Probabilistic Partial User Model Similarity for Collaborative Filtering

Our current work on a probabilistic approach to compute partial user preference similarities was accepted and published at the 1st International Workshop on Inductive Reasoning and Machine Learning for the Semantic Web (IRMLeS) at the 6th European Semantic Web Conference (ESWC) 2009. The paper is available online at as volume 474. The presentation is available at the IRMLeS Web page.

The general idea is that people may share similar preferences only partially. For instance, a person may like Italian food like another person but not Chinese food. But the other person does like Chinese food. Traditional collaborative filtering computes global preference similarity and fail detect this relation. Our approach computes is able to compute partial preference similarities on the basis of hypothesized user preferences. The hypothesized user preferences are learned applying traditional machine learning algorithms. We could show, that our approach performs significantly better then traditional user-based collaborative filtering. Especially in cases where people have only few common rated items. The strength of our approach are the use of partial preference similarities and using hypothesized user preferences instead of item ratings that are always biased.

It was my first real presentation at a conference and it was a great success. I got very positive feedback on it. But I also noticed that a too fancy presentation may irritate some people. Well, I just had the new version of Keynote installed on my Mac and thus, I had to try out the new fancy features. This workshop was one of the most successful at the this year’s ESWC measured by number of participants. I also enjoyed the workshop dinner where I participated interesting discussion on artificial intelligence, data mining in practice, football and Shakespeare.

At the conference, I got in touch with some very interesting people. Especially at the very well organised poster session and after the conference dinner. Unfortunately, it was the last European Semantic Web Conference because the organizers decided to have the industry as main target. Thus, the abbreviation of ESWC stands now for the Extended Semantic Web Conference.


Recommender systems play an important role in supporting people getting items they like. One type of recommender systems is user-based collaborative filtering. The fundamental assumption of user-based collaborative filtering is that people who share similar preferences for common items behave similar in the future. The similarity of user preferences is computed globally on common rated items such that partial preference similarities might be missed. Consequently, valuable ratings of partially similar users are ignored. Furthermore, two users may even have similar preferences but the set of common rated items is too small to infer preference similarity. We propose first, an approach that computes user preference similarities based on learned user preference models and second, we propose a method to compute partial user preference similarities based on partial user model similarities. For users with few common rated items, we show that user similarity based on preferences significantly outperforms user similarity based on common rated items.


In the following, you can watch my paper presentation I gave at the IRMLeS workshop:

Presentation at the IRMLeS workshop


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Evaluation of the Adoption of New Features in a Web-Based Social Network

Together with Marc Vontobel’s main advisor Gerald Reif, I successfully advised and supported Marc with his bachelor thesis. He was my first student I advised at all. He reenginered Purple Leaf – a party portal – and investigated the impact of social relations among people with respect to acceptance rate of new features on a web page. At the time he finished with his thesis, the community of Purple Leaf consisted of several thousands of people.
It seems that one rather accepts and adopts a new feature when a friend already adopted it. It seems that the inhibition threshold is much lower due to the fact, that people trust their friends and thus, trust the feature and it added value. Marc did a great job that could not be expected from a Bachelor student, starting from the software engineering skills to the data gathering and complex Social Network Analysis (SNA). But he was already prepared from our precedent seminar on “Trust and Recommendation in Social Networks”.

We were surprised as he delivered not only his thesis but also a complete picture book with fantastic analysis of differenc aspects of his party community. He discovered the core group of party people being on most parties, the latent semantics among different music styles and drinks.


Purple Leaf is a social network which offers its member several possibilities to personalize its exclusive events by providing them unique online services. After the size of our platform suddenly increased from 300 initially invited guests to a multiple, we were obliged to completely revise the platform and enlarge our range of services. To embed these new services smoothly into the existing web presence, we fully restructured the application and changed the basis to a modern web framework. After that makeover, we designed five other services which we targeted to increase the customer loyalty and the entertainment value of our platform. Because new features are often not instantly accepted by existing users, we developed an integrated concept for boosting the acceptance of novel functionality. This concept is based on the technology acceptance model which was developed by Davis (1986). The model postulates that the actual use of a new feature is solely based on external factors. On the one hand, there are factors which influence the ‘perceived ease-of-use’ and on the other hand some that have impact on the ‘perceived usefulness’. In order to foster the perceived ease-of-use, we developed several usability concepts and tried to figure out how Web 2.0 features can help to simplify different processes. Beside the creation of intuitive user interfaces and plain procedures, we worked on an elaborated data and application structure which itself also contributed a big part to the simplicity of the new functionality. After we had embedded the services into our Internet portal, we started to analyze the acceptance of one new feature: ‘The most favored Guest’. This service allows every sign up member to define his personal list of favored guests for an upcoming event. Once the selected users are informed about their election, they, in turn, have the chance to define their own list. After a first round of selection, we tried to boost the personal acceptance of our members by providing specific incentives. Beside the active interventions into the process of adoption, we also analyzed a passive phenomenon: Does some kind of peer pressure exist within virtual cliques? If so, there might emerge some interesting changes in common marketing strategies which could narrow down the target audience to some single users of the network. In addition, we visualized some of the encountered situations and putted them together in an illustrated book as supplement to this paper.

Marc Vontobel: “Purple Leaf – Evaluation of the Adoption of New Features in a Web-Based Social Network”, ed. by Gerald Reif, Amancio Bouza, Harald C. Gall, University of Zurich, December 2008. (bachelorsthesis)

Expert Identification Based on E-Mail Analysis

Answer distribution of different authors

Answer distribution of different authors

Dieses Semester hatte ich das Seminar Informationsmanagement am Institut für Informatik an der Universität Zürich belegt. Das Thema des Seminars war “empirische Forschung in der Wirtschaftsinformatik”. Während dem Semester hatten alle Teilnehmer eine Seminarbeit verfasst. Heute und morgen präsentieren die Seminarteilnehmer ihre Arbeit und Resultate.
Meine Präsentation fand schon heute morgen statt von 10:30-11:15. Meine Präsentation wurde zeitlich etwas nach vorne geschoben, da ein paar Teilnehmer und ich selber um 12Uhr noch eine Prüfung in “Innovations und Technologiemanagement” ablegen mussten. Aber das ist ja nur Nebensache ;).
Mein Thema war Dokumentenanalyse: Expertenbestimmung durch E-Mail-Analyse. Die Präsentation verlief sehr gut. Nach der Präsentation überhäufte mich mein Professor mit Komplimenten. Er war begeistert wie methodisch ich bei der Analyse der Fragestellung vorgegangen bin und sagte sogar, dass so manche Redner an einer wissenschaftlichen Konferenz mit weit weniger auftreten. Nach seinem Kommentar war ich natürlich mächtig stolz auf meine Leistung und die Euphorie übertrug sich dann auch auf die gleich anschliessende Prüfung. Da meine Seminararbeit eigentlich bereits eine kleine Forschungsarbeit war möchte er, dass ich noch etwas mehr in meine Arbeit stecke, um die Resultate anschliessend an einer wissenschaftlichen Konferenz zu publizieren.
Hui, ich bin ja so was von durch den Wind über diese neue Perspektive. Mein erstes Paper ist zum greifen nah!!!