2,111 research outputs found
The Price of Privacy - An Evaluation of the Economic Value of Collecting Clickstream Data
The analysis of clickstream data facilitates the understanding and prediction of customer behavior in e-commerce. Companies can leverage such data to increase revenue. For customers and website users, on the other hand, the collection of behavioral data entails privacy invasion. The objective of the paper is to shed light on the trade-off between privacy and the business value of cus- tomer information. To that end, the authors review approaches to convert clickstream data into behavioral traits, which we call clickstream features, and propose a categorization of these features according to the potential threat they pose to user privacy. The authors then examine the extent to which different categories of clickstream features facilitate predictions of online user shopping pat- terns and approximate the marginal utility of using more privacy adverse information in behavioral prediction models. Thus, the paper links the literature on user privacy to that on e-commerce analytics and takes a step toward an economic analysis of privacy costs and benefits. In par- ticular, the results of empirical experimentation with large real-world e-commerce data suggest that the inclusion of short-term customer behavior based on session-related information leads to large gains in predictive accuracy and business performance, while storing and aggregating usage behavior over longer horizons has comparably less value
Dropout Model Evaluation in MOOCs
The field of learning analytics needs to adopt a more rigorous approach for
predictive model evaluation that matches the complex practice of
model-building. In this work, we present a procedure to statistically test
hypotheses about model performance which goes beyond the state-of-the-practice
in the community to analyze both algorithms and feature extraction methods from
raw data. We apply this method to a series of algorithms and feature sets
derived from a large sample of Massive Open Online Courses (MOOCs). While a
complete comparison of all potential modeling approaches is beyond the scope of
this paper, we show that this approach reveals a large gap in dropout
prediction performance between forum-, assignment-, and clickstream-based
feature extraction methods, where the latter is significantly better than the
former two, which are in turn indistinguishable from one another. This work has
methodological implications for evaluating predictive or AI-based models of
student success, and practical implications for the design and targeting of
at-risk student models and interventions
The Metabolism and Growth of Web Forums
We view web forums as virtual living organisms feeding on user's attention
and investigate how these organisms grow at the expense of collective
attention. We find that the "body mass" () and "energy consumption" ()
of the studied forums exhibits the allometric growth property, i.e., . This implies that within a forum, the network transporting
attention flow between threads has a structure invariant of time, despite of
the continuously changing of the nodes (threads) and edges (clickstreams). The
observed time-invariant topology allows us to explain the dynamics of networks
by the behavior of threads. In particular, we describe the clickstream
dissipation on threads using the function , in which
is the clickstreams to node and is the clickstream dissipated
from . It turns out that , an indicator for dissipation efficiency,
is negatively correlated with and sets the lower boundary
for . Our findings have practical consequences. For example,
can be used as a measure of the "stickiness" of forums, because it quantifies
the stable ability of forums to convert into , i.e., to remain users
"lock-in" the forum. Meanwhile, the correlation between and
provides a convenient method to evaluate the `stickiness" of forums. Finally,
we discuss an optimized "body mass" of forums at around that minimizes
and maximizes .Comment: 6 figure
Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda
Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online
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