Denis Parra

Sparse Linear Methods (SLIM) are state-of-the-art recommendation
approaches based on matrix factorization, which rely on a regularized
!1-norm and !2-norm optimization –an alternative optimization
problem to the traditional Frobenious norm. Although
they have shown outstanding performance in Top-N recommendation,
existent works have not yet analyzed some inherent assumptions
that can have an important effect on the performance of these
algorithms. In this paper, we attempt to improve the performance
of SLIM by proposing a generalized formulation of the aforementioned
assumptions. Instead of directly learning a sparse representation
of the user-item matrix, we (i) learn the latent factors’ matrix
of the users and the items via a traditional matrix factorization approach,
and then (ii) reconstruct the latent user or item matrix via
prototypes which are learned using sparse coding, an alternative
SLIM commonly used in the image processing domain. The results
show that by tuning the parameters of our generalized model
we are able to outperform SLIM in several Top-N recommendation
experiments conducted on two different datasets, using both nDCG
and nDCG@10 as evaluation metrics. These preliminary results,
although not conclusive, indicate a promising line of research to
improve the performance of SLIM recommendation.

Abstract Social learning has confirmed its value in enhancing the learning outcomes across a wide spectrum. To support social learning, a visual approach is a common technique to represent and organize multiple students' data in an informative way. This paper presents a design of comparative social visualization for E-learning, which encourages information discovery and social comparisons. Classroom studies confirmed the motivational impact of personalized social guidance provided by the visualization in the target context. The ...

As the sheer volume of information grows, information overload challenges users in many ways. Large conferences are one of the venues suffering from this overload. Faced with several parallel sessions and large volumes of papers covering diverse areas of interest, conference participants often struggle to identify the most relevant sessions to attend.

Abstract—Social tagging systems pose new challenges to developers of recommender systems. As observed by recent research, traditional implementations of classic recommender approaches, such as collaborative filtering, are not working well in this new context. To address these challenges, a number of research groups worldwide work on adapting these approaches to the specific nature of social tagging systems.

Abstract The availability of social tags has greatly enhanced access to information. Tag clouds have emerged as a new" social" way to find and visualize information, providing both one-click access to information and a snapshot of the" aboutness" of a tagged collection. A range of research projects explored and compared different tag artifacts for information access ranging from regular tag clouds to tag hierarchies.

ABSTRACT One common dichotomy faced in recommender systems is that explicit user feedback-in the form of ratings, tags, or user-provided personal information-is scarce, yet the most popular source of information in most state-of-the-art recommendation algorithms, and on the other side, implicit user feedback-such as numbers of clicks, playcounts, or web pages visited in a session-is more frequently available, but there are fewer methods well studied to provide recommendations based on this kind of information.

Abstract Social learning has confirmed its value in enhancing the learning outcomes across a wide spectrum. To support social learning, a visual approach is a common technique to represent and organize multiple students' data in an informative way. This paper presents a design of comparative social visualization for E-learning, which encourages information discovery and social comparisons. Classroom studies confirmed the motivational impact of personalized social guidance provided by the visualization in the target context.

ABSTRACT In this paper we present a system that recommends online comments written by teachers–suggestions of teachers to their peers-about their experience conducting educational activities in an online educational community called Kelluwen. In Kelluwen, the teachers build, use and share collaborative didactical designs whose educational activities are based on Social Web tools.

Understanding user preferences or taste is an important goal in several of the application areas of user modeling such as personalisation or recommendation. Most of the approaches rely on having explicit feedback from users such as ratings to items or lists of interests. However, in many real-life situations we need to rely on implicit feedback such as the amount of times a user has bought a kind of item or listened to a song. The few approaches that can use such input rely on some assumptions that have not been validated through user studies and data analysis. In this work we aim at analyzing the relation between implicit feedback and explicit ratings to items. With this goal in mind, we conduct a user experiment in the music domain. We find thatthere is a strong relation between implicit feedback and ratings. Furthermore, we analyze the effect of other variables and find that only recentness – i.e. time ellapsed since the user interacted with the item being rated – has a significant effect. Using regression analysis, we propose a simple linear model that relates these variables to the rating we can expect to an item. Such model would allow to easily adapt any existing approach using explicit feedback to the implicit case.