A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Computational Science & Engineering
A novel document retrieval method using the discrete wavelet transform
ACM Transactions on Information Systems (TOIS)
Recommending topics for self-descriptions in online user profiles
Proceedings of the 2008 ACM conference on Recommender systems
Do you know?: recommending people to invite into your social network
Proceedings of the 14th international conference on Intelligent user interfaces
Make new friends, but keep the old: recommending people on social networking sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Increasing engagement through early recommender intervention
Proceedings of the third ACM conference on Recommender systems
Social media recommendation based on people and tags
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
Finding useful users on twitter: twittomender the followee recommender
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Context-aware movie recommendation based on signal processing and machine learning
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
A multi-faceted user model for twitter
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
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In recent years, social networks have become one of the best ways to access information. The ease with which users connect to each other and the opportunity provided by Twitter and other social tools in order to follow person activities are increasing the use of such platforms for gathering information. The amount of available digital data is the core of the new challenges we now face. Social recommender systems can suggest both relevant content and users with common social interests. Our approach relies on a signal-based model, which explicitly includes a time dimension in the representation of the user interests. Specifically, this model takes advantage of a signal processing technique, namely, the wavelet transform, for defining an efficient pattern-based similarity function among users. Experimental comparisons with other approaches show the benefits of the proposed approach.