Personalized Recommendation and Consumer Decision-Making:

User Behavior Analysis Based on Taobao Platform

Ying Yang

Economics and Management, South China Normal University, 510631 Guangdong, China

Keywords: Personalized Recommendation, User Behavior, e-Commerce Platform.

Abstract: With the rapid development of e-commerce, personalized recommendation systems have become an

important tool to improve platform user experience and sales performance. Taobao platform utilizes big data

technology to analyze consumer behavior patterns and push products that align with their interests and needs,

effectively stimulating consumers' purchasing desires. However, the widespread use of this technology also

comes with potential risks such as privacy breaches and information overload. This study employs literature

analysis and case study methods to comprehensively examine the functions of personalized recommendation

systems and the consumer experience. By reviewing existing literature, the study delves into the dual impact

of personalized recommendations on consumers' purchase intentions, using Taobao as a case example. The

research shows that personalized recommendation systems play a significant positive role in improving

decision-making efficiency and stimulating consumption desire. However, issues such as information

overload and privacy concerns under precise personalized recommendations have had a negative impact on

consumers' purchasing desires and trust in the platform. Based on these findings, the paper proposes

recommendations from three aspects: recommendation algorithms, information overload, and privacy

protection, to support the healthy development of personalized recommendation mechanisms on e-commerce

platforms.

1 INTRODUCTION

This study focuses on the era of big data, where

personalized recommendations have become an

important marketing tool for e-commerce platforms.

However, consumers still face challenges in

extracting relevant product information from the vast

amount of data, and providing valuable product

information to consumers remains a significant

challenge in the research on personalized

recommendations. At the same time,

recommendation systems with commercial objectives

have raised a series of ethical risks, especially

regarding the protection of user privacy. These

negative phenomena have attracted widespread social

concern. Currently, the academic community has

shown significant interest in recommendation

systems, conducting extensive research and practical

work to propose solutions for personalized

recommendations. This has led to the formation of an

interdisciplinary research field that spans data

mining, psychology, consumer behavior, and other

related disciplines.

This study explores the personalized

recommendation systems of e-commerce platforms

and is highly significant for promoting the continuous

and healthy development of such platforms. The

research aims to discuss how personalized

recommendations impact consumers' decision-

making processes. Therefore, the study focuses on

three main aspects: First, it examines how e-

commerce platforms should simplify information and

match the right products with the users who genuinely

need them when providing personalized

recommendations. Second, it addresses the need for

e-commerce platforms to be mindful of the potential

negative psychological responses from consumers as

they are exposed to large volumes of recommended

products. It emphasizes the importance of actively

reducing user aversion caused by information

overload. Finally, the study discusses how e-

commerce platforms should maintain consumer trust

during the collection of user data and reduce

consumer anxiety, ensuring that privacy concerns are

adequately addressed.

This study adopts a literature research method to

gain an in-depth understanding of current theories and

Yang, Y.

Personalized Recommendation and Consumer Decision-Making: User Behavior Analysis Based on Taobao Platform.

DOI: 10.5220/0014074300004942

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Applied Psychology and Marketing Management (APMM 2025), pages 80-84

ISBN: 978-989-758-791-7

practical research on personalized recommendation

systems, particularly their application in e-commerce

platforms. By reviewing the work of scholars both

domestically and internationally on the algorithmic

models of personalized recommendations and their

impact on consumer behavior, the study identifies the

mechanisms through which personalized

recommendation systems influence consumer

decision-making. This approach helps establish a

solid theoretical framework for the research. At the

same time, a case study method is employed, focusing

on Taobao's personalized recommendation system.

Through a detailed analysis of Taobao's

recommendation algorithms, data collection and

processing procedures, and user feedback

mechanisms, the study examines how personalized

recommendations affect consumer cognition,

emotions, and behavior, and how these influences

ultimately impact their purchasing decisions, either

directly or indirectly.

The ultimate goal of this study is to provide

strategies for creating an effective and innovative

personalized marketing environment for e-commerce

platforms, helping them enhance the consumer

shopping experience while increasing sales. To

achieve this, the study empirically explores the

information adoption willingness of users on e-

commerce platforms, using Taobao as a case study.

2 LITERATURE REVIEW

Personalized recommendations are widely used on e-

commerce platforms, significantly influencing

consumer decisions. Existing literature has explored

personalized recommendations in terms of

information simplification, recommendation

algorithms, and data privacy.

In terms of information simplification, research

indicates that in the context of information overload,

recommendation systems are essential tools for

meeting the personalized needs of online users and

alleviating the burden of information overload (Lai et

al., 2019). Scholars such as Li Zhi argues that

personalized recommendations can reduce customer

search costs while generating more optimal search

results, greatly facilitating the consumer experience

(Li and Sun, 2019).

Regarding recommendation algorithms, research

indicates that personalized recommendations use

mathematical algorithms to calculate the degree of

match between users and each piece of marketing

information, ensuring that the recommended results

align with both the user's preferences and the

attributes of the marketing content (Li, 2024).

Additionally, existing studies focus on improving

recommendation algorithms. Scholars such as Luo

Xian and others, building on traditional collaborative

filtering, combine user clustering and item clustering

to reconfigure similarity measurement methods and

prediction scoring calculations, proposing an

enhanced collaborative filtering algorithm (Luo et al.,

2018).

From the perspective of data privacy, studies have

shown that personalized recommendations and

consumers' privacy concerns have a significant

impact on purchase intentions, with privacy concerns

acting as a mediator between personalized

recommendation systems and purchase decisions

(Hongqi et al., 2022). Moreover, existing research

suggests that personalized recommendation systems

face challenges related to data privacy and security

while providing personalized services to users (Chen

et al., 2024). Researchers generally agree that while

the development and utilization of data resources is

an inevitable trend, it is essential to carefully design

paths for information privacy protection during this

process (Li, 2017).

The above literature indicates that effective

personalized recommendations can help consumers

simplify information and stimulate their consumption

desire. Researchers have observed both the positive

impacts and the associated negative issues,

particularly about data privacy concerns. This study

further reveals the impact of excessive platform

recommendations on consumers and proposes

solutions to information overload. It offers insights

for e-commerce platforms to address the issue of

negative psychological reactions from consumers

towards platform algorithms.

3 CASE ANALYSIS

3.1 Analysis of Personalized

Recommendation Systems

3.1.1 Historical Behavior-Based

Personalized Recommendation

The personalized recommendation system of Taobao

primarily relies on users' historical behavior to

suggest relevant products. These historical behaviors

include users' browsing history, search history,

shopping cart contents, and purchase records. Taobao

analyzes this data to assess users' interest preferences,

subsequently recommending products that are likely

to meet their needs.

Personalized Recommendation and Consumer Decision-Making: User Behavior Analysis Based on Taobao Platform

For example, if a user frequently browses and

purchases home-related products such as furniture,

lighting, and decorations, Taobao's recommendation

system will leverage this user's browsing and

purchase history to suggest similar items, such as

furniture accessories and home cleaning products.

This approach not only helps users discover more

items of interest but also enhances the conversion rate

of purchases.

3.1.2 Real-Time Behavior-Based Timely

Personalized Recommendation

In addition to utilizing historical data, Taobao also

generates dynamic recommendations based on users'

real-time behaviors on the platform, such as the

products they are currently viewing or items added to

their shopping cart. One of the key advantages of e-

commerce recommendation systems is their ability to

collect data on users' interests and actively generate

personalized recommendations based on these

preferences, with real-time updates (Li and Liu,

2004). This real-time recommendation capability can

effectively guide users during critical moments in

their shopping decisions, offering more options for

complementary or alternative products, and

enhancing both the urgency and desire to purchase.

When a user is browsing an electronic product, the

recommendation system will suggest related

accessories or other similar products based on the

characteristics of the viewed item. After a user views

a specific smartphone, the system will immediately

suggest related products such as phone cases,

chargers, or headphones. This approach not only

broadens the consumer's selection but often

encourages additional purchases within the same

shopping session. Taobao also leverages time-limited

promotional campaigns to stimulate purchase

decisions. While browsing a product, the platform

may display alerts such as "limited-time offer" or

"only two items left," creating a sense of urgency and

prompting the user to complete their purchase.

3.1.3 Budget-Based Personalized

Recommendation

Taobao also employs personalized recommendation

systems to suggest products that align with

consumers' budget and price preferences, while

taking into account their price sensitivity in

recommending promotional offers. For instance, the

system may recommend discounted items, flash sales,

or special offers to attract price-sensitive consumers.

If a user frequently purchases moderately priced

clothing and regularly pays attention to discount

information during their shopping process, the

Taobao system will recommend more discounted

products that fit the user's budget based on their

consumption patterns. Additionally, if the user has

browsed clothing items ahead of major promotional

events, such as “Double Eleven”, the system will

push time-limited discounts, coupon offers, and other

promotional information to stimulate the user to make

a purchase during the sales period.

3.2 Negative Impacts of Personalized

Recommendation

3.2.1 Information Overload and Choice

Fatigue

Personalized recommendation systems push products

based on users' historical behaviors, interest

preferences, and real-time data. However, this

recommendation method can sometimes lead to the

problem of information overload. When consumers

are faced with a large number of repetitive or similar

recommendations, they may feel overwhelmed or

experience choice fatigue, which can ultimately result

in decision delay or even abandonment of the

purchase.

For instance, if a consumer browses a large

number of home appliance products on Taobao and

frequently clicks on or adds them to their favorites,

the recommendation system will continuously push

similar home appliance items. If the frequency of

these recommendations is too high, consumers may

feel inundated with excessive suggestions, leading to

an inability to make a purchase decision. Information

overload complicates the user experience, which in

turn can negatively affect the consumer's intention to

buy.

For instance, if a consumer browses numerous

home appliance products on Taobao and frequently

clicks on or adds them to their favorites, the

recommendation system will continuously suggest

similar home appliance items. If these

recommendations are pushed too frequently, the

consumer may feel overwhelmed by the sheer volume

of suggestions, ultimately hindering their ability to

make a purchase decision. Information overload

complicates the user experience, which can

negatively impact the consumer's purchase intention.

3.2.2 Privacy Breaches and Data Security

The accuracy of Taobao's personalized

recommendations relies on a large amount of user

personal data, including search history, browsing

APMM 2025 - International Conference on Applied Psychology and Marketing Management

history, purchase behavior, and location data. These

data are used to push products that users are likely to

be interested in. However, the widespread collection

and use of such data pose a risk of privacy breaches.

If the platform fails to implement adequate data

protection measures, users' personal information may

be exposed or misused by unauthorized third parties.

In June 2021, the SuYang District Court of

Shangqiu, Henan, heard a case involving the leakage

of Taobao user information. The defendant developed

software to scrape personal information, including

digital IDs, Taobao usernames, phone numbers, and

other data, totaling over 1.1 billion records. This

information was then provided to the defendant for

commercial marketing purposes, resulting in a profit

of 340,000 yuan. These incidents undoubtedly

amplified consumer distrust of the platform. As a

result, some consumers may choose to disable

privacy settings on Taobao, significantly reducing the

effectiveness of the platform's personalized

recommendations.

4 DISCUSSION

Through the case analysis of Taobao's personalized

recommendation system, it is evident that Taobao

profoundly influences consumers' purchasing

decisions through its precise recommendation

algorithms and data mining technologies. Whether it's

the combination of recommendations based on

historical behavior, real-time behavior analysis, or

precise price recommendations, these strategies have

significantly enhanced the user shopping experience

and improved the platform's sales conversion rate.

Taobao's personalized recommendations have

transformed the consumer decision-making process

and also provided valuable insights for other e-

commerce platforms.

However, accurate predictions do not necessarily

equate to good predictions. The over-precision of

personalized recommendation systems may limit

users' choices, leading to a homogenized information

environment that narrows the range of options

available to users. This phenomenon is particularly

evident on platforms like Taobao, where consumers'

perspectives are confined by personalized

recommendations, potentially preventing them from

discovering a more diverse array of product choices.

Excessive information push can also lead to user

resistance, as consumers may experience a series of

psychological issues. A large volume of data

recommendations on the homepage can create

pressure, leading to information fatigue. Consumers

are more likely to accept personalized

recommendations when they feel their personal

information is secure. At the same time, consumers

will be more likely to accept personalized

recommendations from e-commerce platforms based

on consumer needs and purchasing preferences (Yang

et al., 2019). For instance, after a consumer places an

order, merchants may use SMS (short message

service) to recommend additional products, or private

items added to the shopping cart may appear in

homepage recommendations. Such practices can lead

consumers to experience a crisis of trust.

5 SUGGESTION

5.1 Content and Interface

Regarding recommendation effectiveness, timely

updates and upgrades to the technical services are

essential. The content of personalized

recommendations should be optimized and tailored to

the socio-economic context, focusing on key content

and specialized services. Regular customer

satisfaction surveys should be conducted to improve

recommendation methods and update

recommendation data, ensuring alignment with

societal and consumer demands (Yang et al., 2019).

As for the recommendation interface, it is crucial to

design a more user-friendly layout to provide a

positive shopping experience for consumers. Instead

of simply listing the products or services, brief

descriptions should accompany them, and the

arrangement should reflect consumers' preferences.

This will enhance the efficiency of information

retrieval. Additionally, incorporating multimedia

formats such as images, audio, and video can enrich

the contextual experience, deepen consumers'

impressions of the products and services, and

generate interest and pleasure, ultimately increasing

their willingness to adopt the recommendations

(Wang et al., 2021).

5.2 Value Orientation and Security

Mechanisms

In the face of information overload, Taobao must

adopt a user-centered approach by optimizing

algorithms and providing higher-quality, more

relevant recommendation information (Li & Wang.

2024). For example, providing multi-channel services

such as preference customization, homepage

customization, or allowing consumers to set which

channels they want to receive information from, can

Personalized Recommendation and Consumer Decision-Making: User Behavior Analysis Based on Taobao Platform

maximize the value of recommended content.

Effective personalized recommendations require high

consumer trust in the shopping platform’s

recommendation system. The greater the consumer's

trust and acceptance of the system, the more likely

they are to engage with the recommendations.

Therefore, when it comes to privacy issues, Taobao

must undoubtedly enhance the transparency of its

information collection practices. It should actively

improve the privacy policy framework and details,

such as user authorization, data usage methods, and

validity periods, making these aspects more detailed

and transparent, thereby increasing user trust in the

platform (Li & Wang. 2024). This means promptly

informing users about the privacy permissions

involved in personalized recommendations and

actively guiding them on how to disable privacy-

related services during their first use. Additionally, in

the event of a privacy breach, there should be channels

for users to hold the platform accountable, thus

reinforcing user trust in the platform.

6 CONCLUSION

This study explored the impact of the personalized

recommendation system of e-commerce platforms on

consumer decision-making and found that it played a

significant positive role in improving decision-

making efficiency and stimulating consumer desire.

Personalized recommendations help consumers

simplify information and use precise

recommendation mechanisms to recommend

products to the right customers. The information

overload and privacy protection issues that arise

under precise personalized recommendations have a

negative impact on consumers' purchasing desire and

trust in the platform.

The conclusions of this study hold significant

reference value for future research, providing a

theoretical foundation for the academic community to

further explore the ethical issues surrounding

personalized recommendation systems, particularly

in the areas of data protection and privacy

management. This study offers practical guidance to

platforms and merchants in designing personalized

recommendation services, reminding them to

strengthen consumer privacy protection and

implement appropriate information filtering while

improving the user experience. Additionally, this

research provides valuable insights for further studies

on consumer behavior, especially regarding the

impact of information overload within personalized

recommendation mechanisms on the consumer

decision-making process.

However, this study mainly focuses on the impact

of personalized recommendation systems on

consumers' information adoption intention and

purchasing behavior. Future research can also explore

the psychological impact of personalized

recommendations on consumers' purchasing intention

from the perspective of consumers' sensitivity to

personalized recommendations for different types of

products and the information narrowing of

personalized recommendations.

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