NDLI: A study of machine learning methods for detecting user interest during web sessions

Content Provider	ACM Digital Library
Author	Dias, Hugo Jorge, Alípio M. Leal, José Paulo Anand, Sarabjot S.
Abstract	The ability to have an automated real time detection of user interest during a web session is very appealing and can be very useful for a number of web intelligence applications. Low level interaction events associated with user interest manifestations form the basis of user interest models. However such data sets present a number of challenges from a machine learning perspective, including the level of noise in the data and class imbalance (given that the majority of content will not be of interest to a user). In this paper we evaluate a large number of machine learning techniques aimed at learning from class imbalanced data using two data sets collected from a real user study. We use the AUC, recall, precision and model complexity to compare the relative merits of these techniques and conclude that useful models with AUC above 0.8 can be obtained using a mix of sampling and cost based methods. Ensemble models can provide further accuracy but make deployment more complex.
Starting Page	149
Ending Page	157
Page Count	9
File Format	PDF
ISBN	9781450326278
DOI	10.1145/2628194.2628239
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-07-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Imbalanced classification User interaction User interest Machine learning Modeling
Content Type	Text
Resource Type	Article

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A study of machine learning methods for detecting user interest during web sessions