Seminar on Current Topics in Recommender Systems

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Together with Wolfgang Wörndl and Daniel Herzog I will be teaching a seminar on Current Topics in Recommender Systems next semester.

We will offer 15 topics, of which I will advise the following:

Utilizing Context with Neural Network Recommendations (Advisor: Linus Dietz)

Some products with recommender systems are used in several contexts, e.g., device type, time of day, etc. The recommendation quality can be improved if the recommender systems is aware of the context and can incorporate this into the ranking of items. It is, however, a challenge to model context effectively and find out the right amount of influence it should have along with traditional features in collaborative filtering and content-based recommendations.

  • Adomavicius and Tuzhilin (2015): Context-Aware Recommender Systems
  • Wu et al. (2017): Recurrent Recommender Networks
  • Beutel et al. (2018): Latent Cross: Making Use of Context in Recurrent Recommender Systems

Touristic Region Detection (Advisor: Linus Dietz)

When recommending travel destinations from all around the globe, one needs to have a list of destinations. Often touristic regions correspond to political regions, however this not always the case. This topic is concerned how to find a 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

Wearable Devices for Proactive Tourist Recommendations (Advisor: Linus Dietz)

Researchers in the area of tourist recommendations often face the challenge that tourists do not receive the recommendations at the right time. Although research has shown that context-awareness is important for recommendations on the go, there are several challenges in a tourism scenario. In foreign countries there is often no reliable Internet connection, the user behavior is quite different from their daily life, and sometimes travelers may be uncomfortable to pull out their smartphones, either because they fear to be stolen or it is inappropriate for social reasons. In this topic it is to be explored what would be alternative devices and approaches to receive proactive, context-aware recommendations during travel.

  • Adomavicius and Tuzhilin (2015): Context-Aware Recommender Systems
  • Wörndl and Lamche (2015): User Interaction with Context-aware Recommender Systems on Smartphones
  • Seneviratne et al. (2017): A Survey of Wearable Devices and Challenges

Automatic Preference Elicitation from Social Media (Advisor: Linus Dietz)

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

Affective Computing for Recommender Systems (Advisor: Linus Dietz)

Affective computing is a current hype trend in several disciplines. Analyzing the facial expressions of the user can give clues to her needs, the general context and the physical status, e.g., sleepiness of drivers. Planning a travel is an emotional endeavor. Travel recommender systems could make use of the facial expressions of users to learn what they like. For example, within a small game the user could be presented with some images of travel destinations and by analyzing her reactions using computer vision. This topic would analyze the roots of affective computing and modern solutions to derive emotions from users. Additional focus should be on the design of user interfaces in recommender systems to actually trigger emotional responses by the users while learning about their preferences.

  • Politou et al. (2017): A survey on mobile affective computing
  • Neidhardt et al. (2015): A picture-based approach to recommender systems
  • Tkalcic et al. (2011): Affective recommender systems: The role of emotions in recommender systems


The full information for TUM master students can be found on the course webpage.