The Journal of Machine Learning Research
Proceedings of the 10th international conference on Intelligent user interfaces
Utilizing Physical and Social Context to Improve Recommender Systems
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Google Shared. A Case-Study in Social Search
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Social links from latent topics in Microblogs
WSA '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
Social and collaborative web search: an evaluation study
Proceedings of the 16th international conference on Intelligent user interfaces
Investigating topic models for social media user recommendation
Proceedings of the 20th international conference companion on World wide web
Terms of a feather: content-based news recommendation and discovery using twitter
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Rank and relevance in novelty and diversity metrics for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Power to the people: exploring neighbourhood formations in social recommender system
Proceedings of the fifth ACM conference on Recommender systems
TopicNets: Visual Analysis of Large Text Corpora with Topic Modeling
ACM Transactions on Intelligent Systems and Technology (TIST)
Modeling topic specific credibility on twitter
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Standard approaches of information retrieval are increasingly complemented by social search even when it comes to rational information needs. Twitter, as a popular source of real-time information, plays an important role in this respect, as both the follower-followee graph and the many relationships among users provide a rich set of information pieces about the social network. However, many hidden factors must be considered if social data are to successfully support the search for high-quality information. Here we focus on one of these factors, namely the relationship between content similarity and social distance in the social network. We compared two methods to compute content similarity among twitter users in a one-per-user document collection, one based on standard term frequency vectors, the other based on topic associations obtained by Latent Dirichlet Allocation (LDA). By comparing these metrics at different hop distances in the social graph we investigated the utility of prominent features such as Retweets and Hashtags as predictors of similarity, and demonstrated the advantages of topical proximity vs. textual similarity for friend recommendations.