Research

Interests

Through my research projects, I have tried to grasp why users behave so unpredictably, whilst seeking to improve their experience with recommenders. My investigations allowed me to work on the following themes.

Comparisons: I prefer A to B

Users are often asked to express a rating on a five-star scale. Unfortunately, ratings have been shown to introduce up to 40% of inconsistencies or noise. This is a serious issue: such data serves to create preference-models, which are in turn used to compute the recommendations. We proposed to replace ratings with a simple comparison modality (“I prefer A to B”). With multiple users-studies, we showed that our approach can be 20% more stable, and that users are in favour of relying on such an interaction.

 

Diversity among recommended items

Nearly all of the recommender research has focused on making algorithms which provide the most accurate suggestions. However, there is much more to users’ satisfaction than pure accuracy. I investigated diversity. For the first time, Dr. S. Castagnos and I were able to show that users actually need some diversity in order to make a confident purchase decision. We proposed a novel time-dependant diversity model.

Layout vs. content

I had the opportunity to analyse the respective effects of layout and content throughout my research. In the case of dynamic-critiquing for instance, Dr. J. Zhang and I created a collection of meaning-augmented icons, to replace textual critiques. The results were that users used them more frequently and felt it required less effort.

User acceptance, and possible adoption

Understanding what leads users to accepting (clicking on) recommended items, and later deciding to adopt this system (rather than another) has been, and remains one of my key interests. In order to decipher user’s motivation, I have designed and carried out over ten user studies, gathering over 600 participants’ feedback.

 

Intelligent systems

The user modelling community has long sought to make intelligent systems. The emergence of the Web has meant that users’ traces have become abundant (through log files for instance). I had the chance to work on users’ explicitly and implicitly expressed preferences, and at times I used an eye-tracker to establish links between users’ action and actual associated meaning.

Posters

Discover a selection of posters which I created for my research.

How Users Perceive and Accept Personalized Recommendations.
Poster presented at the UMAP09 conference, Trento, Italy. [ref] [poster]

LAMA: LAst.fm Musical Amplifier. This framework that I co-developed, gave the ability to quickly write new interfaces for music recommenders. Poster presented at the ”Invisible Computing : Novel Interfaces to Digital Environments” I&C research day, EPFL 2009. [poster]

User Issues in Recommender Systems: Acceptance, Involvement and trust. Poster presenting the initial research questions of my Ph.D. thesis. [poster]

 

Supervised Projects

Throughout my Ph.D. and post-doc, I had the pleasure of supervising the projects of the following excellent students:

  • Emeline Schmidt and Magali Kamalski. Developpment of a movie comparison framework, and running of three users-studies. (July-December 2010). Upcoming papers.
  • Thomas Girard. TagMatrix – a Data Visualization for TraceTrack. Improvements on the Lama Framework. (June 2009) [pdf]
  • Aurelia Rochat. Tomorrow’s music player: a spinning interface. (January 2009) [pdf]
  • Lucas Maystre: TraceTrack a Recommender System Interface That Displays Your Listening Habits. (January 2009) [pdf]
  • Ganesh Venkateshwaran. Mobile Critiquing Interface. (January 2008). [co-supervised with J. Zhang]
  • Joël Schintgen. Implementation of an Interactive travel Map. (January 2008) [co-supervised with L. Chen] [pdf]

Presentations

I enjoy discussing an presenting my work, and had the opportunity of talking at the following events.

  • Eye-Tracking Product Recommenders’ Usage. At the ACM International Conference on Recommender Systems, Barcelona, Spain (September 2010).
  • User Perceived Qualities and Acceptance of Recommender Systems: the Role of Diversity. Presentation of my thesis work. EPFL (April 2010) and LORIA, Nancy (July 2010).
  • Découvrir les interactions Homme-Machine. Open day for high school students, EPFL (March 2010) and Open day doctoral school, EPFL (March 2010).
  • Explicit and Implicit User Preferences, a Human Computer Interaction Perspective. At the Workshop on Semantic User Descriptions and their Influence on 3D graphics and VR, as part of the SEMINAIRE DU TROISIEME CYCLE ROMAND, VRLAB – EPFL (November 13, 2008)
  • User Technology Adoption Issues in Recommender Systems. At Networking and Electronic Commerce Research Conference, Lake Garda, Italy (October 18-21, 2007).

Miscellaneous

I take interest in reviewing papers and have been a Program Committee Member for the IUI09 conference.

During my Ph.D. I was a teaching assistant for my professor’s Human-Computer Interaction master course. I contributed to the elaboration of exercises and exams, guidance of student projects, and grading of the work. I also taught the course during two months (medical leave of my Prof.).

Comments are closed.