TY - GEN
T1 - Social media-driven news personalization
AU - O'Banion, Shawn
AU - Birnbaum, Lawrence A
AU - Hammond, Kristian J
PY - 2012
Y1 - 2012
N2 - While social media have achieved significant and widespread adoption as platforms for sharing information, their use as a source of data for predicting user interests has not yet been fully explored. In this paper, we present a content-based approach to modeling user interests based on Twitter. Our recommendation system uses information retrieval techniques to represent tweets and users as collections of news topics, including high-level categories (e.g., sports, politics, business) and detailed subtopics (e.g., Chicago Bulls, Mitt Romney, entrepreneurship). We discuss the design of a system that uses this information to deliver news recommendations in the form of a personalized newspaper. Finally, we describe a novel method for evaluating recommendation sys- tems based on Twitter that involves mining Twitter data to identify explicit indicators of news interests and comparing these to retroactive system recommendations.
AB - While social media have achieved significant and widespread adoption as platforms for sharing information, their use as a source of data for predicting user interests has not yet been fully explored. In this paper, we present a content-based approach to modeling user interests based on Twitter. Our recommendation system uses information retrieval techniques to represent tweets and users as collections of news topics, including high-level categories (e.g., sports, politics, business) and detailed subtopics (e.g., Chicago Bulls, Mitt Romney, entrepreneurship). We discuss the design of a system that uses this information to deliver news recommendations in the form of a personalized newspaper. Finally, we describe a novel method for evaluating recommendation sys- tems based on Twitter that involves mining Twitter data to identify explicit indicators of news interests and comparing these to retroactive system recommendations.
KW - Information retrieval
KW - Personalization
KW - Recom- mendation system
KW - Social media analysis
KW - User profiling
UR - http://www.scopus.com/inward/record.url?scp=84867475449&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867475449&partnerID=8YFLogxK
U2 - 10.1145/2365934.2365943
DO - 10.1145/2365934.2365943
M3 - Conference contribution
AN - SCOPUS:84867475449
SN - 9781450316385
T3 - RSWeb'12 - Proceedings of the 4th ACM RecSys Workshop on Recommender Systems and the Social Web
SP - 45
EP - 51
BT - RSWeb'12 - Proceedings of the 4th ACM RecSys Workshop on Recommender Systems and the Social Web
T2 - 4th ACM RecSys Workshop on Recommender Systems and the Social Web, RSWeb 2012
Y2 - 9 September 2012 through 9 September 2012
ER -