Master-Seminar in Wintersemester 2019/20:

Topics in Recommender Systems

(Dr. Wolfgang Wörndl)


[14.01.2020] Room information for the presentations finalized, see below

[03.01.2020] Presentation dates corrected

[31.12.2019] Happy New Year! I have posted the presentation schedule below, we do not need the slot on 24.01. I have also added the submitted papers to the Moodle course

[17.12.2019] The presentations will be held on 24.01., 27.01. and 28.01.20 (rooms to be determined), each session starting at 16:00. I will post the schedule by the end of the year

[18.10.2019] I registered every seminar participant in TUM-Online, so you should also have access to the Moodle course where you can find the example paper for the ACM format I showed at the information meeting

[08.10.2019] The information meeting in December had to be moved fro Dec. 18th to Dec. 17th, 16:00 in 02.09.023. (It is not a big problem if you can't participate on this date.)

[05.08.2019] Assingment of participants to topics

[07.07.2019] List of topics added

[04.07.2019] 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 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
  • Information for paper
    • 7-9 pages in English in this format: (ACM Master Article Template)
    • You can either use the LaTex (recommended) or Word templates
    • 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 appendix are optional and not expected
  • Information for presentation
    • Duration is 25-35 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] 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


  • (optional) information/pre-course meeting on Wed, 17.07.2019, 16:00 in room 01.07.023 (this meeting is completely optional and you won’t hurt your chances of getting a place if you do not come)
  • Information meeting on Wed, 16.10.2019, 16:00 with the seminar participants in room 01.07.023 (focus on the paper)
  • Submission of your paper in the correct format until Mon, 16.12.2019, 23:59 (no extensions!)
    • Submit the PDF and also the source code (TeX or Word) via Email to woerndl[AT] (sending a Dropbox link or something similar is also possible)
  • Information meeting on Tue, 17.12.2019, 16:00 in room 02.09.023 (focus on the presentations)
  • The presentations will be held on three of the following dates each starting at 16:00 (exact date and room to be determined): 22.01., 23.01., 24.01., 27.01., 28.01., 29.01.2020
  • Presentation schedule:
    • Monday, 27.01.2020 (room 02.08.020) from 16:00:
      - Mehmet Taner Ünal: Mobile Recommender Systems
      - Ananya Misra: Recommendations for Groups
      - Archishman Roy: User Interaction Issues of Recommender Systems
      - Elif Erbil: Tourism Recommender Systems
    • Tuesday, 28.01.2020 (room 02.09.023) from 16:00:
      - Xavier Fontes: Preference Elicitation from Social Media for Recommender Systems
      - Ali Ganbarov: Collecting Implicit and Explicit Feedback for Recommendations
      - André Miguel Ferreira da Cruz: Proactive Recommendation in Mobile Guides
      - Ebru Sözbir: Beyond Matrix Completion


  • 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] (after 17.07.2019, 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] (after 17.07.2019) (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)

List of Topics

1. Conversational and Critique-Based Recommender System

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
  • McGinty and B. Smyth (2010): Adaptive Selection: An Analysis of Critiquing and Preference-Based Feedback in Conversational Recommender Systems

2. Mobile Recommender Systems (Student: Mehmet Taner Ünal, Advisor: Wolfgang Wörndl)

Mobile devices such as smartphones are increasingly used for information access tasks while traveling. However, mobile information access still suffers from limited resources regarding input capabilities, displays, network bandwidth and other limitations of mobile devices. In addition, mobile applications must consider mobile user constraints such as limited attention span while moving, changing locations and contexts, and expectations of quick and easy interactions. Therefore, it is desirable to tailor information access to the current user needs in mobile recommendation and other adaptive systems.

  • Lathia (2015): The Anatomy of Mobile Location-Based Recommender Systems
  • Baltrunas at al. (2012): Context Relevance Assessment and Exploitation in Mobile Recommender Systems
  • Pimenidis et. al. (2018): Mobile recommender systems: Identifying the major concepts

3. Recommendations for Groups (Student: Ananya Misra, Advisor: Wolfgang Wörndl)

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

4. Recommending and Presenting Sequences of Items

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

5. User Interaction Issues of Recommender Systems (Student: Archishman Roy, Advisor: Wolfgang Wörndl)

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 to put users in control of their preferences that systems manage, and also allowing feedback on recommendations.

  • Konstan and Riedl (2012): Recommender systems: from algorithms to user experience
  • Jugovac and Jannach (2017): Interacting with Recommenders – Overview and Research Directions
  • He et al. (2016): Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities

6. Tourism Recommender Systems (Student: Elif Erbil, Advisor: Wolfgang Wörndl)

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.

  • 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

7. Preference Elicitation from Social Media for Recommender Systems (Student: Xavier Fontes, Advisor: Wolfgang Wörndl)

It is said that Facebook, Google and Twitter know you better than yourself. If this was true, it would be a fruitful information source not only for targeted advertisement, but for personalized recommendations in other domains. An exemplary task would how 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 reliably derive the traveler type from a social media profile? How could this be implemented in the different 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
  • Dietz et al. (2018): Characterisation of Traveller Types Using Check-In Data from Location-Based Social Networks

8. Privacy-Enhanced Recommender Systems

Recommender systems generate personalized recommendation based on information about users. The more accurate this information is, the better recommendations can be tailored towards user needs and interests. But collecting and utilizing personal data raises privacy issues. Users may be unaware which data is collected and do not want systems to acquire information about themselves. There are existing solutions to generate personalized recommendation while still respecting user privacy. Thus, this is topic about the trade-off between personalization and privacy in recommender systems.

  • Drosatos et al. (2015): Pythia: A Privacy-Enhanced Personalized Contextual Suggestion System for Tourism
  • Saravanan and Ramakrishnan (2016): Preserving Privacy in the Context of Location Based Services Through Location Hider in Mobile-Tourism
  • Friedman et al. (2016): A Differential Privacy Framework for Matrix Factorization Recommender Systems

9. Collecting Implicit and Explicit Feedback for Recommendations (Student: Ali Ganbarov, Advisor: Wolfgang Wörndl)

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

10. Proactive Recommendation in Mobile Guides (Student: André Miguel Ferreira da Cruz, Advisor: Wolfgang Wörndl)

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 at 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

11. Time-Aware Recommender Systems

Context such as time and location is important information to be considered in recommender systems. A simple example is a recommendation service for nearby restaurants based on the current user location. But time also plays a larger role. For example, there are temporal constraints when visiting a city because some sights may have opening times and a restaurant for dinner does not make much sense in the morning. In addition, user preference may be dynamic and change over time.

  • Campos et al. (2014): Time-aware Recommender Systems: a Comprehensive Survey and Analysis of Existing Evaluation Protocols
  • Aggrawal (2016): Time- and Location-Sensitive Recommender Systems
  • Yuan et al. (2013): Time-aware Point-of-interest Recommendation

12. Beyond Matrix Completion (Student: Ebru Sözbir, Advisor: Wolfgang Wörndl)

The main goal of recommender systems is to predict unknown ratings of items for users. This can be seen as the task to complete the user-item matrix. Method such as matrix factorization can solve this task and have been successfully applied in various domains. However, for some scenarios these general approaches work not as well. So the first cited paper discusses why this is the case and reviews some alternative approaches. These include considering novelty and diversity, context, user interaction and also sequence-aware recommendation.

  • Jannach et al. (2016): Recommender Systems – Beyond Matrix Completion
  • Quadrana et al.: (2018): Sequence-aware recommender systems
  • Ge et al. (2010): Beyond Accuracy: Evaluating Recommender Systems by Coverage and Serendipity