1. Introduction

In recent years, the rapid development of artificial intelligence technology has profoundly changed the ecosystem of internet applications, driving innovation in areas such as search engines, recommendation systems, and automated customer service. With breakthroughs in AI technology related to big data processing, machine learning, and natural language processing, the application of AI in the internet has become a hot topic of research in both academia and industry. Research by Zhang and colleagues indicates that applying artificial intelligence in recommendation systems can significantly improve the accuracy and relevance of recommendations, particularly excelling in addressing users' personalized needs [1]. Furthermore, AI-driven customer service automation helps enhance customer satisfaction, provides higher-quality services to users, improves companies' management of customer relationships, and promotes business development and growth [2]. This study reviews the major advancements of artificial intelligence in internet applications through a literature review, particularly focusing on the current state and future trends in recommendation systems and automated customer service. The aim of this paper is to contribute to the research on the application of artificial intelligence in the internet domain to some extent, summarizing current application outcomes and providing references for future research. This review aids in promoting further development in this field, especially in enhancing service personalization and optimizing user experience.

2. Literature review

2.1. AI in Recommendation Systems

Recommendation systems are one of the important areas of AI technology in internet applications. Their core function is to predict users' future needs based on historical behavior data and provide personalized recommendations. Zhang, Q. and colleagues discussed collaborative filtering and content-based recommendation systems in detail in their research, noting that while these traditional methods are effective, they have certain limitations in addressing data sparsity and cold start issues, which can affect the accuracy and diversity of recommendations [1].

To address these challenges, Lai Xiaoxin and other scholars proposed solutions that leverage alternative information sources, such as social network data and user profiling data, along with collaborative filtering algorithms and content-based recommendation algorithms [3]. Beheshti and colleagues further expanded this field by exploring the latest advancements in personalized recommendation systems. They emphasized the advantages of AI in handling multidimensional data and complex user behavior patterns, particularly in the application of natural language processing (NLP) technologies [4]. Additionally, the research by Gazdar and Hidri examined the application of novel similarity measures in collaborative filtering, proposing methods to enhance computational efficiency while maintaining the accuracy of recommendation systems [5]. These studies indicate that, with advancements in AI technology, recommendation systems are evolving towards greater efficiency and intelligence.

2.2. AI in Automated Customer Service

With the surge in internet users, the demand for customer service is continuously expanding. Nicolescu and colleagues explored how AI-driven automated customer service can enhance customer satisfaction while reducing operational costs. Their research indicates that AI customer service can handle most routine inquiries and provide 24/7 support, significantly improving the speed and efficiency of service responses [6].

At the same time, Chaturvedi and Verma examined how to balance automation and humanized service in customer support. They noted that while AI customer service excels in handling standardized tasks, it still faces challenges when addressing tasks that involve emotions or complex needs [7]. Additionally, Castillo and others investigated the potential "dark side" of AI in customer service, highlighting the risk of declining customer experiences due to a lack of human interaction [8].

2.3. AI in content generation and SEO

Content generation is another important area of AI in internet applications. In recent years, with the development of technologies such as natural language processing and generative adversarial networks (GANs), the application of AI in automated content generation has become increasingly widespread. Davenport explored how AI is changing the future of content generation and marketing strategies, noting that AI-generated content has significant advantages in improving content production efficiency and personalization, while also raising issues related to copyright and ethics [9].

In terms of search engine optimization (SEO), AI technology primarily enhances user experience by improving search algorithms, optimizing user queries, and increasing the relevance of search results. Lee and others studied the application of AI in search engines, emphasizing the importance of machine learning algorithms in understanding user intent and providing personalized search results [10]. Their research also discussed the potential of AI technologies in handling vast amounts of data and processing real-time queries.

3. Case Analysis

The use of artificial intelligence in internet applications has become increasingly common, especially in demonstrating strong potential for enhancing user experience, optimizing business processes, and reducing operational costs. By analyzing multiple specific cases, we can gain a better understanding of how AI technology is applied in different contexts and the impacts it brings. Here are three typical cases: Amazon's automated customer service, Netflix's personalized recommendation system, and Baidu's intelligent search engine.

3.1. Alexa of Amazon

As a global leader in e-commerce, Amazon has consistently leveraged AI technology to enhance user service experiences. Its automated customer service system and voice assistant Alexa extensively utilize AI technology, significantly improving the efficiency of customer service.

First, by deploying an AI-based automated customer service system, Amazon can handle a large volume of routine customer inquiries, such as order tracking, product recommendations, and returns. The automated customer service employs natural language processing (NLP) technology to analyze user language input, identify user needs, and provide relevant solutions. This not only alleviates the pressure on human customer service representatives but also dramatically shortens customer wait times. According to Amazon's reports, the AI customer service system can address over 80% of common inquiries, greatly reducing operational costs. Additionally, Amazon's AI customer service system operates 24/7, free from time and location constraints, significantly enhancing customer satisfaction.

Secondly, Alexa, Amazon's voice assistant, excels in controlling smart home devices, playing music, and providing information. By integrating voice recognition and natural language understanding technologies, Alexa can engage in conversations with users and assist them in completing various tasks. Not only can Alexa perform simple tasks, but it also adapts to users' behavior patterns and preferences over time through continuous machine learning algorithms, offering more personalized service. By constantly learning user commands and habits, Alexa can respond increasingly accurately to user needs, thereby enhancing the user experience. Amazon's success story demonstrates that the application of AI technology can significantly improve the efficiency of customer service and user satisfaction while achieving service personalization and automation through intelligent means.

3.2. Netflix's personalized recommendation system

Netflix is one of the largest streaming platforms in the world, and its success is largely attributed to its advanced personalized recommendation system. This system utilizes artificial intelligence and big data technology to analyze user viewing history, ratings, search behavior, and other data to recommend films and shows that each user may find interesting.

Netflix's recommendation system primarily relies on collaborative filtering algorithms and content-based recommendation algorithms. Collaborative filtering analyzes users' viewing behaviors alongside those of similar users to predict content they might enjoy. Meanwhile, content-based algorithms assess the characteristics of the films and shows themselves, such as genre, actors, and directors, to match users' preferences. The combination of these two methods enables Netflix to identify the most suitable content for each user among thousands of films and series, greatly enhancing the viewing experience.

The success of Netflix's recommendation system is reflected not only in user satisfaction but also in its ability to effectively increase user engagement. According to internal statistics, about 80% of the content watched by users comes from the recommendation system. This indicates that the AI recommendation system can accurately predict user preferences while helping the platform boost content visibility, ultimately improving user retention and subscription renewal rates. By continuously optimizing its AI algorithms, Netflix can dynamically adjust recommendation results, ensuring that users consistently discover new content that interests them. This AI-driven personalized recommendation system has helped Netflix maintain a strong competitive edge in the fiercely competitive streaming market.

3.3. Baidu's intelligent search engine

As China's largest search engine company, Baidu is also at the forefront of AI technology application in the industry. Baidu's intelligent search engine leverages advanced technologies such as deep learning, natural language processing, and knowledge graphs to significantly enhance the user search experience.

First, Baidu's intelligent search engine uses deep learning technology to analyze users' search intent, rather than merely matching keywords. For instance, when a user searches for a particular question, Baidu can infer the user's true desired answer based on the keywords they input. This capability stems from the extensive training of Baidu's AI system on massive datasets, allowing it to recognize the semantic relationships and intentions behind user searches. Additionally, Baidu employs knowledge graph technology to construct a vast information network of different entities and relationships. When users pose complex queries, Baidu can provide more precise answers rather than just a list of web links.

Second, Baidu has introduced personalized search features in its search engine. By analyzing users' search histories, browsing behaviors, and interests, Baidu can deliver personalized search results. For the same keyword, different users may receive varying search results because the search engine customizes recommendations based on individual interests and behavioral patterns. This personalized service not only enhances user experience but also increases user engagement.

Finally, Baidu has further optimized user experience through its voice search functionality. With the widespread adoption of smartphones, an increasing number of users choose to conduct searches via voice input. Baidu's voice search feature combines voice recognition and natural language understanding technologies to quickly and accurately convert users' speech into search queries and provide precise search results. This convenient and fast search method not only improves search efficiency but also offers users a more user-friendly experience.

4. Conclusion

By analyzing Amazon's automated customer service and voice assistant Alexa, Netflix's personalized recommendation system, and Baidu's intelligent search engine, we can see the extensive impact and potential of artificial intelligence in internet applications. AI enhances user experience, optimizes processes, and reduces costs; however, it also reveals certain limitations. While Amazon's AI customer service is efficient, it still relies on human intervention to address complex and emotional needs, indicating a need for future improvements in emotional understanding capabilities. Netflix's recommendation system faces issues such as filter bubbles and cold start problems, and future efforts could focus on integrating more data sources to enhance recommendation diversity and improve the experience for new users. Baidu's search engine, while providing personalized results, also raises concerns about privacy and security, highlighting the need for greater algorithm transparency and data protection.

Overall, the application of artificial intelligence in the internet holds great promise, but there remains room for improvement in handling complex needs, enhancing human interaction, and protecting user privacy. Future developments should emphasize the integration of technology with humanized experiences while ensuring privacy security and fostering ongoing innovation in AI technology.

This paper explores the development of artificial intelligence in internet applications such as recommendation systems, automated customer service, and content generation through a literature review, but it lacks depth and breadth in certain areas. Although it addresses multiple studies, the analysis of specific technical implementations and challenges in these fields remains somewhat superficial and brief. Additionally, the discussion on future research directions is rather broad, lacking concrete recommendations and empirical support. Future research should strengthen comparative analyses of different algorithms and explore innovative applications of AI across more industries to promote further development in this field.