Recent studies on people recommendation have focused on suggesting people the user already knows. In this work, we use social media behavioral data to recommend people the user is not likely to know, but nonetheless may be interested in. Our evaluation is based on an extensive user study with 516 participants within a large enterprise and includes both quantitative and qualitative results. We found that many employees valued the recommendations, even if only one or two of nine recommendations were interesting strangers. Based on these results, we discuss potential deployment routes and design implications for a stranger recommendation feature. Author Keywords People recommendation, social matching, socia
This thesis improves the process of recommending people to people in social networks using new clust...
Recommender systems are a means of personalizing the presentation of information to ensure that user...
We study personalized recommendation of social software items, including bookmarked web-pages, blog ...
Recent studies on people recommendation have focused on suggesting people the user already knows. In...
Recent studies on people recommendation have focused on suggesting people the user already knows. In...
Social recommender systems, such as “Who to follow” on Twitter, utilize approaches that recommend fr...
This paper studies people recommendations designed to help users find known, offline contacts and di...
As the amount of information on the web grows exponentially every year, users rely more and more on ...
In this paper we describe a novel UI and system for providing users with recommendations of people t...
Two main approaches to using social network infor-mation in recommendation have emerged: augmenting ...
People recommenders are a widespread feature of social networking sites and educational social learn...
We study personalized item recommendation within an enterprise social media application suite that i...
Unlike expert location systems which respond to users’ specific information needs, expert recommende...
It is well recognized that users rely on social media (e.g. Twitter or Digg) to fulfill two common n...
This study explores why recommendation seekers look for recommendations, and how they interact with ...
This thesis improves the process of recommending people to people in social networks using new clust...
Recommender systems are a means of personalizing the presentation of information to ensure that user...
We study personalized recommendation of social software items, including bookmarked web-pages, blog ...
Recent studies on people recommendation have focused on suggesting people the user already knows. In...
Recent studies on people recommendation have focused on suggesting people the user already knows. In...
Social recommender systems, such as “Who to follow” on Twitter, utilize approaches that recommend fr...
This paper studies people recommendations designed to help users find known, offline contacts and di...
As the amount of information on the web grows exponentially every year, users rely more and more on ...
In this paper we describe a novel UI and system for providing users with recommendations of people t...
Two main approaches to using social network infor-mation in recommendation have emerged: augmenting ...
People recommenders are a widespread feature of social networking sites and educational social learn...
We study personalized item recommendation within an enterprise social media application suite that i...
Unlike expert location systems which respond to users’ specific information needs, expert recommende...
It is well recognized that users rely on social media (e.g. Twitter or Digg) to fulfill two common n...
This study explores why recommendation seekers look for recommendations, and how they interact with ...
This thesis improves the process of recommending people to people in social networks using new clust...
Recommender systems are a means of personalizing the presentation of information to ensure that user...
We study personalized recommendation of social software items, including bookmarked web-pages, blog ...