The Journal of Machine Learning Research
Lucene in Action (In Action series)
Lucene in Action (In Action series)
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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In this paper we consider the application of content-based recommendation techniques to web curation services which allow users to curate and share topical collections of content (e.g. images, news, web pages etc.). Curation services like Pinterest are now a mainstay of the modern web and present a range of interesting recommendation challenges. In this paper we consider the task of recommending collections to users and evaluate a range of different content-based techniques across a variety of content signals. We present the results of a large-scale evaluation using data from the Scoop.it web page curation service