Alexey Drutsa
Pavel Serdyukov
ABSTRACT
Nowadays, billions of people use the Web in connection with their daily needs. A significant part of the needs are constituted by search tasks that are usually addressed by search engines. Thus, daily search needs result in regular user engagement with a search engine. User engagement with web sites and services was studied in various aspects, but there appear to be no studies of its regularity and periodicity. In this paper, we studied periodicity of the user engagement with a popular search engine through applying spectrum analysis to temporal sequences of different engagement metrics. We found periodicity patterns of user engagement and revealed classes of users whose periodicity patterns do not change over a long period of time. In addition, we used the spectrum series as metrics to evaluate search quality.
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Index Terms
- Engagement Periodicity in Search Engine Usage: Analysis and its Application to Search Quality Evaluation
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Periodicity in User Engagement with a Search Engine and Its Application to Online Controlled Experiments
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Reviews
Bálint Molnár
In the recent cyberspace environment, the intensive and extensive use of search engines is commonplace. Tracking usage patterns of users and then aligning with the implicit user requirements is an essential competition factor among search engine manufacturers. There are several approaches for positive feedback to monitor the impact of freshly introduced changes on users. This paper proposes a mathematically grounded method for analysis of user behavior. The research combines the discrete Fourier transformation that is used for the analysis of frequencies of user activities and traditional statistical methods, for example, cluster analysis. The method is applied to investigate the effects of modifications in a specific search engine. The researchers use the log file of a search engine and therefore have a statistically significant set of data. Frequency or periodicity can be examined with the help of the discrete Fourier transformation method, which provides the transformation between the frequency and spectrum domain. The transformation allows for the application of cluster analysis whereby the typical patterns of user behavior can be explored. The conclusion of the research is that the introduced periodicity metrics along with the proposed analysis method are much better than previously used basic/naive metrics. The paper is interesting for researchers and professionals interested in social networks, search engines, and text and data mining methods. Online Computing Reviews Service
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