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Using early view patterns to predict the popularity of youtube videos
TLDR
These proposed models lead to significant decreases in relative squared errors, reaching up to 20% reduction on average, and larger reductions for videos that experience a high peak in popularity in their early days followed by a sharp decrease in popularity.
A brief survey of automatic methods for author name disambiguation
TLDR
A taxonomy for characterizing the current author name disambiguation methods described in the literature is proposed, a brief survey of the most representative ones is presented and several open challenges are discussed.
Streams, structures, spaces, scenarios, societies (5s): A formal model for digital libraries
TLDR
The fundamental abstractions of Streams, Structures, Spaces, Scenarios, and Societies (5S), which allow us to define digital libraries rigorously and usefully, are proposed.
Detecting Spammers and Content Promoters in Online Video Social Networks
TLDR
This paper manually builds a test collection of real YouTube users, classifying them as spammers, promoters, and legitimates, and provides a characterization of social and content attributes that may help distinguish each user class.
SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods
TLDR
A benchmark comparison of twenty-four popular sentiment analysis methods, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles is presented, highlighting the extent to which the prediction performance of these methods varies considerably across datasets.
A source independent framework for research paper recommendation
TLDR
This paper proposes a novel source independent framework for research paper recommendation that requires as input only a single research paper and generates several potential queries by using terms in that paper, which are then submitted to existing Web information sources that hold research papers.
Exploiting user feedback to learn to rank answers in q&a forums: a case study with stack overflow
TLDR
The authors' L2R method was trained to learn the answer rating, based on the feedback users give to answers in Q&A forums, and was able to outperform a state of the art baseline with gains of up to 21% in NDCG, a metric used to evaluate rankings.
Learning to advertise
TLDR
A new framework for associating ads with web pages based on Genetic Programming (GP), which aims at learning functions that select the most appropriate ads, given the contents of a Web page to optimize overall precision and minimize the number of misplacements.
Using web information for author name disambiguation
TLDR
Results show that the method yields results that outperform those of two state-of-the-art unsupervised methods and are statistically comparable with those of a supervised one, but requiring no training.
An unsupervised heuristic-based hierarchical method for name disambiguation in bibliographic citations
TLDR
The results show that the unsupervised method, when using all attributes, performs competitively against all other methods, under both metrics, loosing only in one case against a supervised method, whose result was very close to the authors'.
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