E-commerce Product Recommendation by Personalized
Promotion and Total Surplus Maximization
Qi Zhao
University of California, Santa Cruz
1506 High St., Santa Cruz, 95060, CA, USA
[email protected]
ABSTRACT
Our experiment setting includes a full-fledged E-commerce
product search system similar to Amazon. The system hosts
real skin care products from Amazon. The experimental
subjects are hired from Amazon Mechanical Turk. Each
subject is instructed to go through multiple steps - BDM
training (screening unqualified subjects), selecting favorite
products, ranking and bidding. Linear regression models
are trained on the collected bidding data. Given any userproduct pair, optimal pricing is sought by maximizing the
expected seller profit. The proposed approach is evaluated
against several baseline algorithms using RMSE and seller
profit as metrics. The experimental results suggest that the
proposed approach significantly outperforms baseline algorithms. Please refer to [1] for details.
Existing recommendation algorithms treat recommendation
problem as rating prediction and the recommendation quality is measured by RMSE or other similar metrics. However, we argued that when it comes to E-commerce product
recommendation, recommendation is more than rating prediction by realizing the fact price plays a critical role in
recommendation result. In this work, we propose to build
E-commerce product recommender systems based on fundamental economic notions. We first proposed an incentive compatible method that can effectively elicit consumer’s
willingness-to-pay in a typical E-commerce setting and in a
further step, we formalize the recommendation problem as
maximizing total surplus. We validated the proposed WTP
elicitation algorithm through crowd sourcing and the results demonstrated that the proposed approach can achieve
higher seller profit by personalizing promotion. We also
proposed a total surplus maximization (TSM) based recommendation framework. We specified TSM by three of the
most representative settings - e-commerce where the product quantity can be viewed as infinity, P2P lending where
the resource is bounded and freelancer marketing where the
resource (job) can be assigned to one freelancer. The experimental results of the corresponding datasets shows that
TSM exceeds existing approach in terms of total surplus.
2.
Keywords
E-commerce; recommendation; economics; surplus maximization; personalized promotion
1.
RECOMMENDATION BY PERSONALIZED
PROMOTION
We proposed to elicit consumer’s WTP in E-commerce
setting and conduct personalized promotion to improve seller’s
profit. Our proposed approach comprises two major ingredients BDM auction - which guarantees to be incentive compatible and lottery - which helps collect data at lowest cost.
We proposed to treating the recommendation problem as
matching producer and consumer so that the total surplus
will be maximized. This is motivated by the fact that the
perceived value of the same product vary across individuals.
Hence, the total surplus of selling the same products to different consumers might be different. We argued that it is
important for recommender systems to consider the benefits
of both consumers and producers. As a result, total surplus
maximization is a sensible optimizing objective to achieve
this goal.
We proposed a TSM based recommendation framework
that can easily specified for various applications such as Ecommerce product recommendation, P2P lending and freelancer marketing. In particular, the personalized utility and
product quantity constraint can be easily specified according to application. We showed that each specification can
be treated as a standard constrained optimization problem.
We evaluated the proposed framework on real datasets from
E-commerce, P2P lending and freelancer marketing and the
results suggest that our approach yields much higher total surplus that standard collaborative filtering based techniques.
3.
Permission to make digital or hard copies of part or all of this work for personal or
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on the first page. Copyrights for third-party components of this work must be honored.
For all other uses, contact the owner/author(s).
RECOMMENDATION BY TOTAL SURPLUS MAXIMIZATION
REFERENCES
[1] Q. Zhao, Y. Zhang, D. Friedman, and F. Tan.
E-commerce recommendation with personalized
promotion. In Proceedings of the 9th ACM Conference
on Recommender Systems, pages 219–226. ACM, 2015.
WSDM 2016 February 22-25, 2016, San Francisco, CA, USA
c 2016 Copyright held by the owner/author(s).
ACM ISBN 978-1-4503-3716-8/16/02. . . $15.00
DOI: http://dx.doi.org/10.1145/2835776.2855085
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