24,146 research outputs found
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
Dissortative From the Outside, Assortative From the Inside: Social Structure and Behavior in the Industrial Trade Network
It is generally accepted that neighboring nodes in financial networks are
negatively assorted with respect to the correlation between their degrees. This
feature would play an important 'damping' role in the market during downturns
(periods of distress) since this connectivity pattern between firms lowers the
chances of auto-amplifying (the propagation of) distress. In this paper we
explore a trade-network of industrial firms where the nodes are suppliers or
buyers, and the links are those invoices that the suppliers send out to their
buyers and then go on to present to their bank for discounting. The network was
collected by a large Italian bank in 2007, from their intermediation of the
sales on credit made by their clients. The network also shows dissortative
behavior as seen in other studies on financial networks. However, when looking
at the credit rating of the firms, an important attribute internal to each
node, we find that firms that trade with one another share overwhelming
similarity. We know that much data is missing from our data set. However, we
can quantify the amount of missing data using information exposure, a variable
that connects social structure and behavior. This variable is a ratio of the
sales invoices that a supplier presents to their bank over their total sales.
Results reveal a non-trivial and robust relationship between the information
exposure and credit rating of a firm, indicating the influence of the neighbors
on a firm's rating. This methodology provides a new insight into how to
reconstruct a network suffering from incomplete information.Comment: 10 pages, 10 figures, To appear in conference proceedings of the
IEEE: HICSS-4
RankMerging: A supervised learning-to-rank framework to predict links in large social network
Uncovering unknown or missing links in social networks is a difficult task
because of their sparsity and because links may represent different types of
relationships, characterized by different structural patterns. In this paper,
we define a simple yet efficient supervised learning-to-rank framework, called
RankMerging, which aims at combining information provided by various
unsupervised rankings. We illustrate our method on three different kinds of
social networks and show that it substantially improves the performances of
unsupervised metrics of ranking. We also compare it to other combination
strategies based on standard methods. Finally, we explore various aspects of
RankMerging, such as feature selection and parameter estimation and discuss its
area of relevance: the prediction of an adjustable number of links on large
networks.Comment: 43 pages, published in Machine Learning Journa
Corporate payments networks and credit risk rating
Aggregate and systemic risk in complex systems are emergent phenomena
depending on two properties: the idiosyncratic risks of the elements and the
topology of the network of interactions among them. While a significant
attention has been given to aggregate risk assessment and risk propagation once
the above two properties are given, less is known about how the risk is
distributed in the network and its relations with the topology. We study this
problem by investigating a large proprietary dataset of payments among 2.4M
Italian firms, whose credit risk rating is known. We document significant
correlations between local topological properties of a node (firm) and its
risk. Moreover we show the existence of an homophily of risk, i.e. the tendency
of firms with similar risk profile to be statistically more connected among
themselves. This effect is observed when considering both pairs of firms and
communities or hierarchies identified in the network. We leverage this
knowledge to show the predictability of the missing rating of a firm using only
the network properties of the associated node
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