Paper at SIGIR 2012 — Language Intent Models for Inferring User Browsing Behavior

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1. I moved to Barcelona in the first week of September for a three-month internship at Yahoo! Research Labs. I’m very excited about it, and I’m looking forward to getting to know the people here and the problems they are working on.

   Update: My work at Yahoo! Research Labs resulted in a publication at SIGIR 2012 [1]; see [post] for the abstract(20120623-sigir2012-language-intent-models.md).

   References

   [1] Manos Tsagkias and Roi Blanco. 2012. Language intent models for inferring user browsing behavior. In Proceedings of the 35^th international ACM SIGIR conference on research and development in information retrieval (SIGIR ‘12). Association for Computing Machinery, New York, NY, USA, 335–344. ACM Link. PDF

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2. modeling. We correct for this discrepancy by introducing two hypergeometric language models for modeling both queries, and documents to be retrieved.

   In the second part, we focus on predicting behavior. First we look at predicting listeners’ preference in spoken user generated content, namely, podcasts. Then, we predict popularity of news articles from several news agents in terms of the volume of comments they receive. We develop models for predicting the popularity of an article for both before and after it is published. Finally, we look at a different aspect of news impact: how reading a news article affects future user browsing behavior. In each setting, we find patterns that characterize the underlying behavior and extract features that we then use to establish models for predicting online behavior.

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3. Abstract

   On-line news agents provide commenting facilities for readers to express their views with regard to news stories. The number of user supplied comments on a news article may be indicative of its importance or impact. We report on exploratory work that predicts the comment volume of news articles prior to publication using five feature sets. We address the prediction task as a two stage classification task: a binary classification identifies articles with the potential to receive comments, and a second binary classification receives the output from the first step to label articles “low” or “high” comment volume. The results show solid performance for the former task, while performance degrades for the latter.

   References

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4. Much of what is discussed in social media is inspired by events in the news and, vice versa, social media provide us with a handle on the impact of news events. We address the following linking task: given a news article, find social media utterances that implicitly reference it. We follow a three-step approach: we derive multiple query models from a given source news article, which are then used to retrieve utterances from a target social media index, resulting in multiple ranked lists that we then merge using data fusion techniques. Query models are created by exploiting the structure of the source article and by using explicitly linked social media utterances that discuss the source article. To combat query drift resulting from the large volume of text, either in the source news article itself or in social media utterances explicitly linked to it, we introduce a graph-based method for selecting discriminative terms.  

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5. Abstract

   In this paper we describe how Twitter is used in various languages. We observe notable differences between languages regarding the use of hashtags, links, mentions, and conversations. We propose two dimensions that can be used to classify languages, each of which is likely to require different ways of analysis.

   References

   [1] Wouter Weerkamp, Simon Carter, and Manos Tsagkias. 2011. How People use Twitter in Different Languages. In Web Science 2011. UVA Link; PDF.

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6. Republished article finding is the task of identifying instances of articles that have been published in one source and republished more or less verbatim in another source, which is often a social media source. We address this task as an ad hoc retrieval problem, using the source article as a query. Our approach is based on language modeling. We revisit the assumptions underlying the unigram language model taking into account the fact that in our setup queries are as long as complete news articles. We argue that in this case, the underlying generative assumption of sampling words from a document with replacement, i.e., the multinomial modeling of documents, produces less accurate query likelihood estimates.  

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7. Abstract

   Podcasts display an unevenness characteristic of domains dominated by user generated content, resulting in potentially radical variation of the user preference they enjoy. We report on work that uses easily extractable surface features of podcasts in order to achieve solid performance on two podcast preference prediction tasks: classification of preferred vs. non-preferred podcasts and ranking podcasts by level of preference. We identify features with good discriminative potential by carrying out manual data analysis, resulting in a refinement of the indicators of an existent podcast preference framework. Our preference prediction is useful for topic-independent ranking of podcasts, and can be used to support download suggestion or collection browsing.

   References

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