NewsMe: A case study for adaptive news systems with open user model

Adaptive news systems have become important in recent years. A lot of work has been put into developing these adaptation processes. We describe here an adaptive news system application, which uses an open user model and allow users to manipulate their interest profiles. We also present a study of the system. Our results showed that user profile manipulation should be used with caution. © 2007 IEEE

On the Predictability of Talk Attendance at Academic Conferences

This paper focuses on the prediction of real-world talk attendances at academic conferences with respect to different influence factors. We study the predictability of talk attendances using real-world tracked face-to-face contacts. Furthermore, we investigate and discuss the predictive power of user interests extracted from the users' previous publications. We apply Hybrid Rooted PageRank, a state-of-the-art unsupervised machine learning method that combines information from different sources. Using this method, we analyze and discuss the predictive power of contact and interest networks separately and in combination. We find that contact and similarity networks achieve comparable results, and that combinations of different networks can only to a limited extend help to improve the prediction quality. For our experiments, we analyze the predictability of talk attendance at the ACM Conference on Hypertext and Hypermedia 2011 collected using the conference management system Conferator

Recommending research colloquia: A study of several sources for user profiling

The study reported in this paper is an attempt to improve content-based recommendation in CoMeT, a social system for sharing information about research colloquia in Carnegie Mellon and University of Pittsburgh campuses. To improve the quality of recommendation in CoMeT, we explored three additional sources for building user profiles: tags used by users to annotate CoMeT's talks, partial content of CiteULike papers bookmarked by users, and tags used to annotate CiteULike papers. We also compare different tag integration models to study the impact of information fusion on recommendations outcome. The results demonstrate that information encapsulated in CiteULike bookmarks generally helps to improve several aspects of recommendation. The addition of tags by fusing them into keyword profiles helps to improve precision and novelty of recommendation, but may harm systems ability to recommend generally interesting talks. The effects of tags and bookmarks appeared to be stackable. © 2010 ACM

Collaborative information finding in smaller communities: The case of research talks

Social navigation and social tagging technologies enable user communities to assemble the collective wisdom, and use it to help community members in finding the right information. However, it takes a significantly-sized community to make a social system truly useful. The question addressed in this paper is whether collaborative information finding is feasible in the context of smaller communities. To answer this question, we developed two social systems specifically focused on smaller communities - CoMeT and Conference Navigator II - and explored several techniques to increase the volume of user contributions. This paper reviews the explored techniques and presents empirical evidence that demonstrate their effectiveness. © 2010 ICST

Supporting conference attendees with visual decision making interfaces

Recent efforts in recommender systems research focus increasingly on human factors affecting recommendation acceptance, such as transparency and user control. In this paper, we present IntersectionExplorer, a scalable visualization to interleave the output of several recommender engines with user-contributed relevance information, such as bookmarks and tags. Two user studies at conferences indicate that this approach is well suited for technical audiences in smaller venues, and allowed the identification of applicability limitations for less technical audiences attending larger events. Copyright held by the owner/author(s)

Scalable exploration of relevance prospects to support decision making

Recent efforts in recommender systems research focus increasingly on human factors that affect acceptance of recommendations, such as user satisfaction, trust, transparency, and user control. In this paper, we present a scalable visualisation to interleave the output of several recommender engines with human-generated data, such as user bookmarks and tags. Such a visualisation enables users to explore which recommendations have been bookmarked by like-minded members of the community or marked with a specific relevant tag. Results of a preliminary user study (N =20) indicate that effectiveness and probability of item selection increase when users can explore relations between multiple recommendations and human feedback. In addition, perceived effectiveness and actual effectiveness of the recommendations as well as user trust into the recommendations are higher than a traditional list representation of recommendations

Topic-Relevance Map: Visualization for Improving Search Result Comprehension

Peer reviewe

User model in a box: Cross-system user model transfer for resolving cold start problems

Recommender systems face difficulty in cold-start scenarios where a new user has provided only few ratings. Improving cold-start performance is of great interest. At the same time, the growing number of adaptive systems makes it ever more likely that a new user in one system has already been a user in another system in related domains. To what extent can a user model built by one adaptive system help address a cold start problem in another system? We compare methods of cross-system user model transfer across two large real-life systems: we transfer user models built for information seeking of scientific articles in the SciNet exploratory search system, operating over tens of millions of articles, to perform cold-start recommendation of scientific talks in the CoMeT talk management system, operating over hundreds of talks. Our user study focuses on transfer of novel explicit open user models curated by the user during information seeking. Results show strong improvement in cold-start talk recommendation by transferring open user models, and also reveal why explicit open models work better in cross-domain context than traditional hidden implicit models

Social navigation support for groups in a community-based educational portal

This work seeks to enhance a user's experience in a digital library using group-based social navigation. Ensemble is a portal focusing on computing education as part of the US National Science Digital Library providing access to a large amount of learning materials and resources for education in Science, Technology, Engineering and Mathematics. With so many resources and so many contributing groups, we are seeking an effective way to guide users to find the right resource(s) by using group-based social navigation. This poster demonstrates how group-based social navigation can be used to extend digital library portals and how it can be used to guide portal users to valuable resources. © 2013 Springer-Verlag