14,424 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
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The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.
Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge
Enhanced information retrieval using domain-specific recommender models
The objective of an information retrieval (IR) system is to retrieve relevant items which meet a user information need. There is currently significant interest in personalized IR which seeks to improve IR effectiveness by incorporating a model of the user’s interests. However, in some situations
there may be no opportunity to learn about the interests of a specific user on a certain topic. In our work, we propose an IR approach which combines a recommender algorithm with IR methods to improve retrieval for domains where the system has no opportunity to learn prior information about the user’s knowledge of a domain for which they have not previously entered a query. We use search data from other previous users interested in the same topic to build a
recommender model for this topic. When a user enters a query on a topic, new to this user, an appropriate recommender model is selected and used to predict a ranking which the user may find interesting based on the behaviour of previous
users with similar queries. The recommender output is integrated with a standard IR method in a weighted linear combination to provide a final result for the user. Experiments using the INEX 2009 data collection with a simulated recommender training set show that our approach can improve on a baseline IR system
CIMTDetect: A Community Infused Matrix-Tensor Coupled Factorization Based Method for Fake News Detection
Detecting whether a news article is fake or genuine is a crucial task in
today's digital world where it's easy to create and spread a misleading news
article. This is especially true of news stories shared on social media since
they don't undergo any stringent journalistic checking associated with main
stream media. Given the inherent human tendency to share information with their
social connections at a mouse-click, fake news articles masquerading as real
ones, tend to spread widely and virally. The presence of echo chambers (people
sharing same beliefs) in social networks, only adds to this problem of
wide-spread existence of fake news on social media. In this paper, we tackle
the problem of fake news detection from social media by exploiting the very
presence of echo chambers that exist within the social network of users to
obtain an efficient and informative latent representation of the news article.
By modeling the echo-chambers as closely-connected communities within the
social network, we represent a news article as a 3-mode tensor of the structure
- and propose a tensor factorization based method to
encode the news article in a latent embedding space preserving the community
structure. We also propose an extension of the above method, which jointly
models the community and content information of the news article through a
coupled matrix-tensor factorization framework. We empirically demonstrate the
efficacy of our method for the task of Fake News Detection over two real-world
datasets. Further, we validate the generalization of the resulting embeddings
over two other auxiliary tasks, namely: \textbf{1)} News Cohort Analysis and
\textbf{2)} Collaborative News Recommendation. Our proposed method outperforms
appropriate baselines for both the tasks, establishing its generalization.Comment: Presented at ASONAM'1
Adaptive Matrix Completion for the Users and the Items in Tail
Recommender systems are widely used to recommend the most appealing items to
users. These recommendations can be generated by applying collaborative
filtering methods. The low-rank matrix completion method is the
state-of-the-art collaborative filtering method. In this work, we show that the
skewed distribution of ratings in the user-item rating matrix of real-world
datasets affects the accuracy of matrix-completion-based approaches. Also, we
show that the number of ratings that an item or a user has positively
correlates with the ability of low-rank matrix-completion-based approaches to
predict the ratings for the item or the user accurately. Furthermore, we use
these insights to develop four matrix completion-based approaches, i.e.,
Frequency Adaptive Rating Prediction (FARP), Truncated Matrix Factorization
(TMF), Truncated Matrix Factorization with Dropout (TMF + Dropout) and Inverse
Frequency Weighted Matrix Factorization (IFWMF), that outperforms traditional
matrix-completion-based approaches for the users and the items with few ratings
in the user-item rating matrix.Comment: 7 pages, 3 figures, ACM WWW'1
Predictive User Modeling with Actionable Attributes
Different machine learning techniques have been proposed and used for
modeling individual and group user needs, interests and preferences. In the
traditional predictive modeling instances are described by observable
variables, called attributes. The goal is to learn a model for predicting the
target variable for unseen instances. For example, for marketing purposes a
company consider profiling a new user based on her observed web browsing
behavior, referral keywords or other relevant information. In many real world
applications the values of some attributes are not only observable, but can be
actively decided by a decision maker. Furthermore, in some of such applications
the decision maker is interested not only to generate accurate predictions, but
to maximize the probability of the desired outcome. For example, a direct
marketing manager can choose which type of a special offer to send to a client
(actionable attribute), hoping that the right choice will result in a positive
response with a higher probability. We study how to learn to choose the value
of an actionable attribute in order to maximize the probability of a desired
outcome in predictive modeling. We emphasize that not all instances are equally
sensitive to changes in actions. Accurate choice of an action is critical for
those instances, which are on the borderline (e.g. users who do not have a
strong opinion one way or the other). We formulate three supervised learning
approaches for learning to select the value of an actionable attribute at an
instance level. We also introduce a focused training procedure which puts more
emphasis on the situations where varying the action is the most likely to take
the effect. The proof of concept experimental validation on two real-world case
studies in web analytics and e-learning domains highlights the potential of the
proposed approaches
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