Ying Zhang's research while affiliated with Pennsylvania State University and other places

Publications (6)

Article
In this paper, we use time series analysis to evaluate predictive scenarios using search engine transactional logs. Our goal is to develop models for the analysis of searchers’ behaviors over time and investigate if time series analysis is a valid method for predicting relationships between searcher actions. Time series analysis is a method often u...
Article
In this research, we aim to identify factors that signif- icantly affect the clickthrough of Web searchers. Our underlying goal is determine more efficient methods to optimize the clickthrough rate. We devise a clickthrough metric for measuring customer satisfaction of search engine results using the number of links visited, number of queries a use...
Article
In this paper, we report ongoing efforts in a large scale research project to develop methods for profiling individual Web search engine users by leveraging data recorded in the transaction logs of search engines. Our research aim is to investigate how completely one can profile a Web searcher using log data. Taking a broad brush approach, we prese...
Article
This paper examines the effects of sponsored links' reputation. We pursued this study through a mature design of experiment method – the nested design – in order to investigate this fast growing segment of online advertising. In this study, we firstly analyze the adaptability of the nested design to find out whether the reputation of sponsored link...
Conference Paper
Full-text available
In this paper we investigate the effect of search engine brand (i.e., identifying name that distinguishes a product from its competitors) on evaluation of system performance. Our research is motivated by the large amount of search traffic directed to less than a handful of Web search engines, even though many are of equal technical quality with sim...
Conference Paper
We investigate the effect of search engine brand (i.e., the identifying name or logo that distinguishes a product from its competitors) on evaluation of system performance. This research is motivated by the large amount of search traffic directed to a handful of Web search engines, even though most are of equal technical quality with similar interf...

Citations

... 4.1 und 7.3). Im Suchmaschinenbereich zeigt sich eine starke Markenbindung; Untersuchungen haben gezeigt, dass Nutzer selbst dann ihre Lieblingssuchmaschine bevorzugen, wenn die Ergebnisse einer anderen Suchmaschine mit dem ihnen bekannten Layout und mit dem Markennamen der Lieblingssuchmaschine versehen werden (Jansen et al. 2007b). ...
... This work also highlights differences in search behavior of specific user classes. Jansen et al. [3] present approaches to identify a user's location , geographical interest, topic and level of interest or commercial intent. A closely related research area is the automated categorization (or classification) of texts into predefined categories where texts are usually represented as a bag of (extracted) keywords. ...
... These are the most commonly (e.g. Silverstein et al. (1999);Jansen et al. (2000); Bendersky and Croft (2009);Zhang et al. (2009);Taghavi et al. (2012)) presented statistics and are part of the analysis in our study. Taghavi et al. (2012) have shown two trends in web search query length and its distribution: increase in query length through the years. ...
... Es gibt weitere Faktoren, die die Testergebnisse verfälschen können. Insbesondere ist die Herkunft der Treffer (also von welcher Suchmaschine sie ursprünglich ausgegeben wurden) zu verschleiern, da sonst in der Bewertung starke Markeneffekte zu beobachten sind (Jansen et al. 2007a;Bailey und Thomas 2007). Weiterhin sollte die ursprüngliche Reihung der Treffer für die Juroren nicht sichtbar sein, um Lerneffekte auszuschließen (Bar-Ilan et al. 2009). ...
... Thus, the divergent positioning dynamics and polarization trend can be effectively avoided. In the literature, click-through behavior in Web search engines has been intensively studied [34], [35], and various approaches and algorithms have been proposed to help estimate the position-normalized CTR [36]- [39]. In SSA practice, however, it is still controversial whether or not most Web search engines have been aware of this positional bias on CTR and have taken actions to remove this bias (e.g., via normalization). ...