260 research outputs found
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Marketing and Data Science: Together the Future is Ours
The synergistic use of computer science and marketing science techniques offers the best avenue for knowledge development and improved applications. A broad area of complementarity between the typical focus in statistics and computer science and that in marketing offers great potential. The former fields tend to focus on pattern recognition, control and prediction. Many marketing analyses embrace these directions, but also contribute by modeling structure and exploring causal relationships. Marketing has successfully combined foci from management science with foci from psychology and economics. These fields complement each other because they enable a broad spectrum of scientific approaches. Combined, they provide both understanding and practical solutions to important and relevant managerial marketing problems, and marketing science is already very successful at obtaining unique insights from big data
The egalitarian effect of search engines
Search engines have become key media for our scientific, economic, and social
activities by enabling people to access information on the Web in spite of its
size and complexity. On the down side, search engines bias the traffic of users
according to their page-ranking strategies, and some have argued that they
create a vicious cycle that amplifies the dominance of established and already
popular sites. We show that, contrary to these prior claims and our own
intuition, the use of search engines actually has an egalitarian effect. We
reconcile theoretical arguments with empirical evidence showing that the
combination of retrieval by search engines and search behavior by users
mitigates the attraction of popular pages, directing more traffic toward less
popular sites, even in comparison to what would be expected from users randomly
surfing the Web.Comment: 9 pages, 8 figures, 2 appendices. The final version of this e-print
has been published on the Proc. Natl. Acad. Sci. USA 103(34), 12684-12689
(2006), http://www.pnas.org/cgi/content/abstract/103/34/1268
Incorporating the surfing behavior of web users into PageRank
Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2013.Thesis (Master's) -- Bilkent University, 2013.Includes bibliographical references leaves 68-73One of the most crucial factors that determines the effectiveness of a large-scale
commercial web search engine is the ranking (i.e., order) in which web search
results are presented to the end user. In modern web search engines, the skeleton
for the ranking of web search results is constructed using a combination of the
global (i.e., query independent) importance of web pages and their relevance to
the given search query. In this thesis, we are concerned with the estimation of
global importance of web pages. So far, to estimate the importance of web pages,
two different types of data sources have been taken into account, independent of
each other: hyperlink structure of the web (e.g., PageRank) or surfing behavior
of web users (e.g., BrowseRank). Unfortunately, both types of data sources have
certain limitations. The hyperlink structure of the web is not very reliable and
is vulnerable to bad intent (e.g., web spam), because hyperlinks can be easily
edited by the web content creators. On the other hand, the browsing behavior of
web users has limitations such as, sparsity and low web coverage.
In this thesis, we combine these two types of feedback under a hybrid page importance
estimation model in order to alleviate the above-mentioned drawbacks.
Our experimental results indicate that the proposed hybrid model leads to better
estimation of page importance according to an evaluation metric that uses the
user click information obtained from Yahoo! web search engine’s query logs as
ground-truth ranking. We conduct all of our experiments in a realistic setting,
using a very large scale web page collection (around 6.5 billion web pages) and
web browsing data (around two billion web page visits) collected through the
Yahoo! toolbar.Ashyralyyev, ShatlykM.S
Incorporating the surfing behavior of web users into PageRank
In large-scale commercial web search engines, estimating the importance of a web page is a crucial ingredient in ranking web search results. So far, to assess the importance of web pages, two different types of feedback have been taken into account, independent of each other: the feedback obtained from the hyperlink structure among the web pages (e.g., PageRank) or the web browsing patterns of users (e.g., BrowseRank). Unfortunately, both types of feedback have certain drawbacks. While the former lacks the user preferences and is vulnerable to malicious intent, the latter suffers from sparsity and hence low web coverage. In this work, we combine these two types of feedback under a hybrid page ranking model in order to alleviate the above-mentioned drawbacks. Our empirical results indicate that the proposed model leads to better estimation of page importance according to an evaluation metric that relies on user click feedback obtained from web search query logs. We conduct all of our experiments in a realistic setting, using a very large scale web page collection (around 6.5 billion web pages) and web browsing data (around two billion web page visits). Copyright is held by the owner/author(s)
Sex differences in television viewing and attention: do males really channel surf more than females?
Channel surfing is often thought of as a male-dominated pastime; however, previously there was no objective data supporting this conclusion. In the present study television viewing and channel surfing were monitored in 44 college students who simultaneously performed an auditory vigilance task. In addition, a survey was administered to determine self-reported individual television viewing habits. Results showed that males channel surfed at almost twice the rate of females. In addition, after the first test tone they generally detected more tones in the vigilance task than females. It was concluded the high channel surfing rate of males reflected lower levels of program involvement and attention
PageRank and rank-reversal dependence on the damping factor
PageRank (PR) is an algorithm originally developed by Google to evaluate the
importance of web pages. Considering how deeply rooted Google's PR algorithm is
to gathering relevant information or to the success of modern businesses, the
question of rank-stability and choice of the damping factor (a parameter in the
algorithm) is clearly important. We investigate PR as a function of the damping
factor d on a network obtained from a domain of the World Wide Web, finding
that rank-reversal happens frequently over a broad range of PR (and of d). We
use three different correlation measures, Pearson, Spearman, and Kendall, to
study rank-reversal as d changes, and show that the correlation of PR vectors
drops rapidly as d changes from its frequently cited value, .
Rank-reversal is also observed by measuring the Spearman and Kendall rank
correlation, which evaluate relative ranks rather than absolute PR.
Rank-reversal happens not only in directed networks containing rank-sinks but
also in a single strongly connected component, which by definition does not
contain any sinks. We relate rank-reversals to rank-pockets and bottlenecks in
the directed network structure. For the network studied, the relative rank is
more stable by our measures around than at .Comment: 14 pages, 9 figure
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