Master-Seminar in Wintersemester 2018/19:
Current Topics in Recommender Systems
(Dr. Wolfgang Wörndl, Linus Dietz, Daniel Herzog)
News
[16.01.2019] Rooms for presentations added
[15.01.2019] Schedule for presentations:
- Monday, Jan. 21st (from 16:00, room 02.09.023)
- Ahmed Abdelaziz: Evaluating the User Experience and Usability of Recommender Systems (Advisor: Daniel Herzog)
- Mykolas Gustas: Public and Interactive Displays (Advisor: Daniel Herzog)
- Daniel Homola: Improving Users’ Satisfaction with a Critique-based Recommender (Advisor: Wolfgang Wörndl)
- Tuesday, Jan. 22nd (from 16:00, room 01.05.058)
- Alexis Gamboa Soto: Implementation of Social Media Data to Enhance a Recommender Systems for Travel Destinations Advisor: Wolfgang Wörndl)
- Sameera Thimbiri Palage: Travel Blog Articles for Qualitative Mapping of Touristic Activities to Destinations (Advisor: Linus Dietz)
- Ashmi Banerjee: Automated Travel Preference Elicitation from Social Media: A survey and summary of existing approaches (Advisor: Linus Dietz)
- Wednesday, Jan. 23rd (from 16:00, room 01.07.023)
- Janek Groß: Utilizing Context with Neural Network Recommendations (Advisor: Linus Dietz)
- Rinita Roy: Wearable Devices for Proactive Tourist Recommendations (Advisor: Linus Dietz)
- Marcello Feroce: Determining the Ideal Timing of Proactive Recommendations Based on the User’s Context Emotional State Advisor: Linus Dietz)
[24.07.2018] Topics assigned to participants, see below
[20.06.2018] Date seminar meeting moved from 17.10. to Thu, 18.10.2018, 16:00
[11.06.2018] Web page online
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
- Information for paper
- 7-9 pages in English in this format: http://www.acm.org/publications/proceedings-template (2017 ACM sigconf 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
- The topics are either advised by Wolfgang Wörndl, Linus Dietz or Daniel Herzog. Please send an Email to your advisor 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
- (optional) information/pre-course meeting on Wed, 27.06.2018, 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 Thu, 18.10.2018, 16:00 with the seminar participants
- Submission of your paper in the correct format until Mon, 14.01.2019, 23:59 (no extensions!)
- Submit the PDF and also the source code (TeX or Word) via Email to woerndl[AT]in.tum.de, dietzl[AT]in.tum.de, herzogd[AT]in.tum.de (sending a Dropbox link or something similar is also possible)
- The presentations will be held on the following dates each starting at 16:00 (room to be determined): 21.01, 22.01., 23.01. and (if needed) 24.01.2019
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, dietzl[AT]in.tum.de, herzogd[AT]in.tum.de (after 27.06.2018, 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, dietzl[AT]in.tum.de, herzogd[AT]in.tum.de (after 27.06.2018) (sending a list of preferred topics is completely optional and we can not guarantee that you will get one of your preferences if you get a place in the seminar)
Presentation Schedule
- Monday, Jan. 21st (from 16:00, room 02.09.023)
- Ahmed Abdelaziz: Evaluating the User Experience and Usability of Recommender Systems (Advisor: Daniel Herzog)
- Mykolas Gustas: Public and Interactive Displays (Advisor: Daniel Herzog)
- Daniel Homola: Improving Users’ Satisfaction with a Critique-based Recommender (Advisor: Wolfgang Wörndl)
- Tuesday, Jan. 22nd (from 16:00, room 01.05.058)
- Alexis Gamboa Soto: Implementation of Social Media Data to Enhance a Recommender Systems for Travel Destinations Advisor: Wolfgang Wörndl)
- Sameera Thimbiri Palage: Travel Blog Articles for Qualitative Mapping of Touristic Activities to Destinations (Advisor: Linus Dietz)
- Ashmi Banerjee: Automated Travel Preference Elicitation from Social Media: A survey and summary of existing approaches (Advisor: Linus Dietz)
- Wednesday, Jan. 23rd (from 16:00, room 01.07.023)
- Janek Groß: Utilizing Context with Neural Network Recommendations (Advisor: Linus Dietz)
- Rinita Roy: Wearable Devices for Proactive Tourist Recommendations (Advisor: Linus Dietz)
- Marcello Feroce: Determining the Ideal Timing of Proactive Recommendations Based on the User’s Context Emotional State Advisor: Linus Dietz)
Topics
1. Beyond Matrix Completion (Presenter: Niklas Bremen, 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
2. Mobile Recommender Systems (Presenter: Negin Karimi, 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. Utilizing Context with Neural Network Recommendations (Presenter: Janek Groß, 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
4. Recommendations for Groups (Presenter: Goktug Erce Gurel, Advisor: Daniel Herzog)
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
- Jameons and Smyth (2007): Recommendation to Groups
- Masthoff (2015): Group Recommender Systems: Aggregation, Satisfaction and Group Attributes
5. Recommending and Presenting Sequences of Items (Presenter: Venkata Subrahmanya Sai Sasank Kothe, Advisor: Daniel Herzog)
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.
- Masthoff (2004): Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
- Masthoff (2015): Group Recommender Systems: Aggregation, Satisfaction and Group Attributes
- Wörndl et al. (2017): Recommending a Sequence of Interesting Places for Tourist Trips
6. User Interaction Issues (Presenter: Daniel Homola, 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
- Jannach et. al (2016): User Control in Recommender Systems: Overview and Interaction Challenges
7. Public and Interactive Displays (Presenter: Mykolas Gustas, Advisor: Daniel Herzog)
Public displays are large displays in public spaces that allow a community to interact with the screen and / or other users. Public displays are getting more and more popular as they can provide useful information such as maps, information about public transport or interesting spots nearby to a person passing by or a crowd. Recent work tries to improve the services proved by public screens by enhancing the means of interaction and enabling personalized content on the displays. Nevertheless, these innovations make some people hesitate interacting with such screens because of social embarrassment or privacy issues.
- D. Michelis and J. Müller (2011). The audience funnel: Observations of gesture based interaction with multiple large displays in a city center.
- Alt et al. (2011): Digifieds: Insights into deploying digital public notice areas in the wild.
- Alt et al. (2012): How to Evaluate Public Displays
8. Distributed Recommender Systems and User Interfaces (Presenter: Manuel Benjamin Neuberger, Advisor: Daniel Herzog)
Distributed user interfaces allow computer interfaces to be distributed across multiple devices, multiple users, and multiple platforms. Distributed user interfaces are a current topic in the research of computer science and human computer interaction but are already well-established in different scenarios. One examples is a recommender system where user can enter their movie preferences on a personal device, such as their smartphone, and a software running on a TV could choose and display a movie suitable for all users.
- Vanderdonckt (2010): Distributed User Interfaces: How to Distribute User Interface Elements across Users, Platforms, and Environments
- Elmqvist (2011): Distributed User Interfaces: State of the Art
- Abdrabo and Wörndl (2016): DiRec: A Distributed User Interface Video Recommender
9. Evaluating the User Experience and Usability of Recommender Systems (Presenter: Ahmed Abdelaziz, Advisor: Daniel Herzog)
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.
- Konstan and Riedl (2011): Recommender systems: from algorithms to user experience
- Knijnenburg et al. (2012): Explaining the user experience of recommender systems
- www.usability.gov/how-to-and-tools/methods/usability-evaluation/index.html
10. Tourism Recommender Systems (Presenter: Alexis Gamboa Soto, 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
11. Touristic Region Detection (Presenter: Sameera Thimbiri Palage, 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
12. Wearable Devices for Proactive Tourist Recommendations (Presenter: Rinita Roy, 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
13. Automatic Preference Elicitation from Social Media (Presenter: Ashmi Banerjee, 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
14. Affective Computing for Recommender Systems (Presenter: Marcello Feroce, 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
15. Privacy-Enhanced Recommender Systems (Presenter: Michael Bradt, Advisor: Wolfgang Wörndl)
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