Recommender system architecture for adaptive green marketing


Recommender system architecture for adaptive green marketing

Ying-Lien Lee a,⇑, Fei-Hui Huang b
a Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung County 413, Taiwan
b Department of Marketing and Distribution Management, Oriental Institute of Technology, Taipei County 220, Taiwan

a r t i c l e i n f o

Keywords:
Green marketing
Green consumerism
Recommender system
Fuzzy inference system

a b s t r a c t

Green marketing has become an important method for companies to remain profitable and competitive
as the public and governments are more concerned about environmental issues. However, most online
shopping environments do not consider product greenness in their recommender systems or other shop-
ping tools. This paper aims to propose the use of recommender systems to aid the green shopping process
and to promote green consumerism basing upon the benefits of recommender systems and a compliance
technique called foot-in-the-door (FITD). In this study, the architecture of a recommender system for
green consumer electronics is proposed. Customers’ decision making process is modeled with an adaptive
fuzzy inference system in which the input variables are the degrees of price, feature, and greenness and
output variables are the estimated rating data. The architecture has three types of recommendation:
information filtering, candidate expansion, and crowd recommendation. Ad hoc customization can be
applied to tune the recommendation results. The findings are reported in two parts. The first part
describes the potentials of using recommender systems in green marketing and the promotion of green
consumerism; the second part describes the proposed recommender system architecture using green
consumer electronics as the context. Discussion of the proposed architecture and comparison with other
systems are also included in this part. The proposed architecture provides a capable platform for person-
alized green marketing by offering customers shopping advices tailored to their preferences and for the
promotion of green consumerism.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Recommender systems have become an important technology
for electronic commerce on many fronts (Bose, 2009; Kauffman &
Walden, 2001). It can filter for online shoppers the vast amount
of information, saving the customers from the information over-
load problem (Chen, Shang, & Kao, 2009). It can be a decision aid
for customers who are challenged when they are in the market
for unfamiliar products. It can be a strategic marketing platform
on which online venders can personalize promotions and sales
for each customer (Chen, 2008; Shih, Chiu, Hsu, & Lin, 2002).
Recommender systems have been vigorously researched and
developed in the fields of academia and business. Some notable
examples include Apple Inc.’s Genius of iTunes that make music
recommendations, University of Minnesota’s MovieLens and
Netflix’s Cinematch that recommend movie titles, Amazon.com’s
recommender system that generates recommendations of an
assortment of products, and Outbrain.com’s blog rating widget that

recommends blogs a rater might be interested in. The domain of
recommender systems is not limited to the famous instances men-
tioned above. Recommender systems for news, web pages, jokes,
academic articles, consumer electronics, restaurants, and a pleth-
ora of other subject matters, have been researched and imple-
mented (Adomavicius & Tuzhilin, 2005; Iijima & Ho, 2007).
However, to our knowledge, few researches have dealt with rec-
ommender system of green product.

Green product is increasingly important in our global village as
the general public is becoming more concerned of our impact on
the planet. Driven by this trend, companies have been trying to de-
sign and manufacture greener products, and have been trying to
promote their products and brand images by communicating their
greenness to the customers via a variety of channels. Yet, eco-
labeling remains one of the fundamental ways to inform the cus-
tomers how green their products are and in what respect their
products are green. Eco-labels, usually issued by third-party orga-
nizations, are textual or graphical presentations of the environ-
mental characteristics of a product, which can be found on the
product itself, on the packaging, or in the manual. Examples of
eco-labels include Green Seal, Energy Star, and WEEE (Waste Elec-
trical and Electronic Equipment Directive). Studies have shown
that public education campaign is one of the key determinants of

0957-4174/$ - see front matter � 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.eswa.2011.01.164

⇑ Corresponding author. Address: No. 168, Jifong E. Rd., Wufong Township,
Taichung County 413, Taiwan. Tel.: +886 4 23323000; fax: +886 4 23742327.

E-mail address: yinglienlee@gmail.com (Y.-L. Lee).

Expert Systems with Applications 38 (2011) 9696–9703

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mailto:yinglienlee@gmail.com
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successful eco-labeling programs (Malcohn, Paulos, Stoeckle, &
Wang, 1994).

Public education campaign of eco-labeling programs can be
done via methods such as media coverage, regulation, promotion,
school curriculum, and so on. This research proposes using recom-
mender systems, in addition to these methods, as a means to edu-
cate and inform on-line customers. The justification of such
proposal relies on two of the primary functions of a recommender
system: information filtering and candidate expansion. When cus-
tomers are confronted with a flood of products or with unfamiliar
products, they may have difficulty in making a shopping decision.
Based upon what they have purchased before, a recommender sys-
tem can help the customers by filtering out items that are unlikely
to be preferred. For example, Amazon.com’s recommender system
generates a personalized list of recommended products each time a
customer visits their web site. As to candidate expansion, when a
customer is evaluating the decision to buy a product, a recom-
mender system can ensure that other good candidates are included
in the consideration set by finding related products based upon the
product under consideration. Take Amazon.com’s recommender
system for example again. When a customer is looking at the cat-
alog page of a product, the recommender system recommends
items similar to the current item. Tapping into the capabilities of
information filtering and candidate expansion, recommender sys-
tems can be transformed into a green product advocate informing
the customers of available choices that are greener. A recom-
mender system of green products can also sieve through a set of
products to retrieve only the items matching an implicitly or
explicitly degree of greenness designated by a customer. Such sys-
tem can also find other products whose greenness and other as-
pects are comparable based upon a product under consideration.
The reduced effort in the decision making process may enhance
the quality and users’ satisfaction of the decision (Häubl & Trifts,
2000), which in turn will make green shopping a more enjoyable
experience.

The adaptability of a recommender system can also contribute
to the promotion of green consumerism by using a technique
called foot-in-the-door (FITD) technique (Freedman & Fraser,
1966). FITD is a compliance technique in which a person is more
likely to accept a larger request if this request is preceded by a
smaller request. The technique is also found to be effective in com-
puter-mediated communication (CMC) in addition to face-to-face
or telephone communications (Guéguen, 2002). In a recommender
system of green products, items with higher degree of greenness
and with comparable or equal degrees of price and feature can
be first recommended to a user who is reluctant to buy green prod-
ucts. Appropriate feedback should be given to the user about the
environmental contribution of the purchase one has made. The de-
gree of greenness of the recommended items in the future can be
adjusted accordingly if the users’ purchasing transactions reflect
acceptance or rejection of the items.

The goal of this paper is to develop a recommender system
architecture for green consumer electronics. Instead of simply add-
ing an additional green attribute to the conventional recommender
systems, the architecture uses an adaptive behavioral agent to find
the products of a certain degree of greenness according to users’
behaviors. The agent uses an adaptive fuzzy inference system to
learn users’ behavior over time with a basic assumption that a
bilateral relationship of either symbiosis or antibiosis exists be-
tween the pairs of price vs. feature, price vs. greenness, and feature
vs. greenness.

The rest of this paper is organized as follows. The next section
gives brief review of recommender systems and fuzzy inference
systems. The proposed architecture is presented and discussed in
Section 3. Conclusions and future research directions are presented
in the final section.

2. Related work

2.1. Recommender system

Recommender systems have a variety of forms with different
functions (Manouselis & Costopoulou, 2007; Wan, Menon, & Rama-
prasad, 2007). Therefore, it warrants a clear definition of the kind
of recommender systems this paper is dealing with. Schein, Pope-
scul, Ungar, and Pennock (2005) define recommender systems as
the following: ‘‘Recommender systems suggest items of interest
to users based on their explicit and implicit preferences, the pref-
erences of other users, and user and item attributes’’. This defini-
tion points out the fundamental parts and necessary input and
output data of a recommender system. First, a recommender sys-
tem needs data of preferences from single user or multiple users.
The system can explicitly elicit preferences from users by asking
them to rate some items, or implicitly by inferring their prefer-
ences from past transactions (Resnick & Varian, 1997). Second, a
recommender system requires attributes of users and items.
Manouselis and Costopoulou (2007) refer to these two sets of attri-
butes as ‘‘user model’’ and ‘‘domain model’’, respectively. Several
representations can be used as user models, such as per user prod-
uct ratings, demographic attributes, transaction histories, and so
on. On the other hand, domain models can be represented as char-
acteristics of products and as derived attributes such as taxono-
mies, hierarchies, and ontologies. Both models may utilize the
acquired user preferences to derive their own data.

The core of a recommender system is the mechanism of sugges-
tion generation based upon the user model and domain model. The
mechanism can be formulated as follows (Adomavicius & Tuzhilin,
2005): Let C be the set of all customers and P be the set of all prod-
ucts that a recommender knows of. In addition, let U(c, p) be the
utility function that associates (c, p) pairs with utility values which
can be ratings, profits, or some other measurements. The objective
of a recommender system is to find a set of items p0 e P such that
U(c, p) is maximized for a customer. The mathematical formulation
is as follows:

8c 2 C; p0 ¼ arg max
p2P

Uðc; pÞ

In the formulation, ‘‘arg max’’ means ‘‘the argument of the
maximum’’.

Recommender systems can be generally classified into three
categories according to the mechanism of recommendation gener-
ation (Adomavicius & Tuzhilin, 2005; Schein et al., 2005): (1) Con-
tent-based systems recommend items that are similar to the ones a
user preferred in the past. (2) Collaborative systems recommend
items that other like-minded users preferred in the past. (3) Hybrid
systems recommend items by combining content-based and col-
laborative methods in recommendation generation.

As Adomavicius and Tuzhilin (2005) point out, content-based
and collaborative systems have some challenges to be dealt with.
For content-based systems, the first problem is ‘‘limited content
analysis’’, in which case the recommendation is limited by the fea-
tures associated with the items. However, some features are hard-
er to extract than others are. For example, extracting features from
textual information is easier than from multimedia data. Also,
items that are identical in terms of features are indistinguishable.
The second problem is overspecialization, in which case the sys-
tem can only recommend items that are similar to items a user
liked in the past. In other words, the lack of diversity may jeopar-
dize the practicality of a recommender system. The third problem
is ‘‘new user problem’’, in which case a user is unable to get reli-
able recommendations until a sufficient amount of transactions
are present for the recommender system to learn about the users’
preferences.

Y.-L. Lee, F.-H. Huang / Expert Systems with Applications 38 (2011) 9696–9703 9697



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