AU2021106572A4 - A recommendation system and method for e-commerce using machine learning - Google Patents
A recommendation system and method for e-commerce using machine learning Download PDFInfo
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/30—Semantic analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
Abstract
A RECOMMENDATION SYSTEM AND
METHOD FOR E-COMMERCE USING
MACHINE LEARNING
ABSTRACT
The present invention is related to a recommendation system
and method for e-commerce using machine learning. The objective of
present invention is to solve the abnormalities presented in the prior art
techniques related online product evaluation at ecommerce portal using
user's comments about the product.
26
DRAWINGS
DATA SET MODULE
COMMENT
COMMENT DATA Deep Learning
EXTRACTION DATA Module
COMMENT MODULE # PROCESSING
MODULE
FIGURE 1
27
Description
COMMENT DATA Deep Learning EXTRACTION DATA Module COMMENT MODULE # PROCESSING MODULE
FIGURE 1
[001]. The present invention relates to the technical field of machine
learning based comments analysis.
[002]. The present invention relates to the field of online product
evaluation using comments analysis.
[003]. Particularly, the present invention relates to the field of
computer implemented method for product evaluation.
[004]. More particularly, the present invention is related to a
recommendation system and method for e-commerce using
machine learning.
[005]. The subject matter discussed in the background section should
not be assumed to be prior art merely as a result of its mention in the
background section. Similarly, a problem mentioned in the
background section or associated with the subject matter of the
background section should not be assumed to have been previously
recognized in the prior art. The subject matter in the background
section merely represents different approaches, which in-and-of
themselves may also be inventions.
[006]. Some of the work listed herewith:
[007]. 104809635DYNAMIC INTERNET COMMENT ANALYSIS
METHOD CN - 29.07.2015 notecases G06Q 30/02Appl.No
201510239174.8Applicant Suzhou Lienchiang Information technology
Service Co., Ltd. Inventor Chen Ningbin The invention discloses a
dynamic internet comment analysis method. The dynamic internet
comment analysis method comprises the following steps: S, collecting
opinion comment texts of all commentators, and transmitting opinions of
the commentators to a dynamic analysis platform; S2, uploading
information data of a seller, in response to the opinion comment texts of the commentators, to the dynamic analysis platform; S3, associating information with the dynamic analysis platform by a user; S4, integrating the information in the steps Si, S2 and S3 by the dynamic analysis platform, and performing analysis and excavation. Through the mode, the dynamic internet comment analysis method provided by the invention has the advantages that the source of comment opinions is authoritative, professional, objective, fair, correct and wide, and comprehensively covers experts, users, detectors, industrial elites and the like, misdirection and overspreading of various conventional comment websites and APPs are effectively avoided, and multi-class authoritative and objective popular opinions can be obtained.
[008]. .20100146009METHOD OF DJ COMMENTARY ANALYSIS FOR
INDEXING AND SEARCH US - 10.06.201OInt.Class G06F
17/3OAppl.No 12314193Applicant Concert echnologyInventor Kandekar
KunalA method of conducting a disc jockey (DJ) commentary nalysis for
indexing and search is provided. More specifically, a method is provided
for automatically generating metadata related to commentary of media
segments to enable tagging, storing and context relevant searching.
Speech-to-text conversion technology and audio/video analysis are used to
generate content and metadata. Subject matter is then identified and filtered
to a predetermined set of subjects. Metadata tags and context profiles for the media segments are generated to index the media segments. Moreover, context information of the user is used to generate a context profile of the user in a format similar to that of the media segment. Indexed media segments are searched to match with the user context profile and a relevant media segment is presented to the user.
[009]. 112650906INTERNET USER COMMENT ANALYSIS METHOD
13.04.2021Int.Class G06F 16/951Appl.No 02011535936.6Applicant
CO., LTD. Inventor ZHANG CAIJUN The invention discloses an internet
user comment analysis method based on big data text analysis. The method
comprises the following steps: obtaining and sending user evaluation
information; performing data cleaning processing on the user evaluation
information to obtain and send target pure text information; extracting user
emotion data in the target pure text information, classifying the user
emotion data according to a set rule, and generating and sending a
classification report; performing word segmentation operation on the text
information in the classification report by using a Chinese word
segmentation framework to obtain and send word segmentation text data;
performing word vectorization operation on the word segmentation text
data, and removing interference information to obtain vectorized words; and performing operation on the vectorized words by utilizing a naive
Bayes algorithm, and outputting and displaying an operation result. The
invention further discloses an internet user comment analysis system based
on big data text analysis. According to the invention, the user comment
analysis efficiency and accuracy can be effectively improved.
[0010]. 109977414INTERNET FINANCIAL PLATFORM USER
05.07.2019Int.Class G06F 17/27Appl.No 01910256768.8Applicant
GOLAXY DATA TECHNOLOGY CO., LTD.Inventor SUN ING The
invention discloses an internet financial platform user comment theme
analysis system and method, and relates to the field of natural language
processing. The analysis system comprises a data acquisition module, a
financial word vector learning module, a comment theme generation
module, a user comment classification module and a comment theme
updating module. According to the analysis method,a platform user
impression cluster in a financial forum is utilized to generate a user
comment theme, the user comments related to the internet financial
platform are analyzed based on the user comment theme, and the comment
theme is updated regularly. According to the method, the long-term manual
intervention is not needed, the stable internet financial platform comment
analysis and the topic extraction are achieved by means of the user knowledge easy to obtain in the internet, and the comment topics obtained through analysis are representative, so that the user can be helped to more visuallyknow the internet financial platform through an analysis result.
[0011]. .517958COMPUTER NETWORK USER COMMENTS ANALYSIS
13.04.20021nt.Class G06FAppl.No 0003702Applicant JANSSON
OVEInventor STAHRE JONASThe computer network user comments
analysis technique comprises an e.g. Internet connection process for e.g.
mobile terminals (2), web servers (3), other servers (4) and a data base (5).
The script call code is used to supply the users with at least one comment.
[0012]. 1020140089452METHOD AND APPARATUS FOR ANALYZING
15.07.2014Int.Class G06F 17/00Appl.No 020130000943Applicant SK
PLANET CO., LTD.Inventor ti The present specification discloses a
user interest analysis server. The server includes: an interest estimation unit
for analyzing at least one of a comment written in an Internet site by a user
and an article to which the comment is attached to estimate an interest of
the user; an interest index calculation unit for calculating an interest index
for the estimated interest of the user based on at least one of an attribute
and a frequency of the comment and the article; and an interest determination unit for determining the interest of the user based on the calculated interest index. COPYRIGHT KIPO 2014
[0013]. 1113104740NLINE COURSE COMMENT EMOTION ANALYSIS
MODELCN - 19.06.20201nt.Class G06F 0/30Appl.No
202010065670.7Applicant GUILIN UNIVERSITY OF ELECTRONIC
ECHNOLOGY Inventor ZHANG HUIBING The invention discloses an
online course comment sentiment analysis method based on an activation
pooling enhanced BERT model, and relates to the technical field of online
course evaluation, and the method comprises the steps: constructing an
online course comment sentiment analysis model to encode the context
semantics of words in clauses in a comment text and the logic relation
between the clauses; designing an activation function layer and a
maximum-average pooling layer to solve the overfitting problem of the
BERT model in course comment sentiment analysis; and performing
emotion positiveand negative polarity classification on the online course
comments through the newly added emotion classification layer.
According to the method, the problem of overfitting when a BERT model
is directly applied to do a course comment emotion analysis task is
improved, and meanwhile, an emotion classification layer is added to
analyze the course comment emotion; compared with a traditional course comment sentiment analysis model, the online course comment sentiment analysis model has the advantages of being high in accuracy and easy to train, and the accuracy and AUC value of the model are remarkably improved compared with those of a reference model.
[0014]. 1115984540NLINE COMMENT SENTIMENT ANALYSIS
28.08.2020Int.Class 06Q 10/06Appl.No 202010415979.4Applicant
BEIJING TECHNOLOGY AND BUSINESS UNIVERSITYInventor
ZHANG JIAThe invention relates to the technical field of natural anguage
processing sentiment analysis, and discloses an online comment sentiment
analysis method for fresh cold-chain logistics, which comprises the
following steps of crawling online comment information belonging to
logistics categories from a fresh shopping platform; carrying out data
preprocessing operation on the collected online commentinformation of the
fresh cold-chain logistics; performing attribute feature extraction on the
text data by using word2vec, and constructing a viewpoint emotion
lexicon; constructing a comment viewpoint emotion analysis model,
carrying out evaluation dimension weight calculation, and carrying out
manual annotation on included dimensions and emotion tendencies of the
dimensions; and performing emotion polarity analysis on the evaluation
result, and analyzing corpora related to each dimension of the fresh cold chain online comments by utilizing an online comment emotion analysis model. The online comment sentiment analysis method for fresh cold-chain logistics has the advantages of being capable of assisting in rapidly determining fresh cold-chain logistics service quality management to forma logistics service quality system, thereby perfecting the development of the fresh cold-chain logistics industry.
[0015]. 111950296COMMENT TARGET EMOTION ANALYSIS BASED
ON BERT FINE ADJUSTMENT MODEL CN - 17.11.2020Int.Class
G06F 40/30Appl.No 202010849958.3Applicant GUILIN UNIVERSITY
OF ELECTRONIC TECINOLOGYInventor ZHANG HUIBINGThe
invention discloses comment target emotion analysis based on a BERT fine
adjustment model, which comprises a BCRCRF target extraction model
and a BCCDA target emotion classification model, and ischaracterized in
that the BCCDA target emotion classification model is divided into
experimental results on online course comment emotion analysis, the
BCRCRF target extraction model, the BCCDA targetemotion analysis
model and a real Chinese online course comment data set; the BCRCRF
target extraction model comprises the following steps: 1, performing intra
domain unsupervised training on a BERTpre-training model BCR based on
a large-scale Chinese comment corpus; 2, introducing a CRF layer, adding
grammatical constraints to an output sequence of a semantic representation layer in the BCR model, ensuring the rationality of a dependency relationship between prediction tags, and accurately extracting a comment target in a course comment text; and 3, constructing a BCCDA model containing double attention layers to express the emotion polarity of the course comment target in a classified manner. According to the method, the target emotion contained in the online course comments can be accurately analyzed, and the method has important significance in understanding the emotion change of learners and improving the course quality.
[0016]. 109740154AN ONLINE COMMENT FINE-GRAINED EMOTION
10.05.2019 Int.Class G06F 17/27Appl.No 01811598961.lApplicant
XIDIAN UNIVERSITYInventor GONG MAOGUO The invention
discloses an online comment fine-grained sentiment analysis method based
on multi-task learning. The method comprises the steps that a text
representation matrix is sequentially input into a text sentiment feature
extractor, a coarse-grained sentiment feature extractor and a fine-grained
sentiment feature classifier to obtain a fine-grained sentiment classification
result; the text sentiment feature extractor selects a single-layer CNN
network to extract text sentiment information from the input text
representation matrix to obtain an sentiment representation matrix; wherein thecoarse-grained emotion feature extractor extracts coarse-grained emotion features from an input emotion representation matrix by using a plurality of single-layer CNNs (convolutional neural networks)to obtain coarse-grained emotion feature vectors, and the fine-grained emotion feature classifier performs fine-grained emotion classification on the coarse-grained emotion feature vectors by using amulti-layer full connection neural network. The method has the advantages of accurate classification and short training time, can be used for emotion analysis of multi-level and multi-granularity Internet user comments, and can be used for personalized recommendation, intelligent search or product feedback.
[0017]. 104268197INDUSTRY COMMENT DATA FINE GRAIN
SENTIMENT ANALYSIS METHOD CN - 07.01.2015Int.Class G06F
17/30Appl.No 201410486635.7Applicant
nven(to)P rAf54VTh Inventor A$The invention relates to an
industry comment data ine grain sentiment analysis method. The industry
comment data fine grain sentiment analysis method is applied to Internet
data analysis and comprises obtaining comment data of e-commerce
industry goods and preprocessing the comment data; establishing initial
industry sentiment word libraries and computing distribution of words
under different sentiment polarities through 1-gram and 2-gram;
performing Chinese word segmentation on the comment data; based on the sentiment word libraries established through the 1-gram and the 2-gram, utilizing combined sentiment models to perform word modeling to obtain the probability distribution of the words which belong to different topics under different sentiment distributions; utilizing context information to re determine the sentiment alignment of sentiment words in sentences; performing named entity identification and extracting comment characteristics through conditional random fields to compute the sentiment alignment of comment words of the comment characteristics. The industry comment data fine grain sentiment analysis method computes the sentiment of the comment words through the two dimensions of topic and sentiment to achieve fine grain sentiment analysis on the industry comment data, thereby achieving high precision and interpretability of analysis results.
[0018]. 110825423APP CONTINUOUS IMPROVEMENT METHOD
ANALYSISCN - 21.02.2020Int.Class G06F /70Appl.No
201911049834.OApplicant TIANJIN UNIVERSITYInventor CHEN
SHIZHANThe invention relates to an APP continuous improvement
method based on user online comment emotion and preference analysis.
The APP continuous improvement method is characterized by comprising
the following steps: 1) comment data preprocessing; 2) obtaining of comment emotion; 3) acquiring of preference characteristics; 4) scoring of preference features; 5) evolutionary analysis of emotion and preference characteristics of the time sequence; 6) emotion-preference feature association mapping analysis; and 7) APP evolution and maintenance suggestion recommendation. The method is scientific and reasonable in design, the emotion and preference characteristics of the user can be detected from the user comments timely and accurately, developers are helped to analyze and decide follow-up relatedaffairs about evolution maintenance and follow-up version updating of the APP efficiently, the development efficiency is effectively improved, and the APP use experience of the user is optimized...
[0019]. Groupings of alternative elements or embodiments of the invention
disclosed herein are not to be construed as limitations. Each group member
can be referred to and claimed individually or in any combination with
other members of the group or other elements found herein. One or more
members of a group can be included in, or deleted from, a group for
reasons of convenience and/or patentability. When any such inclusion or
deletion occurs, the specification is herein deemed to contain the group as
modified thus fulfilling the written description of all Markus groups used
in the appended claims.
[0020]. As used in the description herein and throughout the claims that
follow, the meaning of "a," "an," and "the" includes plural reference unless
the context clearly dictates otherwise. Also, as used in the description herein,
the meaning of "in" includes "in" and "on" unless the context clearly
dictates otherwise.
[0021]. The recitation of ranges of values herein is merely intended to serve as
a shorthand method of referring individually to each separate value falling
within the range. Unless otherwise indicated herein, each individual value is
incorporated into the specification as if it were individually recited herein.
All methods described herein can be performed in any suitable order unless
otherwise indicated herein or otherwise clearly contradicted by context.
[0022]. The use of any and all examples, or exemplary language (e.g. "such
as") provided with respect to certain embodiments herein is intended merely
to better illuminate the invention and does not pose a limitation on the scope
of the invention otherwise claimed. No language in the specification should
be construed as indicating any non-claimed element essential to the practice
of the invention.
[0023]. The above information disclosed in this Background section is
only for enhancement of understanding of the background of the
invention and therefore it may contain information that does not form
the prior art that is already known in this country to a person of
ordinary skill in the art.
[0024]. The present invention mainly cures and solves the technical
problems existing in the prior art. In response to these problems, the
present invention provides a recommendation system and method for
e-commerce using machine learning.
[0025]. As one aspect of the present invention relates to a system for
product or service evaluation using comment analysis in an online
ecommerce website, wherein the system comprising A Data extraction
module, used to extract comments of a produce selected by a user,
wherein the Data extraction module extract the raw data in the
comment section; A data set module, comprises plurality of data set
related to review of the product or service; A data processing module,
used to process the data extracted by the comments extraction module with the data set module, wherein the data processing module is used for data preprocessing using a Machine Learning algorithm, wherein the data processing module used a data mining technique to transforms raw data into an understandable and readable format using a tactical and mathematical shortlisting with considering a positive or a negative review using the data set module, wherein the data processing module is used for data Cleaning , wherein the Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset, wherein clearing is performed using removing Stop words, Creating vocabulary and Removing some characters; and A deep learning module, used two deep learning models based on Recurrent Neural
Networks (RNN) and Graph Convolution Network(GCN), wherein the
Recurrent Neural Networks (RNN), is used Long Short Term Memory
(LSTM) Model to capture the sequential behavior of comments,
wherein the Graph Convolution Network (GCN)is used to build a
large and heterogeneous comment word graph which contain word
nodes and comment nodes so that global word co-occurrence can be
explicitly modeled and graph convolution can be easily adapted.
[0026]. The principal objective of the present invention is to provide a
recommendation system and method for e-commerce using machine
learning.
[0027]. Further clarify various aspects of some example embodiments of
the present invention, a more particular description of the invention
will be rendered by reference to specific embodiments thereof which
are illustrated in the appended drawings. It is appreciated that these
drawings depict only illustrated embodiments of the invention and are
therefore not to be considered limiting of its scope. The invention will
be described and explained with additional specificity and detail
through the use of the accompanying drawings.
[0028]. In order that the advantages of the present invention will be
easily understood, a detailed description of the invention is discussed
below in conjunction with the appended drawings, which, however,
should not be considered to limit the scope of the invention to the
accompanying drawings, in which:
[0029]. Figure 1 shows an exemplary representation of system for
product evaluation through comments analysis using machine
learning, according to the present invention.
[0030]. The present invention discloses a recommendation system and
method for e-commerce using machine learning.
[0031]. Figure 1 shows the exemplary representation of a
recommendation system and method for e-commerce using machine
learning, according to the present invention.
[0032]. Although the present disclosure has been described with the
purpose of two smart frameworks for providing privacy and protection
in block chain based private transactions using cloud computing
approach, it should be appreciated that the same has been done merely
to illustrate the invention in an exemplary manner and to highlight any
other purpose or function for which explained structures or
configurations could be used and is covered within the scope of the
present disclosure.
[0033]. An a recommendation system and method for e-commerce using
machine learning is disclosed.
[0034]. The system for product or service evaluation using comment
analysis in an online ecommerce website, comprises A Data
extraction module, A data set module, A data set module and A data
processing module.
[0035]. The Data extraction module is used to extract comments of a
produce selected by a user, wherein the Data extraction module
extract the raw data in the comment section.
[0036]. A data set module comprises plurality of data set related to
review of the product or service.
[0037]. A data processing module is used to process the data extracted
by the comment's extraction module with the data set module,
wherein the data processing module is used for data preprocessing
using a Machine Learning algorithm.
[0038]. The data processing module used a data mining technique to
transforms raw data into an understandable and readable format
using a tactical and mathematical shortlisting with considering a
positive or a negative review using the data set module.
[0039]. The data processing module is used for data Cleaning. The Data
cleaning is the process of fixing or removing incorrect, corrupted,
incorrectly formatted, duplicate, or incomplete data within a dataset,
wherein clearing is performed using removing Stop words, Creating
vocabulary and Removing some characters.
[0040]. A deep learning module is used two deep learning models based
on Recurrent Neural Networks (RNN) and Graph Convolution
Network(GCN), wherein the Recurrent Neural Networks (RNN), is
used Long Short Term Memory (LSTM) Model to capture the
sequential behavior of comments.
[0041]. The Graph Convolution Network (GCN)is used to build a large
and heterogeneous comment word graph which contain word nodes
and comment nodes so that global word co-occurrence can be
explicitly modeled and graph convolution can be easily adapted.
[0042]. As another embodiment of present invention a method for
Product or service Evaluation Using Comment Analysis in online
ecommerce website is disclosed in this disclosure.
[0043]. The method comprising steps of extracting a plurality of
comments viewpoint in user comment section of ecommerce website
using a data extraction module.
[0044]. A data set module is prepared using a plurality of comment
viewpoint is input in sentiment classification model.
[0045]. The semantic similarity is calculated between each comment
viewpoint in each emotion class comment using a deep learning
module using the deep learning module using data processing
module.
[0046]. An index on product is assigned according to the result of the
data processing module.
[0047]. The figures and the foregoing description give examples of
embodiments. Those skilled in the art will appreciate that one or
more of the described elements may well be combined into a single
functional element. Alternatively, certain elements may be split into
multiple functional elements. Elements from one embodiment may
be added to another embodiment.
[0048]. For example, order of processes described herein may be
changed and are not limited to the manner described herein.
Moreover, the actions of any block diagram need not be
implemented in the order shown; nor do all of the acts need to be
necessarily performed. Also, those acts that are not dependent on
other acts may be performed in parallel with the other acts. The
scope of embodiments is by no means limited by these specific
examples.
[0049]. Although implementations of the invention have been described
in a language specific to structural features and/or methods, it is to
be understood that the appended claims are not necessarily limited to
the specific features or methods described. Rather, the specific
features and methods are disclosed as examples of implementations
of the invention.
Claims (2)
1. A system for product or service evaluation using comment
analysis in an online ecommerce website, wherein the system
comprising:
A Data extraction module, used to extract comments of a produce
selected by a user, wherein the Data extraction module extract the raw
data in the comment section;
A data set module, comprises plurality of data set related to review of
the product or service;
A data processing module, used to process the data extracted by the
comments extraction module with the data set module, wherein the data
processing module is used for data preprocessing using a Machine
Learning algorithm, wherein the data processing module used a data
mining technique to transforms raw data into an understandable and readable format using a tactical and mathematical shortlisting with considering a positive or a negative review using the data set module, wherein the data processing module is used for data Cleaning , wherein the Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset, wherein clearing is performed using removing Stop words,
Creating vocabulary and Removing some characters; and
A deep learning module, used two deep learning models based on
Recurrent Neural Networks (RNN) and Graph Convolution
Network(GCN), wherein the Recurrent Neural Networks (RNN), is
used Long Short Term Memory (LSTM) Model to capture the
sequential behavior of comments, wherein the Graph Convolution
Network (GCN)is used to build a large and heterogeneous comment
word graph which contain word nodes and comment nodes so that
global word co-occurrence can be explicitly modeled and graph
convolution can be easily adapted.
2. The method for Product or service Evaluation Using Comment
Analysis in online ecommerce website wherein the method comprising
steps of:
Extracting a plurality of comments viewpoint in user comment
section of ecommerce website using a data extraction module ;
Preparing a data set module using a plurality of comment
viewpoint is input in sentiment classification model Calculate the
semantic similarity between each comment viewpoint in each
emotion class
comment using a deep learning module using the deep learning
module using data processing module, and
Assigning an index on product according to the. Result of the data
processing module.
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| AU2021106572A AU2021106572A4 (en) | 2021-08-23 | 2021-08-23 | A recommendation system and method for e-commerce using machine learning |
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| Application Number | Priority Date | Filing Date | Title |
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| AU2021106572A AU2021106572A4 (en) | 2021-08-23 | 2021-08-23 | A recommendation system and method for e-commerce using machine learning |
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