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  • × author_ss:"Chen, Y."
  • × author_ss:"Wang, C."
  1. Wang, C.; Zhao, S.; Kalra, A.; Borcea, C.; Chen, Y.: Predictive models and analysis for webpage depth-level dwell time (2018) 0.01 
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    Abstract
    A half of online display ads are not rendered viewable because the users do not scroll deep enough or spend sufficient time at the page depth where the ads are placed. In order to increase the marketing efficiency and ad effectiveness, there is a strong demand for viewability prediction from both advertisers and publishers. This paper aims to predict the dwell time for a given urn:x-wiley:23301635:media:asi24025:asi24025-math-0001 triplet based on historic data collected by publishers. This problem is difficult because of user behavior variability and data sparsity. To solve it, we propose predictive models based on Factorization Machines and Field-aware Factorization Machines in order to overcome the data sparsity issue and provide flexibility to add auxiliary information such as the visible area of a user's browser. In addition, we leverage the prior dwell time behavior of the user within the current page view, that is, time series information, to further improve the proposed models. Experimental results using data from a large web publisher demonstrate that the proposed models outperform comparison models. Also, the results show that adding time series information further improves the performance.
    Source
    Journal of the Association for Information Science and Technology. 69(2018) no.8, S.1007-1022
    Type
    a
  2. Ackerman, B.; Wang, C.; Chen, Y.: ¬A session-specific opportunity cost model for rank-oriented recommendation (2018) 0.01 
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    Abstract
    Recommender systems are changing the way that people find information, products, and even other people. This paper studies the problem of leveraging the context of the items presented to the user in a user/system interaction session to improve the recommender system's ranking prediction. We propose a novel model that incorporates the opportunity cost of giving up the other items in the session and computes session-specific relevance values for items for context-aware recommendation. The model can work on a variety of different problems settings with emphasis on implicit user feedback as it supports varying levels of ordinal relevance. Experimental evaluation demonstrates the advantages of our new model with respect to the ranking quality.
    Source
    Journal of the Association for Information Science and Technology. 69(2018) no.10, S.1259-1270
    Type
    a

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