A statistical interpretation of term specificity and its application in retrieval
Document retrieval systems
Conversation map: a content-based Usenet newsgroup browser
Proceedings of the 5th international conference on Intelligent user interfaces
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Proceedings of the 11th international conference on World Wide Web
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
HICSS '99 Proceedings of the Thirty-Second Annual Hawaii International Conference on System Sciences-Volume 2 - Volume 2
The Journal of Machine Learning Research
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Conversational Collaborative Recommendation --- An Experimental Analysis
Artificial Intelligence Review
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Hybrid critiquing-based recommender systems
Proceedings of the 12th international conference on Intelligent user interfaces
Learning and adaptivity in interactive recommender systems
Proceedings of the ninth international conference on Electronic commerce
Conversational recommenders with adaptive suggestions
Proceedings of the 2007 ACM conference on Recommender systems
Improving new user recommendations with rule-based induction on cold user data
Proceedings of the 2007 ACM conference on Recommender systems
PeerChooser: visual interactive recommendation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Generating summary keywords for emails using topics
Proceedings of the 13th international conference on Intelligent user interfaces
Tied boltzmann machines for cold start recommendations
Proceedings of the 2008 ACM conference on Recommender systems
Tagsplanations: explaining recommendations using tags
Proceedings of the 14th international conference on Intelligent user interfaces
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
Personalized recommendation of social software items based on social relations
Proceedings of the third ACM conference on Recommender systems
MoviExplain: a recommender system with explanations
Proceedings of the third ACM conference on Recommender systems
Using twitter to recommend real-time topical news
Proceedings of the third ACM conference on Recommender systems
Topic and keyword re-ranking for LDA-based topic modeling
Proceedings of the 18th ACM conference on Information and knowledge management
Chinese online communities: balancing managementcontrol and individual autonomy
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Smarter social collaboration at IBM research
Proceedings of the ACM 2011 conference on Computer supported cooperative work
Using latent topics to enhance search and recommendation in Enterprise Social Software
Expert Systems with Applications: An International Journal
Inspectability and control in social recommenders
Proceedings of the sixth ACM conference on Recommender systems
Beyond lists: studying the effect of different recommendation visualizations
Proceedings of the sixth ACM conference on Recommender systems
Visualizing recommendations to support exploration, transparency and controllability
Proceedings of the 2013 international conference on Intelligent user interfaces
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Content-centric social websites, such as discussion forums and blog sites, have flourished during the past several years. These sites often contain overwhelming amounts of information that are also being updated rapidly. To help users locate their interests at such sites (e.g., interesting blogs to read or discussion forums to join), researchers have developed a number of recommendation technologies. However, it is difficult to make effective recommendations for new users (a.k.a. the cold start problem) due to a lack of user information (e.g., preferences and interests). Furthermore, the complexity of recommendation algorithms often prevents users from comprehending let alone trusting the recommended results. To tackle the above two challenges, we are building a social map-based recommender system called Pharos. A social map summarizes users' content-related social behavior over time (e.g., reading, writing, and commenting behavior during the past week) as a set of latent communities. Each community is characterized by the theme of the content being discussed and the key people involved. By discovering, ranking, and displaying the most "popular" latent communities, Pharos creates a visual social map of a website. This enables new users to obtain a quick overview of the site, alleviating the cold start problem. Furthermore, we use the social map as a context to help explain Pharos-recommended content and people. Users can also interactively explore the social map to locate their interested content or people that are not being explicitly recommended, compensating for the imperfection in the recommendation algorithms. We have deployed Pharos within our company and our preliminary evaluation shows the usefulness of Pharos.