17 research outputs found
SIGIR 2021 E-Commerce Workshop Data Challenge
The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for
"In-session prediction for purchase intent and recommendations". The challenge
addresses the growing need for reliable predictions within the boundaries of a
shopping session, as customer intentions can be different depending on the
occasion. The need for efficient procedures for personalization is even clearer
if we consider the e-commerce landscape more broadly: outside of giant digital
retailers, the constraints of the problem are stricter, due to smaller user
bases and the realization that most users are not frequently returning
customers. We release a new session-based dataset including more than 30M
fine-grained browsing events (product detail, add, purchase), enriched by
linguistic behavior (queries made by shoppers, with items clicked and items not
clicked after the query) and catalog meta-data (images, text, pricing
information). On this dataset, we ask participants to showcase innovative
solutions for two open problems: a recommendation task (where a model is shown
some events at the start of a session, and it is asked to predict future
product interactions); an intent prediction task, where a model is shown a
session containing an add-to-cart event, and it is asked to predict whether the
item will be bought before the end of the session.Comment: SIGIR eCOM 2021 Data Challeng
Are You A Risk Taker? Adversarial Learning of Asymmetric Cross-Domain Alignment for Risk Tolerance Prediction
Most current studies on survey analysis and risk tolerance modelling lack
professional knowledge and domain-specific models. Given the effectiveness of
generative adversarial learning in cross-domain information, we design an
Asymmetric cross-Domain Generative Adversarial Network (ADGAN) for domain scale
inequality. ADGAN utilizes the information-sufficient domain to provide extra
information to improve the representation learning on the
information-insufficient domain via domain alignment. We provide data analysis
and user model on two data sources: Consumer Consumption Information and Survey
Information. We further test ADGAN on a real-world dataset with view embedding
structures and show ADGAN can better deal with the class imbalance and
unqualified data space than state-of-the-art, demonstrating the effectiveness
of leveraging asymmetrical domain information
Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers
The popularity of e-commerce platforms continues to grow. Being able to
understand, and predict customer behavior is essential for customizing the user
experience through personalized result presentations, recommendations, and
special offers. Previous work has considered a broad range of prediction models
as well as features inferred from clickstream data to record session
characteristics, and features inferred from user data to record customer
characteristics. So far, most previous work in the area of purchase prediction
has focused on known customers, largely ignoring anonymous sessions, i.e.,
sessions initiated by a non-logged-in or unrecognized customer. However, in the
de-identified data from a large European e-commerce platform available to us,
more than 50% of the sessions start as anonymous sessions. In this paper, we
focus on purchase prediction for both anonymous and identified sessions on an
e-commerce platform. We start with a descriptive analysis of purchase vs.
non-purchase sessions. This analysis informs the definition of a feature-based
model for purchase prediction for anonymous sessions and identified sessions;
our models consider a range of session-based features for anonymous sessions,
such as the channel type, the number of visited pages, and the device type. For
identified user sessions, our analysis points to customer history data as a
valuable discriminator between purchase and non-purchase sessions. Based on our
analysis, we build two types of predictors: (1) a predictor for anonymous that
beats a production-ready predictor by over 17.54% F1; and (2) a predictor for
identified customers that uses session data as well as customer history and
achieves an F1 of 96.20%. Finally, we discuss the broader practical
implications of our findings.Comment: 10 pages, accepted at SIGIR eCommerce 202
A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction
For e-commerce platforms such as Taobao and Amazon, advertisers play an
important role in the entire digital ecosystem: their behaviors explicitly
influence users' browsing and shopping experience; more importantly,
advertiser's expenditure on advertising constitutes a primary source of
platform revenue. Therefore, providing better services for advertisers is
essential for the long-term prosperity for e-commerce platforms. To achieve
this goal, the ad platform needs to have an in-depth understanding of
advertisers in terms of both their marketing intents and satisfaction over the
advertising performance, based on which further optimization could be carried
out to service the advertisers in the correct direction. In this paper, we
propose a novel Deep Satisfaction Prediction Network (DSPN), which models
advertiser intent and satisfaction simultaneously. It employs a two-stage
network structure where advertiser intent vector and satisfaction are jointly
learned by considering the features of advertiser's action information and
advertising performance indicators. Experiments on an Alibaba advertisement
dataset and online evaluations show that our proposed DSPN outperforms
state-of-the-art baselines and has stable performance in terms of AUC in the
online environment. Further analyses show that DSPN not only predicts
advertisers' satisfaction accurately but also learns an explainable advertiser
intent, revealing the opportunities to optimize the advertising performance
further