International Core Journal of Engineering 2020-26 | Page 126
2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)
CIMR: Intelligent Cellphones Recommendation for
Consumers Based on Machine Learning Techniques
Ruxun Xiang 1 , Min Fu 2, 3
1
Chengdu University of Information Technology, Chengdu, China
2
Alibaba Group, Hangzhou, China
3
School of Computing, Macquarie University, Sydney, Australia
[email protected], [email protected]
questions related to the following aspects: 1) informants’
identity and personal information, such as their ages, and the
academic degree; 2) configurations of the phone they are
using or they have used, such as operating system, and
camera; 3) satisfaction degree about their present cellphone
and their ideal cellphones. Furthermore, CIMR is based on
techniques of machine learning.
Abstract—Cellphones are electronic devices that people use
most in daily life. There are various kinds of cellphones in the
market nowadays, while ordinary consumers are dazzled by
the huge cellphone market and do not know how to choose a
suitable one. Hence, it is of great importance to help them in
doing this kind of selection to satisfy their needs. This paper
proposes
a
machine
learning
based
cellphones
recommendation approach, CIMR, for consumers to pick up
the most suitable cellphones that can mostly meet their desired
requirements. We evaluate CIMR by using thousands of real
consumers all over the world with two proposed methods:
Gradient Boosting Decision Tree (GBDT) and Random Forest
(RF). The experimental results show that the accuracy rate for
recommending cellphones by using our method is 95.87%,
which exceeds the existing old methods by up to 5.49%.
First, we analyze tens of thousands of realistic surveyed
data related to consumers, cellphone configurations and
cellphone usage. Second, we apply machine learning
methods to train the surveyed data and obtain a model that
represents the relationship between consumers and
cellphones. Next, we apply a Random Forest algorithm and a
Gradient Boosting Decision Tree algorithm to figure out the
most suitable cellphones for each consumer that satisfy the
requirements of him/her [5][6]. According to our knowledge,
it is one of the first trials that machine learning techniques
are applied on cellphone recommendation.
Keywords—Machine learning, Cellphone Recommendation,
Data Mining
I. I NTRODUCTION
We evaluate the feasibility and validity of CIMR through
real-world scenarios-based experiments. In the experiments,
we evaluate four sets of recommendation approaches using
our real-world dataset, and these four approaches are: 1) two
existing old methods: Naïve Bayes and Neural Network [15]
[16]; 2) our newly proposed approach CIMR: Random Forest
and Gradient Boosting Decision Tree. We make a
comparison between the four approaches. Next, we select the
better one of our methods and the better one of the existing
methods to compare the cellphone recommendation accuracy
results. The experimental results show that CIMR can
increase the accuracy rate for recommending cellphones to
95.87%, which exceeds the existing old method by 5.49%.
Advanced cellphone technologies have been changing
rapidly and a large number of people purchase cellphones to
meet their daily requirements, which leads to the popularity
of cellphones [1]. The PC market was exceeded by the
cellphone market in 2011 for the first time [1]. A related
survey shows that the cellphones sold in the third quarter of
2013 increased by 44% [1]. Large-scale worldwide mobile
industry giants, such as Apple and Huawei, are introducing
new cellphones constantly with original functionalities and
hardware facilities to attract more customers [2][3][4].
The common ways for a consumer to choose a cellphone
are usually by watching commercials or asking friends and
shopping guides for suggestions. There are several
drawbacks: 1) Commercials exaggerate the descriptions of
cellphones; 2) People’s friends do not understand their true
demands; 3) Shopping guides make recommendations
according to their own interests. There lacks a direct method
to determine the accurate conditions and demands for each
consumer.
The contributions of this paper are: 1) We propose an
original and intelligent machine learning based approach for
recommending appropriate cellphone for consumers; 2) We
propose a methodology to build up a connection between
consumers’ personal situations and cellphones; 3) We
propose a systematic evaluation way in the experiment to
demonstrate how to evaluate the validity of our method.
In this paper, we propose a novel and intelligent approach,
named CIMR, for the following motivations: 1)
recommending suitable cellphones to consumers for a
maximization in budget use and an enjoyable purchase
experience; 2) sending feedbacks of customers to
manufacturers to make adjustments in cellphone
configurations and sales strategies. CIMR is based on
analysis of online and offline data we surveyed during the
period from Sept. to Nov. of 2018. The survey contains
978-1-7281-4691-1/19/$31.00 ©2019 IEEE
DOI 10.1109/AIAM48774.2019.00028
The remainder of this paper is: Section II introduces the
background; Section III discusses the related work; Section
IV illustrates our proposed method of recommending
cellphones; Section V presents the experimental evaluation;
Section VI provides the conclusion and our future work.
We
104
introduce
II. B ACKGROUND
the importance
of
a
cellphone