iScore: Measuring the Interestingness of Articles in a Limited User Environment

Pon, Raymond K.; Cárdenas, Alfonso F.; Buttler, David; Critchlow, Terence

doi:10.1109/cidm.2007.368896

Cited by 17 publications

(26 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, because the definition of interestingness varies from user to user (Pon et al, 2007a) and may even change over time, it is not possible to use traditional offline feature selection algorithms, such as the ones described by Guyon and Eliseeff (2003), to identify which features are important before deploying the system. So, all features are included for classification.…”

Section: Ensemblesmentioning

confidence: 99%

“…There is no single attribute of a document that definitively identifies interesting articles. As a result, using only traditional IR techniques for document classification is not sufficient (Pon et al, 2007). 3.…”

Section: Interestingness Issuesmentioning

confidence: 99%

“…Even the Yahoo! news articles used by Pon et al (2007) address the problem of identifying interesting articles to a large community of users instead of a specific user. Further research in personalized news recommendation will need to be evaluated on a large test data collection that has been collected using many individual users.…”

Section: Future Trendsmentioning

confidence: 99%

“…Another approach uses a weighted majority algorithm to aggregate expert advice from an ensemble of classifiers to address concept drift in real-time ranking (Beckier & Arias, 2007). A simpler ensemble approach is taken by Pon et al (2007a). Different techniques, which are relevant to determining the "interestingness" of an article, are combined together as individual features for a naïve Bayesian classifier.…”

Section: Ensemblesmentioning

confidence: 99%

See 3 more Smart Citations

Measuring the Interestingness of News Articles

Pon

Cárdenas

Buttler

2009

Encyclopedia of Data Warehousing and Mining, Second Edition

Self Cite

View full text Add to dashboard Cite

An explosive growth of online news has taken place. Users are inundated with thousands of news articles, only some of which are interesting. A system to filter out uninteresting articles would aid users that need to read and analyze many articles daily, such as financial analysts and government officials. The most obvious approach for reducing the amount of information overload is to learn keywords of interest for a user (Carreira et al., 2004). Although filtering articles based on keywords removes many irrelevant articles, there are still many uninteresting articles that are highly relevant to keyword searches. A relevant article may not be interesting for various reasons, such as the article’s age or if it discusses an event that the user has already read about in other articles. Although it has been shown that collaborative filtering can aid in personalized recommendation systems (Wang et al., 2006), a large number of users is needed. In a limited user environment, such as a small group of analysts monitoring news events, collaborative filtering would be ineffective. The definition of what makes an article interesting – or its “interestingness” – varies from user to user and is continually evolving, calling for adaptable user personalization. Furthermore, due to the nature of news, most articles are uninteresting since many are similar or report events outside the scope of an individual’s concerns. There has been much work in news recommendation systems, but none have yet addressed the question of what makes an article interesting.

show abstract

Section: Ensemblesmentioning

confidence: 99%

Section: Interestingness Issuesmentioning

confidence: 99%

Section: Future Trendsmentioning

confidence: 99%

Section: Ensemblesmentioning

confidence: 99%

See 2 more Smart Citations

Measuring the Interestingness of News Articles

Pon

Cárdenas

Buttler

2009

Encyclopedia of Data Warehousing and Mining, Second Edition

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [2], we introduced iScore to address how interesting articles can be identified in a continuous stream of news articles. In iScore, a variety of information retrieval algorithms are used to identify interesting articles.…”

Section: Introductionmentioning

confidence: 99%

Online selection of parameters in the rocchio algorithm for identifying interesting news articles

Pon

Cárdenas

Buttler

2008

Proceedings of the 10th ACM Workshop on Web Information and Data Management

Self Cite

View full text Add to dashboard Cite

We show that users have different reading behavior when evaluating the interestingness of articles, calling for different parameter configurations for information retrieval algorithms for different users. Better recommendation results can be made if parameters for common information retrieval algorithms, such as the Rocchio algorithm, are learned dynamically instead of being statically fixed a priori. By dynamically learning good parameter configurations, Rocchio can adapt to differences in user behavior among users. We show that by adaptively learning online the parameters of a simple retrieval algorithm, similar recommendation performance can be achieved as more complex algorithms or algorithms that require extensive fine-tuning. Also we have also shon that online parameter learning can yield 10% better results than best performing filter from the TREC11 adaptive filter task.

show abstract

R U :-) or :-( ? Character- vs. Word-Gram Feature Selection for Sentiment Classification of OSN Corpora

Blamey

Crick

Oatley

2012

Research and Development in Intelligent Systems XXIX

View full text Add to dashboard Cite

Binary sentiment classification, or sentiment analysis, is the task of computing the sentiment of a document, i.e. whether it contains broadly positive or negative opinions. The topic is well-studied, and the intuitive approach of using words as classification features is the basis of most techniques documented in the literature. The alternative character n-gram language model has been applied successfully to a range of NLP tasks, but its effectiveness at sentiment classification seems to be under-investigated, and results are mixed. We present an investigation of the application of the character n-gram model to text classification of corpora from online social networks, the first such documented study, where text is known to be rich in so-called unnatural language, also introducing a novel corpus of Facebook photo comments. Despite hoping that the flexibility of the character n-gram approach would be well-suited to unnatural language phenomenon, we find little improvement over the baseline algorithms employing the word n-gram language model.

show abstract

iScore: Measuring the Interestingness of Articles in a Limited User Environment

Cited by 17 publications

References 29 publications

Measuring the Interestingness of News Articles

Measuring the Interestingness of News Articles

Online selection of parameters in the rocchio algorithm for identifying interesting news articles

R U :-) or :-( ? Character- vs. Word-Gram Feature Selection for Sentiment Classification of OSN Corpora

Contact Info

Product

Resources

About