SEKE 2012 Proceedings - Knowledge Systems Institute

An Empirical Study on Recommendation Methods for Vertical B2C E-commerce
Chengfeng Hui, Jia Liu ∗ , Zhenyu Chen, Xingzhong Du, Weiyun Ma
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Software Insititute, Nanjing University, Nanjing, China
∗ liujia@software.nju.edu.cn
Abstract
Recommender systems have been already performing
well in large comprehensive E-commerce sites. Another
trend emerging in E-commerce area is vertical B2C sites.
The vertical B2C sites sell only one or a few of categories
of goods to end users, and most of the users are new users.
There may be not sufficient history data to generate highquality
recommendation because the traffic of these sites are
low. To analyze the feasibility and usability of popular recommendation
methods (e.g. collaborative filtering, contentbased,
etc.) in vertical B2C sites, we have been working in
collaboration with an E-commerce site to gather real data
from an actually running vertical B2C site. In this paper,
we evaluate the performance of different recommendation
methods over half a year period from December 2010 to
June 2011. We analyze both the performance and cost of
these recommendation methods, and experimental results
show that we should apply suitable methods based on the
available data.
Keywords: Vertical E-commerce, Collaborative Filtering,
Content-based Recommendation, Recommender
System
1. Introduction
E-commerce [6] has been widely used to perform business
transactions. One fast growing subcategory of E-
commerce is vertical B2C, and it is becoming more and
more attractive to end users. Unlike mass online merchants
(e.g. Amazon, Walmart), vertical B2C sites focus
on one single or a small number of categories of products
(e.g. Newegg sells computers/electronics, Netflix sells
Books/Music/Movies), they can provide more various and
comprehensive choices in a particular field.
Vertical B2C E-commerce sites are becoming very popular,
but there is little, if any publication directly evaluating
the feasibility of popular recommendation algorithms on
vertical B2C sites, so we want to analyze the performance
of different recommendation methods in such systems. Fortunately
we have the opportunity to collaborate with an E-
commerce site to gather real data from currently running
site and conduct our case study.Vertical B2C E-commerce
sites do have some features that will limit the application
of popular recommendation methods. For content-based
method, the most obvious limitation is items must be capable
of being described as features, if there is no or limited
feature information about items, it is hard for content-based
techniques to get a good performance. For collaborative
filtering, one precondition is there are sufficient ratings to
items given by users or something equivalent. But unlike
data sets (e.g. Movielens, Eachmovie, etc.) which we usually
evaluate collaborative filtering on, it is very likely that
there are no rating information on vertical B2C sites. Another
problem is most users of vertical B2C E-commerce
sites are new users, which will lead to the lack of sufficient
history behavior data of one user to generate his or her profile.
2. Recommender System
The origin of recommender systems can be traced back
to approximation theory, information retrieval and forecasting
theories [1]. The appearance of first papers about
collaborative filtering in the mid-1990s [3] make recommender
systems become an popular research direction.
And recent explosively development of Internet further increases
the interest in this domain both in the industry and
academia. The most famous recommender system applied
in industry is probably the book recommender system of
Amazon (www.amazon.com). This system records users’
purchase, explore, comment and rating data to recognize
users’ preference and then recommends products to users.
The biggest news in academia is maybe in 2009 Netflix
(www.netflix.com) which is a large DVD rental service
company announced to award 1 million dollar prize to a
team that could increase the accuracy of rating prediction by
10%.The BellKor’s Pragmatic Chaos team eventually won
the prize with their BigChaos solution [8].
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