That didn’t solve the problem on my Mac (which runs Snow Leopard 10.6.5 with version 2.33 of TexShop 2.33. The standard PGF/TikZ installation that comes with TexShop insists on calling Gnuplot with the “set terminal table” command, which won’t work out of the box.

According to some quick googling, installing Gnuplot using Fink seems to alleviate the problem. However, an even easier solution is editing one of the PGF files that handles the Gnuplot calls. To do this edit the pgfmoduleplot.code.tex file in the following location:

The year (‘2009′) could differ on different installations. In this file, go to line 288 and remove the word ‘terminal’ from the following line

so it reads

Now everything should work just fine!

Social bookmarking websites are rapidly growing in popularity. Recommender systems, a promising remedy to the information overload accompanying the explosive growth in content, are designed to identify which unseen content might be of interest to a particular user, based on his or her past preferences. Most previous work in recommendation for social bookmarking suffers from a lack of comparisons between the different available approaches. In this article, we address this issue by comparing and evaluating eight recommendation approaches on four data sets from two domains. We find that approaches that use tag overlap and metadata provide better results for social bookmarking data sets than the transaction patterns that are used traditionally in recommender systems research. In addition, we investigate how to fuse together different recommendation approaches to further improve recommendation accuracy. We find that fusing recommendations can indeed produce significant improvements in recommendation accuracy. We also find that it is often better to combine approaches that use different data representations, such as tags and metadata, than it is to combine approaches that only vary in the algorithms they use. The best results are obtained when both of these aspects of the recommendation task are varied in the fusion process. Our findings can be used to improve the quality of recommendations not only on social bookmarking websites, but conceivably also on websites that offer annotated commercial content.

• Movie Recommendation using Random Walks over the Contextual Graph was accepted for the  CARS 2010 workshop on context-aware recommender systems
• dataTEL – Issues and Considerations regarding Sharable Data Sets for Recommender Systems in Technology Enhanced Learning was accepted for the RecSysTEL workshop. It’s joint effort with Hendrik Drachsler and nine other papers, describing the best practices for collecting social bookmarking data sets for technology-enhanced learning applications

This means I’ll be going to RecSys for the fifth year in a row. Previous editions were held in Bilbao, Minneapolis, Lausanne, and New York. This year’s edition in Barcelona promises to be another great installment!