Since, the internet has become popular, the consumer oriented electronic commerce
has grown so huge that now companies are convinced of the importance of understanding
this new emerging market. The rapid spread of the Internet has made it easy
for a firm to develop a new style of e-business via one-to-one marketing (Min
and Han, 2005).
It is becoming more important for the companies to analyze and understand the needs and expectations of their online users or customers because the Internet is one of the most effective media to gather, disseminate and utilize the information about its users or customers. Thus, it is easier to extract knowledge out of the shopping process to create new business opportunities under the Internet environment.
During the last decades, significant research efforts have been devoted to
conceiving filtering systems that automatically provide users with desirable
and interesting information. In this decade, the so-called recommender systems
have been gaining momentum as another effective means of reducing complexity
when searching for relevant information. Besides, these personalization tools
have attracted an increasing public interest, leveling the ground for new business
opportunities in different fields, such as E-Commerce (EC) and Digital TV (Schafer
et al., 1999; Bedi et al., 2009).
Instead of the traditional on-spot shopping, EC provides alternative ways to
get the information of products for purchasing the desired products with the
following properties of transparency (Philips and Meeker,
||Process transparency (Yang et al., 2004)
As a result, many different approaches have been researched to assist with
data overload including personalization, information filtering and recommender
systems. Recommender systems attempt to address the data overload problem by
providing assistance in a decision making context; supplying a recommendation
based on some predictive element related to the system user. Recommender systems
are particularly useful for decision-makers where decisions must be made in
a short time period and the effort required for interacting with the system
be limited as much as possible (Russell and Yoon, 2008;
Kazemi and Zarandi, 2008; Rigopoulos
et al., 2008).
Recommender systems are one of important tools to be used in this world. Different
technologies and sciences are employed to improve their performance. Web Recommender
Systems (RS), the most successful example of web personalization tools, efficiently
guide the user in a personalized manner to interesting items within a very large
space of possible options (Burke, 2002). Typically RS
recommend information (URLs, Netnews articles), entertainment (books, movies,
restaurants), or individuals (experts) (Al-Shamri and Bharadwaj,
A recommender system is the information filtering that applies data analysis
techniques to the problem of helping customers find the products they would
like to purchase by producing a predicted likeness score or a list of recommended
products for a given customer (Sarwar et al., 1998).
Due to an explosion of e-commerce, recommender systems are rapidly becoming
a core tool for accelerating cross-selling and strengthening customer loyalty
(Min and Han, 2005).
The purpose of this study is to suggest new way for recommender system that pays attention to both preferences of customer and seller.
Recommender systems have gained an increasing importance since the early work
on Collaborative Filtering (CF) in the mid-1990s when researchers started focusing
on RS that explicitly rely on the ratings structure (Adomavicius
and Tuzhilin, 2005). The recommender system analyzes a database of consumer
preferences to overcome the limitations of segment-based mass marketing by presenting
each consumer with a personal set of recommendations (Schafer
et al., 2001). Recommender system help customer to choose efficient
product related to their preferences to purchase. As many web retailer that
use recommender system, we can point Amazon.com, MovieLens.org, CDNow.com (Al-Shamri
and Bharadwaj, 2008).
There are two prevalent approaches for building recommender systems-content-based
recommending and CF (Min and Han, 2005). CF is a general
approach to personalized information filtering. CF systems work by collecting
user feedback in the form of ratings for items in a given domain and exploit
the similarities and differences among the profiles of several users in determining
how to recommend an item. On the other hand, content-based methods provide recommendations
by comparing representations of content that interests the users (Pazzani,
Customer satisfaction measurement enables EC managers to: (1) accurately identify
customers requirements and their relative importance; (2) understand how
customers perceive the EC and whether its performance meets their requirements;
(3) pinpoint the priorities for improvement; (4) Define objectives of service
improvement and follow the progress towards a customer satisfaction index and
(5) increase profits through improved customer loyalty. Different approaches
dealing with the assessment of customer satisfactions already exist (Grigoroudis
and Siskos, 2002).
Most recommender systems take into account only customer satisfaction. In practical
situations, there is another approach for completing the negotiations between
seller and customer. The win-win strategy should be applied to achieve a quiescent
point. That is a situation in which both the seller and the customer feel they
have enough benefits in the present purchase. For example, consider the process
of buying a house. The seller offers a price for a house and the buyer announces
the needs and preferences, including the qualities and quantities, but after
some negotiations between them a point of compromise should be achieved (Niknafs
et al., 2009).
When, the recommending processes takes solely from the viewpoint of the supplier,
the goal will be to maximize the profits of the selling goods under a set of
products that satisfy the customers preferences and budgets. Although,
maximal profit strategy will bring about the highest income to the suppliers,
from management viewpoint, it may not retain customers. Therefore, an alternative
strategy is considered by taking both suppliers and customers preferences
into account (Wang and Wu, 2009).
In electronic commerce, the interaction between two sides of purchase activity is usually carried out through the interface pages of a web site. So, it is better to use an algorithm that gathers necessary data from both sides and gives suitable recommendations such that a quiescent point is achieved. In this study a new algorithm is demonstrated that uses a win-win strategy. In addition to the preferences and needs of the customer the priorities of the seller are entered to the system.
Niknafs et al. (2009) proposed system tries
to find a quiescent point that is satisfactory to both sides. Throughout, the
study this algorithm is called Win-Win-QP algorithm. The main strategy of that
approach is to find a situation in which both sides feel an acceptable level
of satisfaction. In the new proposed method the way of implementing genetic
algorithm and encoding the parameters of problem are modified and this has led
to more degree of satisfaction and fewer steps for approaching the best results.
The parameter like flag, direction and enable are some of new factors that causes
Our aim is to find a quiescent point compromising the satisfaction of both customer and seller. In the website of the store, information is gathered via interactive query forms. This information include: the importance of price, mode and material of the desired item from the point of view of both customer and seller. These questions are made for their satisfaction level also and then in a sequential process, some suggestions are proposed. Satisfaction of both seller and customer are compared in each steps until achieving the margin threshold and that suggestion is selected as the final result.
In our proposed algorithm, we assume some parameters to pursue steps and avoid repetition in making suggestion. The values of flag variable are 1 and 0 to be understood by the machine and easy to implement. Here, at first we will define the problem, then the used parameters will be introduced and finally we explain the steps of algorithm.
DESCRIBING THE PROBLEM
In this study, we propose a new way for special win-win problem as defined below:
Assume that we have an online shop for clothes, for each of clothes there are 3 features such as material, mode and price that will be shown with F and also for each feature there are 3 states like Table 1.
The customer wants to select one of these clothes with special features and state and announces the first suggestion. The proposed method checks that suggestion, if it can satisfy both customer and seller, it is accepted, but if it cant, some of the states should be changed to achieve satisfaction of both. For checking the level of satisfaction, we consider a formula such that the distance between their pleasures becomes less than a specified threshold. For example (pleasure of customer- pleasure of seller)>20 that means customer satisfaction is more important. If the above criterion is not satisfied, it is needed to improve and change some of parameters and the differences of pleasures should be less than threshold.
When, it detects that changing should be happened, checks all features and
then select the feature with minimum importance, to changes its state.
This process will be repeated until terminating criterion is satisfied. That
is a point that we can assume both seller and customer are satisfied and the
business will be done. The terminate criteria is:
DEFINITION OF PARAMETERS
Parameter 1: Direction is used to show moving backward or forward through steps of algorithm:
Parameter 2: Denotes the nth state of the mth feature. The customer suggests his/her preferences as shown in Table 2. For example according to Table 1, F11 is the old mode of clothes.
Parameter 3: Flag is like parameter 2 with zero value as default is used to show which feature is changed and which one is not. Zero means no change in feature, one means feature is changed.
Parameter 4: Enable: is also like parameter 2 filled by 1 as default value to show if changing in this feature is valid or not. 0 means not valid, 1 means valid. For example, if all three possible values of mode are used, it is not possible to change it again and so the enable value would be set to 0.
Parameter 5: State shows how many times a feature can be changed. After any changes in feature, the related value decrements by 1.For example in Table 1 and three states are defined for each feature, so maximum value is 2, if we have k states for each feature, the maximum will be k-1.
Parameter 6: In this approach, we pay attention to both customer and seller, so we consider satisfaction parameter to measure the satisfaction level of customer and seller. Scmn is used to measure the level of Customer satisfaction for nth state of mth feature. Ssmn is used similarly for seller. Table 3 and 4 show these parameters for customers and also same table for seller is considered.
|| Different features and their states
|| Sample of parameters
||Customer’s satisfaction value of features
|| Importance of features for customer and seller
Parameter 7: Importance parameter shows the level of importance of each feature for customer (Icm) and seller (Ism). Table 4 shows this parameter.
Parameter 8: Pleasure is the main parameter for assessing the final value of pleasure for seller and customer. This parameter is used for deciding about the final suggestion as the optimized setup of features such that both seller and customer feel satisfaction.
Pc stands for the pleasure of customer and Ps is the pleasure of seller. These
parameters are considered to be the product of Scij and Ici
Pc = Scij * Ici
Ps = Ssij * Isi
For both seller and customer, we calculate the pleasure value and our objective
is to decrease the distance between their pleasures. The threshold value will
be the criterion of comparing the distances.
MATERIALS AND METHODS
Our algorithm, named I-WinWin-QP is proposed in different steps as below:
Input: Preferences of seller and customer,
Importance level of seller and customer,
The first suggestion of customer,
Output: The final suggestion to seller and customer.
||Get the customers suggestion
||Initialize all the parameters
||Calculate Ps and Pc for seller and customer:
Pc = Sc1i * Ic1 +Sc2j
* Ic2 +Sc 3k * Ic3
Ps = Ss1i * Is1 +Ss2j * Is2
+Ss 3k * Is3
||Calculate (Pc-Ps). If termination criterion is satisfied,
stop the algorithm, else continue
||Find the feature with minimum importance and consider it as changing feature
||For changing feature, evaluate flag
If related flag is 0, change it to 1 and continue
||Then evaluate enable for that feature
If enable = 1, continue
Else if enable = 0, set direction = 0 and flag = 1 for this feature
||Evaluate state for this feature to know how many times it
can be changed
If state = 0, set direction = 0
If state = 1 decrease it so state = 0 and set enable = 0 to show that all states
If state = 2 decrease it so state = 1
If direction = 1 continue,
Else set direction = 1 and go to step 11
||Select new state for the changing feature and reset the table
of customers suggestion as defined in step 1 with this state and then
do steps 3 to 9
If terminate criterion is satisfied, finish,
Else select the last state and do steps 3 to 9
Check criterion, if satisfied, go to step 11
Else it is finished
||Find the next minimum importance and go to step 6. When all
of the three features are checked go to next step
||Propose new suggestion as the final result
This approach is implemented by C#.Net and SQLServer2005. The project was done as a collaboration between Computer engineering department of shahid bahonar kerman university and information technology department of Industrial kerman university from march 2009 till January 2010. Figure 1 depicts the changing in pleasures of customer and seller. After detecting the importance and satisfaction of each feature by customer and seller, the proposed algorithm suggests new form of states depending on customers satisfied state of each feature.
|| Pleasure levels in first run
|| Pleasure level in second run
|| Pleasure level in third run
In Fig. 1, it is shown that after five steps, the algorithm
reaches the termination criterion and suggests new combination of states of
features. In Fig. 2, chart of second run, it is shown that
seller has higher pleasure level at the end of process. In Fig.
3, it is shown that in only three steps, algorithm reaches the termination
criterion. Figure 4-7 show other runs of
the algorithm. As it can be seen all the runs approach to a suitable point.
|| Pleasure level in 4th run
|| Pleasure level in 5th run
|| Pleasure level in 6th run
As it is shown well in different runs, if one feature has high importance to
customer, it wont be changed until the other two features are changed
and algorithm doesnt reach termination criterion.
|| Customers satisfaction and importance
|| Sellers satisfaction and importance
|| Satisfaction of customer and seller
|| Pleasure level in 7th run
For instance in first run, Mode has high importance to customer, so in system
suggestion, mode doesnt change. It will be discussed in third run, too.
In Table 5-7 the summery of three run is gathered. In Table 5 and 6, the satisfaction and importance of different features are shown, satisfactions of one feature is related to Table 1 and based on that sequence, in this table only level of satisfactions is written. In Table 7, the final pleasure that software acquires, is explained.
In this study, we propose a method for making a recommender system for both seller and customer such that the satisfaction level of both be more than a threshold margin. First the needs and preferences of seller and customer are determined and then through the proposed algorithm successive suggestions are made until achieving a point that both sides of the business feel satisfaction. For this purpose, software is written and the result is shown.
Comparing with the previous algorithm (Niknafs et al.,
2009), this new algorithm can find the result in less steps and the other
benefit is to consider both customer and seller and related to their preferences
In this study, algorithm tries to find the minimum importance and then changes that feature, after that second minimum and finally if it doesnt reach the termination criterion, changes the highest importance feature for the customer.