Designing Adaptive Engagement Approaches for Audience-bounded Online Communities

Audience-bounded online communities require innovative user engagement techniques. Without special efforts focus- ing on engagement, the contribution volume will likely to be insufficient to maintain a sustainable community-driven sys- tem. This paper presents an adaptive approach for user en- gagement that aims to apply alternative engagement strate- gies to users with different behavior in the online community. We report the results of the first experiments testing the fea- sibility of such approach. We discuss further design options that can be explored and the implications of the approach

Of course we share! Testing Assumptions about Social Tagging Systems

Social tagging systems have established themselves as an important part in today's web and have attracted the interest from our research community in a variety of investigations. The overall vision of our community is that simply through interactions with the system, i.e., through tagging and sharing of resources, users would contribute to building useful semantic structures as well as resource indexes using uncontrolled vocabulary not only due to the easy-to-use mechanics. Henceforth, a variety of assumptions about social tagging systems have emerged, yet testing them has been difficult due to the absence of suitable data. In this work we thoroughly investigate three available assumptions - e.g., is a tagging system really social? - by examining live log data gathered from the real-world public social tagging system BibSonomy. Our empirical results indicate that while some of these assumptions hold to a certain extent, other assumptions need to be reflected and viewed in a very critical light. Our observations have implications for the design of future search and other algorithms to better reflect the actual user behavior

Final report, independent Study during Fall 2009 "Improving Collaborative Filtering in Social Tagging Systems for the Recommendation of Scientific Articles"

This report describes our study of different ways to improve existing collaborative filtering techniques in order to recommend scientific articles. Using data crawled from CiteUlike, a collaborative tagging service for academic purposes, we compared the classical user-based collaborative filtering algorithm as described by Schafer et al. [2], with two enhanced variations: 1) using a tag-based similarity calculation, to avoid depending on ratings to find the neighborhood of a user, and 2) incorporate the amount of raters in the final recommendation ranking to decrease the noise of items that have been rated by too few users. We provide a discussion of our results, describing the dataset and highlighting our findings about applying collaborative filtering on folksonomies instead of the classic bipartite user-item network, and providing guidelines of our future research

The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0

Semantic Grounding Strategies for Tagbased Recommender Systems

Recommender systems usually operate on similarities between recommended items or users. Tag based recommender systems utilize similarities on tags. The tags are however mostly free user entered phrases. Therefore, similarities computed without their semantic groundings might lead to less relevant recommendations. In this paper, we study a semantic grounding used for tag similarity calculus. We show a comprehensive analysis of semantic grounding given by 20 ontologies from different domains. The study besides other things reveals that currently available OWL ontologies are very narrow and the percentage of the similarity expansions is rather small. WordNet scores slightly better as it is broader but not much as it does not support several semantic relationships. Furthermore, the study reveals that even with such number of expansions, the recommendations change considerably.Comment: 13 pages, 5 figure