Hindawi Publishing Corporation Applied Computational Article 2022

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 Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Aiming at data sparsity and timeliness in traditional E-commerce collaborative filtering recommendation algorithms, when
constructing user-item rating matrix, this paper utilizes the feature that commodities in E-commerce system belong to different
levels to fill in nonrated items by calculating RF/IRF of the commodity’s corresponding level. In the recommendation prediction
stage, considering timeliness of the recommendation system, time weighted based recommendation prediction formula is adopted
to design a personalized recommendation model by integrating level filling method and rating time. The experimental results
on real dataset verify the feasibility and validity of the algorithm and it owns higher predicting accuracy compared with present
recommendation algorithms.
1. Introduction
With the rapid development of the Internet and continuous
expansion of E-commerce scale, commodity number and
variety increase quickly. Merchants provide numerous com-
modities through shopping websites and customers usually
take a large amount of time to find their commodities.
Browsing lots of irrelevant information 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 of users.
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 of massive dataset mining and it aims at help-
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 factors are
usually based on website best seller commodities, user city,
past purchase behaviors, and purchase history to predict the
possible purchase behaviors of users.
Traditional collaborative filtering (CF) algorithms have
problems of data 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-
ences of real-time situation. On the basis of 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 close
to evaluation time and low weight on data far from evaluation
time; and exploring the influences of the number of near-
est neighbors on recommendation accuracy and obtaining
optimal nearest-neighbor set. Through the abovementioned
changes, the prediction accuracy of the algorithm can be
improved and the needs of users’ personalized services can
be satisfied.
The remainder of this paper is organized as follows.
In Section 2, we provide an overview of related work at
home and abroad. Section 3 introduces the key technology
of E-commerce recommendation system. Section 4 provides
Hindawi Publishing Corporation
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 of E-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 for users and many prototypes of personalized
recommendation systems have emerged and obtained good
application effects. A lot of reprehensive recommendation
systems are shown as in Table 1.
Utilizing various social relations in social networking
services for recommendation studies has achieved great
progress and becomes the hotspot field 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 out experiments 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 social networking services, which adopted trust
and feedback information to generate recommendation result
lists, with higher recommendation precision. Adomavicius
and Tuzhilin [5] proposed multidimensional space recom-
mendation algorithm and pointed out that it was necessary
to add recommendation feature dimensions according to
specific conditions. Nguyen et al. proposed a nonlinear
probability algorithm GPFM for context recommendation
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
descent method is used for optimization, which improves
the model expansibility [6]. Paper [7] compares four main
recommendation technologies and introduces the review
E-commerce research hot topic in the field of personal-
ized recommendation. The study [8] proposes personalized
product recommendations 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
Benyoucef made a review on relevant literatures of E-
commerce personalized recommendation, illustrating the
concept of social commerce, discussing the relevant design
characteristics of social commerce and E-commerce, and
putting forward a new model and a set of principles to guide
the design of social commerce [9]. Li et al. proposed an
E-commerce personalized recommendation algorithm that
integrated commodity similarity, recommendation trust, and
social relations [8]. Experiment results indicate that social
relations in social networking can be used to improve the
recommendation algorithm accuracy. Zhang and Liu raised
a personalized recommendation algorithm integrating trust
relationship and time series [10]. In another paper [11], a
social networking recommendation algorithm integrating all
kinds of context information was proposed. On the basis
of users’ geographical location and time information, this
algorithm deeply explores the social relations of potential
users and helps users to seek other users with similar pref-
erence. Then, corresponding recommendations are made by
combining social relations of mobile users, which effectively
solves the recommendation accuracy. In literature [12], the
author comprehensively considered the influences of user
preference, geographical convenience, and friends and put
forward a group purchase discount coupon recommendation
system to promote the commodity with sensitive locations.
It is not difficult to find out through deep analysis of
the abovementioned algorithms that existing personalized
recommendation algorithms still have many deficiencies:
poor expansibility of preference models, inability to adapt
to dynamic change of datasets, resulting in lack of time
information that can be used, and inability to solve cold start
problems very well. Aiming at the abovementioned problems,
based on a comprehensive consideration of factors such as
timeliness of the recommendation system, time weighted
based recommendation prediction formula is adopted and
different weights are given to rating data according to rating
time, so as to improve the recommendation quality of E-
commerce recommendation system.
3. Key Technology of Recommendation System
In order to better solve the problems of data sparsity and
rating time factor, this paper adopts level filling method
to predict the nonrated items and finally combines time
weights in the recommendation prediction stage to improve
the recommendation accuracy of the algorithm.
3.1. Hierarchical Filling Method. Traditional collaborative
filtering algorithm CF sets the nonrated items as the average
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 purely
sets the median as the prediction rating. Different users give
different item rating. The advantage of this method is simple,
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. For

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 3OneTwo 1 Two nThree 1 Three p Three 1 Three qItem 1 Item r· · ·· · ·· · ·· · ·
Figure 1: Commodity hierarchy in E-commerce.
E-commerce websites, each commodity owns its category,
which has a parent category. Namely, commodities in E-
commerce own the concept of level and different commodi-
ties own different hierarchies, shown as in Figure 1. The
level of one commodity is considered in the construction
of rating matrix. For commodities at different levels, 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 of rated items
the total number to be rated items . (1)
Item Rated Frequency (IRF) represents the weight of
rated items and the calculation method is shown as follows:
IRF = log ( SUMallrate
SUMdefaultrate
) , (2)
where SUMallrate represents the total amount of rated data and
automatically filled prediction rating. SUMdefaultrate represents
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 matrix 𝑃.
Output: user rating matrix 𝑄 after the prediction
rating is filled.
Step 1. Calculate RF ∗ IRF of each category in matrix 𝑃.
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 filled
and finally constructs the user-item rating matrix 𝑄.
Through calculating RF∗IRF of specific category of items,
this paper fills scores of the top-𝑁 category items of RF ∗ IRF
instead of simply filling dataset 0, which can reduce the data
sparsity. Finally, the algorithm automatically fills new items
with 3, aiming at solving cold start of new items.
3.2. Improvement of Recommendation Timeliness. CF algo-
rithm does not take the influences of time on rating data
into consideration and it treats item ratings of different users
visited at different moments equally. Interests and preferences
of different users dynamically 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 paper introduces time
function, shown as follows:
𝑓 (𝑡𝑎,𝑖) = 𝑒−𝑡𝑎,𝑖 , (3)
where 𝑡𝑎,𝑖 represents the time that user a has interests in
item 𝑖. Time function 𝑓(𝑡) is a monotone decreasing function
and decreases with the increase of time 𝑡 and time weight
maintains (0, 1). Namely, the newer the data is, the greater the
weight is and the time function is. In this way, the influence
of time on recommendation quality is solved.
3.3. Time Weighted Based Prediction Function. CF algorithm
predicts the rating of item 𝑖 by current user 𝑢 according 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 and 𝑎 and (5) calculates the similarity between users 𝑢 and
𝑎 by 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)
where 𝐼𝑢,𝑎 represents the item set rated by users 𝑢 and 𝑎. 𝑅𝑢,𝑖
and 𝑅𝑎,𝑖, respectively, represent the score of item 𝑖 by users 𝑢
and 𝑎, and 𝑅𝑢 and 𝑅𝑎, respectively, represent the mean score
of all items by users 𝑢 and 𝑎.
Time function 𝑓(𝑡) is added in (4) and the weighted
prediction rating of item 𝑖 by target user 𝑢 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)