • Registration: using the matching system
  • Duration: WS 2023/24
  • ECTS: 5
  • Capacity: 12
  • Concept
    • 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 into a selected aspect.
      • You need to search for suitable literature besides 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 valuable. You can not only find literature based on keywords but also find papers that have cited a certain paper. To do so, follow your most relevant articles' "cited by ..." links.
  • Information for Paper
    • 12-14 pages (excluding 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), use your own keywords, and include a short abstract
    • No postal address or telephone number, no permission block, copyright line or page numbering, no categories, and subject descriptors or general terms
    • References and citations need to be in the correct format (but usage of a LaTex BIB file is optional)
    • Acknowledgments or appendices are optional and not expected
  • Information for Presentation
    • Duration is 20 mins + 5 mins Q&A
    • 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 Ashmi Banerjee (ashmi [dot] banerjee AT
  • Grading will be based on both the paper and the presentation (approximately equal weight)
  • Prerequisites for credits:
    • Submit the paper in acceptable quality by 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 Mon 16.10.2023, 16:00 (remote via Zoom) focusing on the paper (but you are free to start working on your paper after the matching is complete and you have been assigned a place and topic!)
  • Submitting your paper in the correct format until Wednesday, 20.12.2023, 23:59 (no extensions!)
    • Submit the PDF and also the source file(s) (TeX or Word) via Email to Ashmi Banerjee (ashmi [dot] banerjee AT (sending a Dropbox link or something similar is also possible)
  • The presentations will be held in person in Garching on 31.01.2024, 01.02.2024, 02.02.2024, each starting at 15:00 at FMI building -- 00.12.019, Seminarraum (5612.EG.019)​​​​​​​ in Garching.
  • In-person attendance during the presentations is a mandatory requirement for grading.

Topics and Literature

Foundation article for all topics:

Some more example resources for each topic have been added. Students are encouraged to use additional relevant resources besides the ones mentioned for their work.

1. Adversarial Machine Learning for Recommender Systems (Fatia Kusuma Devi)

Recommender Systems are built to exploit user interaction data and side information to provide improved results. All these algorithms have the same underlying fundamental assumption of “data stationarity”; training and test data are sampled from a similar distribution. However, in an adversarial setting, an intelligent and adaptive attacker deliberately manipulates the data such that this stationarity assumption is violated, thus compromising the RS’s integrity. The integrity of the RS is of paramount significance as it is often used to make life-affecting decisions. Hence, it is important to understand how to build an efficient yet secure RS protected from the clutches of an attacker.

  • Yashar Deldjoo, Tommaso Di Noia, and Felice Antonio Merra. 2020. Adversarial Machine Learning in Recommender Systems (AML-RecSys). Proceedings of the 13th International Conference on Web Search and Data Mining. Association for Computing Machinery, New York, NY, USA, 869–872.

2. People-to-People Reciprocal Recommender Systems (Oskar Brenner)

While most recommender systems recommend a list of items, they rarely consider the other side, i.e., they are solely determined by the choices of the user seeking the recommendation. However, in the case of social networking platforms or dating platforms, the system's success is determined if both parties are interested. This topic explores the different scenarios of an RS where reciprocation is expressed and discusses the distinctive nature of the reciprocal recommenders.

  • Iván Palomares, Carlos Porcel, Luiz Pizzato, Ido Guy, Enrique Herrera-Viedma, Reciprocal Recommender Systems: Analysis of state-of-art literature, challenges and opportunities towards social recommendation, Information Fusion, Volume 69, 2021, Pages 103-127, ISSN 1566-2535,

3. Natural Language Techniques for Recommender Systems (Andrei Staradubets)

Often, RS receives enormous amounts of texts as input, and it is important to analyze these texts using advanced Natural Language Processing Algorithms (NLP). This topic explores the variety of textual inputs that can be accepted by an RS and identifies the cases where NLP can potentially assist in solving the problems.

  • Weiwei Guo, Huiji Gao, Jun Shi, Bo Long, Liang Zhang, Bee-Chung Chen, and Deepak Agarwal. 2019. Deep Natural Language Processing for Search and Recommender Systems. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '19). Association for Computing Machinery, New York, NY, USA, 3199–3200.

4. Fairness in Recommender Systems (Atakan Filgöz)

 A common concern in recent years is whether these Recommender Systems allocate resources fairly or are biased, resulting in an unfair distribution of items or resources. RS often falls prey to Popularity Bias, where certain results are shown more frequently to the users despite their poor quality. The topic studies the fairness in Recommender Systems, possible use cases of biases, and how they can be redesigned to mitigate them.

  • Jurek Leonhardt, Avishek Anand, and Megha Khosla. 2018. User Fairness in Recommender Systems. In Companion Proceedings of the The Web Conference 2018 (WWW '18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 101–102.

5. Cross-domain approaches to Recommender Systems (Fuyuan Li)

When stored user information across multiple domains is used to recommend items to the user, it is referred to as a cross-domain approach to RS. Websites like Alibaba or Amazon often use prior user history while recommending items to the user. However, it becomes increasingly difficult for companies with a single domain, e.g., Spotify and Netflix. This topic explores the different cross-domain recommender systems and their challenges owing to knowledge transfers across multiple domains and evaluates them.

  • Muhammad Murad Khan, Roliana Ibrahim, and Imran Ghani. 2017. Cross Domain Recommender Systems: A Systematic Literature Review. ACM Comput. Surv. 50, 3, Article 36 (May 2018), 34 pages.

6. Group Recommendation Techniques (Muhammed Enes Deniz)

When recommending items to a group of users instead of a single user, all group members' preferences must be taken into account. Different preference aggregation strategies exist for this purpose. Basic approaches, such as calculating the 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 like the item. It is important to say that there is no perfect way to aggregate individual preferences. Instead, the group's intrinsic characteristics and the problem's nature must be considered.

  • Jameson and Smyth (2007): Recommendation to Groups
  • Masthoff (2015): Group Recommender Systems: Aggregation, Satisfaction and Group Attributes
  • Alvarado Rodriguez et al. (2022): A Systematic Review of Interaction Design Strategies for Group Recommendation systems

7. Multi-stakeholder recommender systems (Sema Yılmazer)

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 recommendations, 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 different parties' possible conflicting interests and consider ethical considerations.

  • 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

8. Human Decision Making in Interactive Recommender Systems (Niklas Mamtschur)

The user interface influences how people make decisions. For example, when searching for products, it is important to list appropriate items and consider how to present them. The sequence of items plays a role; adding images may increase sales, or adding an inferior item may actually persuade customers to buy a target product. This topic should discuss 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

9. Evaluating the User Experience of Recommender Systems (Mert Yılmaz İkinci)

Recommender systems recommend products and services to users, such as movies or restaurants. The quality of a recommender system is not only determined by the accuracy of the recommendations. When interacting with the recommender system, the overall user experience (UX) is a very important factor that 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 and the usability of the system's user interfaces, which is an 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

10. Tourism Recommender Systems (Robyn Kölle)

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

  • Herzog and Wörndl (2014): A Travel Recommender System for Combining Multiple Travel Regions to a Composite Trip.
  • Ben Messaoud et al. (2017): SemCoTrip: A Variety-Seeking Model for Recommending Travel Activities in a Composite Trip 
  • Mrazovic et al. (2017): Improving Mobility in Smart Cities with Intelligent Tourist Trip Planning

11. Touristic Region Detection (Dilara Müstecep)

When recommending travel destinations from around the globe, one must have a list of destinations. Often, touristic regions correspond to political regions; however, this is not always the case. This topic concerns how to find touristic areas beyond political boundaries and project them on the map.

  • Dietz (2018): Data-Driven Destination Recommender Systems
  • Schlieder and Henrich (2011): Spatial grounding with vague place models
  • Adams et al. (2015): Frankenplace: Interactive Thematic Mapping for Ad Hoc Exploratory Search

12. Automatic Preference Elicitation from Social Media (Leni Maria Rohe)

It is said that Facebook, Google, and Twitter know you better than yourself. If this were true, it would be a fruitful information source for targeted advertisement and personalized recommendations in other domains. An exemplary task would be to derive, e.g., traveler types from public information of a social media profile. This could include posts, pictures, and additional metadata like locations. Neidhardt et al. established the Seven Factor Model for traveler types. Is it possible to derive the traveler type from a social media profile reliably? How could this be implemented in social network platforms and evaluated using real persons?

  • Sertkan et al. (2018): Mapping of Tourism Destinations to Travel Behavioural Patterns
  • Neidhardt et al. (2015): A picture-based approach to recommender systems