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Please develop a table of at least 25 articles Citation Research objectives Methodology Findings Conclusion/Future research Find answers to these questions 1- what are the important infrastructure needed in a company and a country to make e-commerce work 2- success factors for e-commerce adoption 3- e-commerce in airlines 4- code sharing 5- competiteve advantage 6- the personalization through e- commerce all those related chapter 2

Research Article
Research on E-Commerce Platform-Based Personalized
Recommendation Algorithm
Zhijun Zhang, Gongwen Xu, and Pengfei Zhang
School of Computer Science and Technology, Shandong Jianzhu University, Jinan, Shandong 250101, China
Correspondence should be addressed to Zhijun Zhang; [e-mail]
Received 22 February 2016; Accepted 26 June 2016
Academic Editor: Francesco Carlo Morabito
Copyright © 2016 Zhijun Zhang et al. This is an open access article distributed under the Creative Commons Attr
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is prope
Aiming atdata sparsity and timeliness in traditionalE-commerce collaborative filtering recommendation algorithms,when
constructing user-item rating matrix, this paper utilizes the feature that commodities in E-commerce system belo
levels to fill in nonrated items by calculating RF/IRF of the commodity’s corresponding level. In the recommendati
stage, considering timeliness of the recommendation system, time weighted based recommendation prediction fo
to design a personalized recommendation modelby integrating levelfilling method and rating time.The experimentalresults
on real dataset verify the feasibility and validity of the algorithm and it owns higher predicting accuracy compare
recommendation algorithms.
1. Introduction
With the rapid development of the Internet and continuous
expansion ofE-commerce scale,commodity number and
variety increase quickly. Merchants provide numerous com-
modities through shopping websites and customers usually
take a large amountof time to find theircommodities.
Browsing lots ofirrelevantinformation and products will
make consumers run off due to the information overload. In
the E-commerce age, users need an electronic shopping assis-
tant, which can recommend possible interesting or satisfying
commodities according to interests and hobbies ofusers.
To solve all these problems, a personalized recommendation
system emerges [1].
Personalized recommendation recommends information
and commodities to users according to interests and purchas-
ing behaviors of users. Personalized recommendation system
is an advanced business intelligence platform established on
the basis ofmassive datasetmining and itaims athelp-
ing E-commerce websites provide completely personalized
decision-making support and information service for cus-
tomer purchase.E-commerce platform-based personalized
recommendation technology has been widely mentioned in
academia and industry.The recommendation factorsare
usually based on website best seller commodities,user city,
past purchase behaviors, and purchase history to predict t
possible purchase behaviors of users.
Traditionalcollaborative filtering (CF) algorithms have
problems ofdata sparsity and cold start.With the rapid
development of the network technology,personalized rec-
ommendation in E-commerce environment faces new chal-
lenges,faster timeliness,higher accuracy,and stronger user
personalization.Its major feature is considering the influ-
encesof real-time situation.On the basisof traditional
collaborative filtering algorithms,three innovation points
are added:a more proper filling data method for nonrated
commodity; adding time and giving high weight on data cl
to evaluation time and low weight on data far from evaluat
time;and exploring the influences of the number of near-
est neighbors on recommendation accuracy and obtaining
optimal nearest-neighbor set. Through the abovementione
changes,the prediction accuracy ofthe algorithm can be
improved and the needs of users’personalized services can
be satisfied.
The remainderof this paperis organized asfollows.
In Section 2,we provide an overview ofrelated work at
home and abroad.Section 3 introduces the key technology
of E-commerce recommendation system. Section 4 provide
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Applied Computational Intelligence and So Computing
Volume 2016, Article ID 5160460, 7 pages
http://dx.doi.org/10.1155/2016/5160460

2 Applied Computational Intelligence and Soft Computing
Table 1: The typical recommendation systems.
Field Personalized recommendation systems
Electronic mall Amazon.com, eBay, Alibaba
Movie MovieLens, Netflix.com, Moviefinder.com
Message PHOAKS, GroupLens, p-Tango
Web page Siteseer, QuIC, R2P, METIOREW
Music Music.Yahoo.com, Ringo, CoCoA
the experiment dataset and evaluation metrics, which intro-
duces the experimental scheme,experiment results,and its
analysis,followed by the conclusion and future work in
Section 5.
2. Related Work
With the continuous improvements ofE-commerce plat-
form,E-commerce personalized recommendation system
has gradually formed into a perfect system.Academia and
E-commerce enterprises have paid more and more attention
to the recommendation system. At present, many large-scale
websites at home and abroad have provided recommendation
function forusersand many prototypesof personalized
recommendation systems have emerged and obtained good
application effects.A lot of reprehensive recommendation
systems are shown as in Table 1.
Utilizing various socialrelations in socialnetworking
servicesfor recommendation studieshas achieved great
progressand becomesthe hotspotfield of personalized
recommendation studies.Bonhard and Sasse studied the
influences of social background on recommendation results
and results proved that when users purchase commodities,
they tend to accept the recommendation of acquaintance [2].
Sinha and Swearingen carried outexperiments by the aid
of multiple online recommendation systems and the experi-
mental results indicated that when online systems and friends
both provided recommendations, users tended to select the
latter [3]. Caverlee et al. [4] constructed the trust framework
by online socialnetworking services,which adopted trust
and feedback information to generate recommendation result
lists,with higher recommendation precision.Adomavicius
and Tuzhilin [5] proposed multidimensionalspace recom-
mendation algorithm and pointed out that it was necessary
to add recommendation feature dimensions according to
specific conditions.Nguyen etal. proposed a nonlinear
probability algorithm GPFM for contextrecommendation
model. In the recommendation process, this algorithm used
Gaussian process, which can not only display feedback infor-
mation but also use implicit feedback information. Gradient
descentmethod is used for optimization,which improves
the modelexpansibility [6].Paper [7] compares four main
recommendation technologiesand introducesthe review
E-commerce research hottopic in the field ofpersonal-
ized recommendation.The study [8] proposes personalized
productrecommendations based on preference similarity,
recommendation trust, and social relations.
Aiming at personalized problems of E-commerce, many
domestic scholars carried out thorough studies.Huang and
Benyoucefmadea review on relevantliteraturesof E-
commerce personalized recommendation,illustrating the
concept of social commerce,discussing the relevant design
characteristics ofsocialcommerce and E-commerce,and
putting forward a new model and a set of principles to guid
the design ofsocialcommerce [9].Li et al. proposed an
E-commerce personalized recommendation algorithm that
integrated commodity similarity, recommendation trust, an
socialrelations [8].Experiment results indicate that social
relations in socialnetworking can be used to improve the
recommendation algorithm accuracy. Zhang and Liu raised
a personalized recommendation algorithm integrating trus
relationship and time series [10].In another paper [11],a
social networking recommendation algorithm integrating a
kinds ofcontextinformation was proposed.On the basis
of users’geographicallocation and time information,this
algorithm deeply explores the socialrelations ofpotential
users and helps users to seek other users with similar pref-
erence. Then, corresponding recommendations are made b
combining social relations of mobile users, which effectivel
solves the recommendation accuracy.In literature [12],the
author comprehensively considered the influences ofuser
preference,geographicalconvenience,and friends and put
forward a group purchase discount coupon recommendatio
system to promote the commodity with sensitive locations
It is notdifficultto find outthrough deep analysis of
the abovementioned algorithms thatexisting personalized
recommendation algorithmsstill have many deficiencies:
poor expansibility ofpreference models,inability to adapt
to dynamic change ofdatasets,resulting in lack oftime
information that can be used, and inability to solve cold sta
problems very well. Aiming at the abovementioned problem
based on a comprehensive consideration of factors such as
timeliness ofthe recommendation system,time weighted
based recommendation prediction formula is adopted and
different weights are given to rating data according to ratin
time,so as to improve the recommendation quality ofE-
commerce recommendation system.
3. Key Technology of Recommendation Syst
In order to better solve the problems of data sparsity and
rating time factor,this paper adopts levelfilling method
to predictthe nonrated items and finally combines time
weights in the recommendation prediction stage to improv
the recommendation accuracy of the algorithm.
3.1.HierarchicalFillingMethod.Traditionalcollaborative
filtering algorithm CF sets the nonrated items as the avera
or a fixed value,for example,3 (rating between 1 and 5),
shown as in Table 2.Three is set for it is the middle rating
of 1–5. It does not consider the user preference and it pure
sets the median as the prediction rating. Different users gi
different item rating. The advantage of this method is simp
but it cannot solve the problems of traditional collaborative
filtering methods in sparse user matrixes.
To reduce the sparsity of the rating matrix,this paper
adopts level filling method to construct the rating matrix. F

Applied Computational Intelligence and Soft Computing 3
Table 2: User-item rating matrix.
User/item Item 1 Item 2 Item 3 Item 4
User 1 1 3 4 3
User 2 3 2 3 3
User 3 3 5 2 3
One
Two 1 Two n
Three 1 Three p Three 1 Three q
Item 1 Item r
· · ·
· · ·· · ·
· · ·
Figure 1: Commodity hierarchy in E-commerce.
E-commerce websites,each commodity owns its category,
which has a parentcategory.Namely,commodities in E-
commerce own the concept of level and different commodi-
ties own differenthierarchies,shown as in Figure 1.The
levelof one commodity is considered in the construction
of rating matrix.For commodities atdifferentlevels,this
paper supposes its subordinate commodities to fill different
prediction rating through calculation.This paper combines
traditional classification methods with user rating data and
through calculation a preliminary rating is made on the
nonrated data of recommendation users. This method is used
to construct a new user-item rating matrix.
For rated data,ratings are extracted to the belonging
category. In the construction of rating matrix by collaborative
filtering technology,for one category,its Rated Frequency
(RF) is calculated and the calculation method is shown as
follows:
RF = the number ofrated items
the totalnumber to be rated items
. (1)
Item Rated Frequency (IRF)represents the weightof
rated items and the calculation method is shown as follows:
IRF =log( SUMallrate
SUMdefaultrate
) , (2)
where SUMallraterepresents the total amount of rated data and
automatically filled prediction rating. SUMdefaultraterepresents
the total number of automatically filled prediction ratings.
This paper proposes a user-item rating matrix construc-
tion algorithm, which automatically fills ratings of nonrated
data in RF∗IRF greater than the threshold. The design flow
of hierarchical filling method is shown as follows:
Input: initial user-item rating matrix𝑃.
Output:user rating matrix𝑄 after the prediction
rating is filled.
Step 1.Calculate RF∗IRF of each category in matrix𝑃.
Step 2.Fill in the average item rating of RF∗IRF greater than
threshold of the rating matrix.
Step 3.For new items in rating matrix, 3 is automatically fille
and finally constructs the user-item rating matrix𝑄.
Through calculating RF∗IRF of specific category of items,
this paper fills scores of the top-𝑁category items of RF∗IRF
instead of simply filling dataset0, which can reduce the data
sparsity.Finally,the algorithm automatically fills new items
with3, aiming at solving cold start of new items.
3.2.Improvement ofRecommendation Timeliness.CF algo-
rithm does not take the influences oftime on rating data
into consideration and it treats item ratings of different use
visited at different moments equally. Interests and prefere
of differentusersdynamically change with time,so the
time when different users have interests in the same item
differs.However,if the rating is the same,they are likely to
be regarded as similar neighbor users,further influencing
the recommendation quality.This paperintroducestime
function, shown as follows:
𝑓 (𝑡𝑎,𝑖) = 𝑒−𝑡𝑎,𝑖, (3)
where𝑡𝑎,𝑖 represents the time thatuser a has interests in
item𝑖. Time function𝑓(𝑡)is a monotone decreasing function
and decreases with the increase of time𝑡 and time weight
maintains(0, 1). Namely, the newer the data is, the greater th
weight is and the time function is. In this way, the influenc
of time on recommendation quality is solved.
3.3. Time Weighted Based Prediction Function.CF algorithm
predicts the rating of item𝑖by current user𝑢according to the
rating similarity of users or items, shown as follows:
𝑅𝑢,𝑖 = 𝑅𝑢 + ∑𝑁
𝑎=1(𝑅𝑎,𝑖− 𝑅𝑎) ⋅sim(𝑢, 𝑎)
∑𝑁
𝑎=1|sim(𝑢, 𝑎)| , (4)
where sim(𝑢, 𝑎)represents the rating similarity between users
𝑢 and𝑎and (5) calculates the similarity between users𝑢 and
𝑎by Pearson’s correlation coefficient:
sim(𝑢, 𝑎)
= ∑∈𝑖 𝐼𝑢,𝑎 (𝑅𝑢,𝑖 − 𝑅𝑢) (𝑅𝑎,𝑖 − 𝑅𝑎)
√ ∑∈𝑖 𝐼𝑢,𝑎 (𝑅𝑢,𝑖 − 𝑅𝑢)2√ ∑∈𝑖 𝐼𝑢,𝑎 (𝑅𝑎,𝑖− 𝑅𝑎)2
, (5)
where𝐼𝑢,𝑎represents the item set rated by users𝑢 and𝑎. 𝑅𝑢,𝑖
and𝑅𝑎,𝑖, respectively, represent the score of item𝑖 by users𝑢
and𝑎, and𝑅𝑢 and𝑅𝑎, respectively, represent the mean score
of all items by users𝑢 and𝑎.
Time function𝑓(𝑡)is added in (4) and the weighted
prediction rating of item𝑖 by target user𝑢 is improved:
𝑃𝑢,𝑖 = 𝑅𝑢 + ∑𝑛
𝑎=1(𝑅𝑎,𝑖− 𝑅𝑎) ⋅ sim(𝑢, 𝑎) ⋅ 𝑓 (𝑡𝑎,𝑖)
∑𝑛
𝑎=1|sim(𝑢, 𝑎)| ⋅ 𝑓 (𝑡𝑎,𝑖) . (6)

4 Applied Computational Intelligence and Soft Computing
Client
Registration, login
Shopping
DabaBase
Sales data
Product segmentation
Client’s file
Rating matrix
preprocessing
Hierarchical filling
reduce sparse
Produce the
nearest neighbors
Recommender engine
Client DB Trade DB
Sales
record
Marketing
management
Sales
Potential
customer
Purchasing
commodity
Data
preprocessing
Figure 2: NewRec recommendation model.
Here,𝑓(𝑡𝑎,𝑖) is shown as (3) and each rating item owns
only one weight. Latest ratings are given with great weight and
past ratings are given with small weight, which helps predict
more accurately.
3.4. New Recommendation Model (NewRec).Aiming at data
sparsity and timeliness in traditionalcollaborative filtering
recommendation algorithms,this paper integrates hierar-
chicalfilling method and time on the basisof CF and
puts forward a new personalized recommendation algorithm,
NewRec.NewRec recommendation modelis shown as in
Figure 2. The whole recommendation model is divided into
three main modules:data preprocessing module,sparsity
reduction module,and nearest-neighbor recommendation
module.
Data preprocessingmoduleinput user information,
including user purchase records, user rating on commodities,
and user duration time on websites. This useful information
is converted into acceptable data format of the recommenda-
tion method, forming user-item rating matrix.
In sparsity reduction module,for allthe items in user-
item rating matrix, RF/IRF of the commodity’s corresponding
levelis calculated and filled in the specific value of rating
matrix, which solves the problem of data sparsity.
In nearest-neighbor recommendation module, consider-
ing timeliness of the recommendation system, time weighted
based recommendation prediction formula isadopted to
calculate the prediction ratings of the target items, rank them,
and select top-𝑁items as recommendation set.
4. Experimental Analysis
4.1. Dataset.The dataset in this paper is from https://moviel-
ens.org/,which is collected by GroupLens research group
in University ofMinnesota.This datasetrealizessitesof
user personalized recommendation by collaborative filterin
technology. The system adopts the user ratings ranging fro
1 to 5. The higher the rating is, the more interested the us
are. This dataset contains the ratings of 1,682 movies by 9
users. According to the latest statistics, there are over 70,0
users and 6,600 rated movies in the database of MovieLen
site.At present,datasets in MovieLens site are abundant,
clear, real, and accurate, so they have been widely used in
simulation test of the personalized recommendation system
and authoritative test data sources in this field. Taking this
the simulation dataset,this paper designs a reasonable and
feasible evaluation standard and carries out a comparative
analysis on the recommendation quality ofthe improved
algorithm.The experimentalresults prove the validity and
rationality of the improved algorithm.
4.2.ExperimentalScheme.For collaborative filtering rec-
ommendation algorithm,its actualeffects in E-commerce
personalized recommendation system are mainly influence
by two factors:data sparsity and the number of the nearest
neighbors.Thus,this experiment designs the following two
schemes.
CF algorithm,time-based function recommendation
(TimeRec for short), hierarchical filling (HF for short), and
NewRec in this paper under different degrees of data spars
are compared.Different degrees ofdata sparsity can truly
simulate the working condition of E-commerce recommen-
dation system and verify the changes ofrecommendation
effects under different conditions of effective information.
Under different numbers ofnearest neighbors,recom-
mendation performances of CF,HF,TimeRec,and NewRec
are compared.This process can verify the changes ofrec-
ommendation effectsof each recommendation algorithm
underdifferentnumbersof nearestneighborsand help
each recommendation algorithm select optimalnumber of
nearestneighbors for convenience ofoperation in future
experiments.
This section designs 5 experiments to verify the superio
ity of the algorithm in this paper:
(1) The influences of different degrees of sparsity on re
ommendation quality: in the experiment, this paper
selected three degrees of data sparsity for compariso
(2) MAE comparisonbetweenhierarchicalfilling
method and traditional collaborative filtering CF.
(3) The influences of time on recommendation accuracy
(4) The influences of numbers of nearest neighbors on
recommendation algorithms: the influences of differ-
ent scales of nearest-neighbor sets on recommenda-
tion quality are observed.
(5) The recommendation qualities:with the same num-
ber ofneighbors,the recommendation qualities of
different algorithms are compared.

Applied Computational Intelligence and Soft Computing 5
0.92
0.81
0.74
0.75
0.8
0.85
0.9
MAE
20 30 40 50 6010
NeighbourNum
Figure 3:The impact of data sparsity on recommendation algo-
rithm.
4.3. Baseline.To test the performance of NewRec recommen-
dation model and time function-based improved algorithm
TimeRec,this paper will verify the validity of the model by
experiment. Traditional collaborative filtering recommenda-
tion algorithm CF [13] is taken as baseline.CF algorithm
utilizes the similarities between items to recommend similar
commodities for target users. The similarities between users
or items can be calculated by (5).
4.4.Metrics.To compare the algorithm performance,this
paper adopts MAE and RMSE to evaluate the recommen-
dation performance of the recommendation algorithm. The
definition of MAE is shown as follows:
MAE = ∑𝑖,𝑗
󵄨󵄨󵄨󵄨󵄨𝑅𝑖,𝑗 −̂ 𝑅𝑖,𝑗
󵄨󵄨󵄨󵄨󵄨
𝑁 , (7)
where𝑅𝑖,𝑗represents the actual rating of commodity𝑗by user
𝑖 and̂𝑅𝑖,𝑗 represents the prediction rating of commodity𝑗by
user𝑖. 𝑁represents the number of all prediction ratings. The
definition of RMSE is shown as follows:
RMSE=√ ∑𝑖,𝑗(𝑅𝑖,𝑗 −̂ 𝑅𝑖,𝑗)2
𝑁 . (8)
4.5. Experiment Results
4.5.1.Influences ofData Sparsity on Recommendation Algo-
rithm.Data sparsity refers to the ratio ofnonrated items
to the elements in the whole rating matrix.To verify the
influences ofdata sparsity on recommendation accuracy,
this paper fills the prediction ratings in originaluser-item
rating matrix for recommendation calculation. Datasets with
sparsity of 0.92, 0.81, and 0.74 are selected and CF algorithm
was used for verification. The experimental results are shown
as in Figure 3.
It can be seen from Figure 3 that the recommendation
quality does not increase with the decrease of sparsity. In this
experiment,when the sparsity is 0.81,the recommendation
quality is the highest.In the following experiment,datasets
with sparsity of 0.81 are taken for experiment.
4.5.2. Analysis of Hierarchical Filling (HF) Method in Recom-
mendation Accuracy.To verify the influences of data sparsity
on recommendation accuracy, MAE is calculated before and
CF
HF
20 30 40 50 6010
NeighbourNum
0.75
0.8
0.85
0.9
0.95
MAE
Figure 4: Analysis of HF in recommendation accuracy.
CF
TimeRec
20 30 40 50 6010
NeighbourNum
0.75
0.8
0.85
0.9
MAE
Figure 5: Influences of time on recommendation accuracy.
after hierarchical filling (HF) method through experiment. I
can be seen from Figure 4 that, with the increasing numbe
of focused users among neighbor users, MAE of HF method
and MAE of CF method both decrease and MAE of HF is
smaller than that of CF algorithm under the same number
neighbors. Thus, HF method is better than CF algorithm.
4.5.3.Influences ofTime on Recommendation Accuracy.To
guarantee the recommendation accuracy, influences of tim
on prediction rating stage shall be considered and each rat
item owns only one weight. Latest ratings are endowed wit
greater weightand pastratings are endowed with smaller
weight,which helps better forecast. To verify the influences
of time on recommendation accuracy, this section compar
MAE between CF algorithm and TimeRec algorithm.
It can be seen from Figure 5 that MAE of the improved
TimeRec algorithm with time function is lower than that
of CF withouttime function.Through comparison,it is
proved that time does have influences on recommendation
prediction and the use of time function improves the recom
mendation quality of the recommendation system.
4.5.4. Influences of the Number of User Neighbors on Reco
mendation Accuracy.It is easy to calculate the nearest neigh-
bor of each user by calculating the similarity between user
To verify the influences of the number of user neighbors on
recommendation accuracy,this section makes comparison
through experiment and the number ofnearest neighbors
increased from 10 to 60, with interval of 10. The experime
results are shown in Figure 6.
It can be seen from Figure 6 that,with the increasing
number ofnearest neighbors,MAE of four algorithms all
tend to decrease firstly and increase then.However,MAE

6 Applied Computational Intelligence and Soft Computing
CF
HF
TimeRec
NewRec
20 30 40 50 6010
NeighbourNum
0.75
0.8
0.85
0.9
MAE
Figure 6: Influences of the number of user neighbors on accuracy.
CF
HF
TimeRec
NewRec
20 30 40 50 6010
NeighbourNum
0.7
0.8
0.9
1
RMSE
Figure 7: Comparison among different algorithms on accuracy.
of the improved algorithm NewRec is lower than those of
the other three algorithms, which indicates that the NewRec
can provide better recommendation quality than the other
three.From the further analysis,it can be seen thatfour
recommendation algorithmsown lowestMAE when the
number of nearest neighbors is 40. Namely, when the number
of nearest neighbors is 40, four recommendation algorithms
all can achieve good recommendation quality.
4.5.5.Comparison among DifferentRecommendation Algo-
rithms on Recommendation Accuracy.To verify the recom-
mendation accuracy of NewRec algorithm proposed in this
paper,this section calculates RMSE of algorithms through
experimentsand theexperimentalresultsare shown in
Figure 7.
It can be seen from Figure7 that, compared with
traditional collaborative filtering recommendation algorithm
CF, levelfilling-based improved algorithm HF,and time
function-based improved algorithm TimeRec, the improved
algorithm NewRec owns the highest recommendation accu-
racy.
To sum up the abovementioned experimentalresults,
the following conclusion can be drawn. Compared with the
other three algorithms,the recommendation quality of the
improved algorithm NewRec is significantly improved after
hierarchical filling and time function are added.
This paper utilized the features that commodities in E-
commerce system belong to different levels to fill in specific
score in rating matrix by calculating RF/IRF of the commod-
ity’s corresponding level, which solves problems of data spar-
sity and cold start to certain extent. In the recommendation
prediction stage,in consideration of timeliness of the rec-
ommendation system, time weighted based recommendat
prediction formula is adopted and different weights are giv
to rating data according to rating time, so as to improve th
recommendation quality ofE-commerce recommendation
system.The experiment results in real dataset indicate that
the algorithm in this paper is better than the traditional
collaborative filtering recommendation algorithm in runnin
efficiency and recommendation accuracy.
5. Conclusions
Collaborative filtering is a common recommendation tech-
nology of E-commerce personalized recommendation sys-
tem. However, it also owns many problems. For data spars
in user-item rating matrix and timeliness ofuser evalua-
tion, this paper proposes an improved collaborative filterin
recommendation algorithm, NewRec, and verifies the feasi
bility of NewRec algorithm through experiment simulation,
proving that it can improve the recommendation quality of
E-commerce recommendation system.At present, there are
stillmany problems and shortcomings in the studies of E-
commerce personalized recommendation.For user person-
alized recommendation, the improved collaborative filterin
algorithm in this paper fails to consider the influences of
context and user interaction behaviors,which need further
thorough studies in the future.
Competing Interests
The authors declare that they have no competing interests
Acknowledgments
This paper is supported by the Science and Technology De
opmentPlanning ofShandong Province (2014GGX101011,
2015GGX101018),A Projectof Shandong Province Higher
EducationalScienceand Technology Program (J12LN31,
J13LN11, and J14LN14), and Jinan Higher Educational Inno-
vation Plan (201401214, 201303001).
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