PAWS Meeting April 1st, 2014

Happy April Fool!

Today Chirayu was presenting the progress of his thesis, "Using External Sources to Improve Research Talk Recommendation in Small Communities". The keyword-based Content-based and Content-boosted Collaborative Filtering models work pretty well. In contrast, The SVD-based in (25,75, and 100 latent topics) ones underperform below the keyword-based ones in the baseline and other three external source models.

While both techniques perform decently, clustering the keyword-based and SVD-based (100 latent topics) performed very poorly. The group has suggested to explored on more latent topics (25 - 1000 topic) and tried on the spatial clustering technique, in which Julio and Sherry used it in the other related work and performed very well in the very spare dataset, comparing with other techniques. Sherry also suggested to try to use only certain portions of the external sources, for example, the first page of homepage,  the latest k bibliographies, or the latest k external academic bookmarked articles.

PAWS meeting - Apr 15, 2014

PAWS meeting - Apr 15, 2014

In today's meeting, Roya presented her work on dynamic linking of programming contents. She has designed and conducted studies to investigate the effect of two dynamic linking approaches based on concept-based similarity calculation. The value of personalization was explored in that context as well.

PAWS meeting - Nov 10, 2009

In today's meeting, there were two presentations. In the first one Rosta presented an introduction to the theory behind people's participation in organizations, which comes mainly from the organizational domain. Following, she presented a study, conducted in two steps, about socialization tactics on several WikiPage projects. The study was focused on the participation of contributors (not on viewers). In the first part of the study it was observed the behavior of the users. in the second part was measured the impact of socialization techniques specially on newcomers. One interesting result was that personalized messages produced more participation than standardized messages on newcomers.

In the second turn, Denis presented the paper "The Effect of Correlation Coefficients on Communities of Recommenders". This paper was written by Lathia, Hailes and Capra from the University College London. They compare different measures of similarity showing their distribution (using MovieLens as dataset) and comparing their accuracy (MAE) and coverage results.

They show an interesting result on their study: that the similarity coefficients don't have a significant impact on the accuracy metrics compared to the neighborhood size. The experiments show that in some cases, using a random similarity measure between users can result in better accuracy of item prediction whether a large number of users have been used in the neighborhood. At the end of the paper there's a discussion about these non-expected results. Between 3 of them (criticism to accuracy metrics, data sparsity and the use of user-based similarity) they highlight that their results show a lack of support to the user-based similarity as a measure to capture an important factor on providing recommendations. So far, different similarity metrics show different rankings and distributions, but none of them extensively outperform any other.

One open question is: Should this result be extrapolated to any other dataset? Other questions: Which kind of similarity measure could help to capture better the concept of "word-of-mouth"? Do we really know what the ratings mean and how to use them to provide better recommendations?

PAWS meeting - Oct 20, 2009

Summer project at Telefonica: Expert-based recommendation system

Jae-wook introduced his project at Telefonica summer internship. The project is called Wisdom of the Few, which is an expert-based recommendation system. The original idea of the expert-recommendation was presented at SIGIR 2009. This time the study was extended aiming at the following objectives.

• Re-examine the idea using the music dataset collected from Metacritic.
• Build a client-based UM and recommendation, so that it can solve several issues such as privacy and scalability.
• Make a user interface using Adobe AIR, which can be ported to different platform.

We will do the evaluation soon, in the winter 2009.

PAWS meeting - Sep 8, 2009

The second meeting of the Fall 2009 semester is devoted to discussing selected papers from the UMAP 2009 conference. The two papers presented are focusing on mobile recommender systems.

Paper 1. PBohnert, F. and Zukerman, I. (2009). Non-intrusive personalisation of the museum experience. In Houben, G.-J., McCalla, G. I., Pianesi, F., and Zancanaro, M., (Eds.), 17th International Conference on User Modeling, Adaptation, and Personalization (UMAP 2009), pp. 307–318, Trento, Italy.

Authors utilize a special hand-operated tool - Geckotracker to obtain tracking data from visitors of Melbourne Museum. In particular exhibits of interest and viewing times are collected. The data is then analyzed in order to build a prediction model, capable of recommending new unvisited exhibits to see. Actual log viewing times are used as a primary measure of user interest. Several competing models are built. Leave one out method is used to estimate models' performance.

Paper 2. Partridge, K. and Price, B. (2009). Enhancing mobile recommender systems with activity inference. In Houben, G.-J., McCalla, G. I., Pianesi, F., and Zancanaro, M., (Eds.), 17th International Conference on User Modeling, Adaptation, and Personalization (UMAP 2009), pp. 307–318, Trento, Italy.

Paper focuses on mobile activity recommender system Magitti. Magitti recommends 5 classes of activities: eat, shop, do, see, and read. The data used for building several alternative recommender models was provided by the Japan Statistics Bureau and has data from 10 000 people reporting their activity every 15 min during one whole day. Recommender models take into account several factors, including location, surrounding venues, time of the day, personal calendar, etc. Magitti has gone though a small scale evaluation by 11 researchers and administrative staff users. Results show that a combination of location-based and personal-activity-pattern models works best.

Also discussed:
- UMUAI Special Issue on Educational Data Mining
- UMAP 2009 Proceedings

PAWS meeting - Apr 15, 2009

Jae-wook presenting paper Combining document representations for known item search by Paul Ogilvie and Jamie Callan

The paper investigates the pre-conditions for successful combination of document representations formed from structural markup for the task of known-item search. As this task is very similar to work in meta-search and data fusion, we adapt several hypotheses from those research areas and investigate them in this context. To investigate these hypotheses, we present a mixture-based language model and also examine many of the current meta-search algorithms. We find that compatible output from systems is important for successful combination of document representations. We also demonstrate that combining low performing document representations can improve performance, but not consistently. We find that the techniques best suited for this task are robust to the inclusion of poorly performing document representations. We also explore the role of variance of results across systems and its impact on the performance of fusion, with the surprising result that the correct documents have higher variance across document representations than highly ranking incorrect documents.

Dhruba Baishya presenting a set of innovative visualization techniques, including:

- eigen factor score
- dewey circles
- ny times api
- flickr ecosystem
- ted sphere
- radial social network
- knowledge network
- author co-citation
- euro2004
- web trend map
- los ojos del mundo
- botanical tree
- tagging behavior in nicovideo
- flickr group

Related links
- http://www.visualcomplexity.com/vc/
- http://infosthetics.com/
- http://developer.nytimes.com/visualizations_app/

PAWS Meeting (25 March, 2009)

1. Denis discussed his experiment on Recommendation for CiteULike.

2. Danielle presented a paper about “Tagsplanation” - best paper at IUI’09.
Authors investigated the use of tags for generating and explaining recommendation.

3. Tomek presented a paper that describes Document Summarization based on Eye-Tracking with Web-cam. The paper raised some concerns.

4. Katrina (from Switzerland ;-) introduced her research on culturally-adaptive user interfaces. The system is modeling users’ culture along several dimensions (nationality, religion, education, etc.) and tries to adjust its interface.