Web Search Optimization Using Deep Learning Techniques
Web search optimization involves enhancing a website’s visibility and ranking on search engine results pages (SERPs) through various techniques, including keyword optimization, content creation, link building, and user experience improvements. Deep learning techniques, such as natural language processing (NLP) and neural networks, can play a crucial role in optimizing web search. NLP models can analyze user queries and understand their intent more effectively, allowing for better keyword targeting and content optimization. Neural networks can also analyze vast amounts of data to identify patterns and trends in search engine algorithms, enabling website owners to adapt their optimization strategies accordingly. By leveraging deep learning techniques, webmasters can enhance their SEO efforts, increase organic traffic, and improve their website’s visibility in search engine results.
As digitalization is gradually transforming reality into Big Data, Web search engines and recommender systems are fundamental user experience interfaces to make the generated Big Data within the Web as visible or invisible information to Web users. In addition to the challenge of crawling and indexing information within the enormous size and scale of the Internet, e-commerce customers and general Web users should not stay confident that the products suggested or results displayed are either complete or relevant to their search aspirations due to the commercial background of the search service. The economic priority of Web-related businesses requires a higher rank on Web snippets or product suggestions in order to receive additional customers. On the other hand, web search engine and recommender system revenue is obtained from advertisements and pay-per-click. The essential user experience is the self-assurance that the results provided are relevant and exhaustive. This survey paper presents a review of neural networks in web search that covers web search engines, ranking algorithms, citation analysis. The use of artificial intelligence (AI) based on neural networks and deep learning in learning relevance and ranking is also analyzed, including its utilization in Big Data analysis and semantic applications. Finally, the random neural network is presented with its practical applications to reasoning approaches for knowledge extraction.
COVER PAGE
TITLE PAGE
APPROVAL PAGE
DEDICATION
ACKNOWELDGEMENT
ABSTRACT
CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND OF THE PROJECT
1.2 PROBLEM STATEMENT
1.3 AIM/OBJECTIVE OF THE PROJECT
1.4 SCOPE OF THE PROJECT
CHAPTER TWO
LITERATURE REVIEW
2.1 RELATED WORK
2.2 OVERVIEW OF DEEP LEARNING
2.3 HISTORCAL BACKGROUND OF DEEP LEARNING
2.4 OVERVIEW OF NEURAL NETWORKS
CHAPTER THREE
METHODOLOGY
3.1 INTRODUCTION
3.2 WEB SEARCH ENGINES
3.3 SPATIAL SEARCH VARIATION
3.4 TIME SEARCH VARIATION
3.5 NEURAL NETWORKS
3.6 NEURAL NETWORKS IN WEB SEARCH
3.7 NEURAL NETWORKS IN LEARNING TO RANK ALGORITHMS
CHAPTER FOUR
RESULT ANALYSIS
4.1 RESULTS
4.2 RANKING
4.3 RANKING ALGORITHMS
CHAPTER FIVE
5.1 CONCLUSION
5.2 REFERENCES
1.0 INTRODUCTION
1.1 BACKGROUND OF THE PROJECT
The Internet has enabled the direct connection between users and information. This has fundamentally changed how businesses operate, including the travel industry and e-commerce. The Internet provides real-time information and the direct purchase of services and products. Web users can directly buy flight tickets, hotel rooms, and holiday packs. Travel industry supply charges have been eliminated or decreased because the Internet has provided a shorter value chain; however, services or products not displayed within the higher order of web search engines or recommender systems lose tentative customers. A similar scenario also applies in academic and publication searches where the Internet has permitted the open publication and accessibility of academic research. Authors are able to avoid the conventional method of human evaluation of the journal and upload their work onto their personal websites. With the intention to expand the research contribution to a wider number of readers and be cited more, authors have the personal interest to show publications in high academic search rank orders.
Web search engines and recommender systems were developed as interfaces between users and the Internet to address the need for searching precise data and items. Although they provide a straight link between web users with the pursued products or wanted information, any web search result list or suggestion will be biased due profitable economic or personal interests along with by the users’ own inaccuracy when typing their queries or requests. Sponsored search enables the economic revenue that is needed by web search engines; it is also vital for the survival of numerous web businesses and the main source of income for free to use online services. Multiple payment options adapt to different advertiser targets while allowing a balanced risk share among the advertiser and the web search engine for which the pay-per-click method is the widest used model.
Ranking algorithms are critical in the presented examples as they decide on the result relevance and order, therefore, marking data as transparent or nontransparent to e-commerce customers and general web users. Considering the web search commercial model, businesses or authors are interested in distorting ranking algorithms by falsely enhancing the appearance of their publications or items, whereas web search engines or recommender systems are biased to pretend relevance exists with respect to the rank in which they order results from explicit businesses or web sites in exchange for a commission or payment. The main consequence for a web user is that relevant products or results may be “hidden” or displayed at the very low order of the search list and unrelated products or results on a higher order.
Searching for information or meaning needs three elements: a universe formed of entities or ideas to be searched, a high-level query that specifies the properties or concepts requested by a user, and a method that searches and selects entities from the universe according to an algorithm or rule. The examples used by animals when they search in a large unknown space were investigated and the role of teamwork was discussed; these ideas were then applied to the search for information when N concurrent “search agents” with similar characteristics are being used establishing that the total average search time can be reduced by aborting and re-starting a search process after a pre-determined time-out if it has not been successful. The use of more concurrent agents can actually reduce the total energy costs, despite the increase in the number of agents, as well as the search times. These results were confirmed in terms of large datasets distributed over large networks.
Artificial neural networks are models based on the brain within the central nervous system; they are usually presented as artificial nodes or “neurons” in different layers connected together via synapses. The learning properties of artificial neural networks have been applied to resolve extensive and diverse tasks that would have been difficult to solve by ordinary rules-based programming. Neural networks and artificial intelligence have also been applied to web searching in result ranking and relevance as a method to learn and adapt to variable user interests.
This paper presents a survey of web searches in Section, including internet assistants, web search engines, meta-search engines, web result clustering, travel services, and citation analysis. Ranking is described in Section; with ranking algorithms, relevance metrics and learning to rank. We define recommender systems in Section and neural networks in web searches, learning to rank, recommender systems, and deep learning are analyzed in Section, including the random neural network and its application to web searches and ranking algorithms. Finally, conclusions are explained on Section, followed by the survey bibliography.
1.2 PROBLEM STATEMENT
Search engine optimization (SEO) is the process of increasing the quality and quantity of website traffic by increasing the visibility of a website or a web page to users of a web search engine. Optimizing a website may involve editing its content, adding content, and modifying HTML and associated coding to both increase its relevance to specific keywords and remove barriers to the indexing activities of search engines like Google ,Yahoo etc. Promoting a site to increase the number of backlinks, or inbound links, is another SEO tactic. Website becomes invisible when there no enough content which leads to high ranking and when the search engine is not working or powerful. And this problem can be overcome by using a deep learning techniques to increase the optimization.
1.3 OBJECTIVE OF THE STUDY
In this we will walk through the analysis of Google Search Console data combined with a machine learning clustering technique to provide an indication on what pages can be optimized to improve the organic traffic of a company website. The student involved shall also learn machine learning for an SEO task.
1.4 SCOPE OF THE PROJECT
An innovative and emerging technology in the Artificial Intelligence (AI) field is changing the way we look at machine learning. The concept of “deep learning” has been around for decades, but recent advancements in this particular field of AI is starting to cause a stir in tech companies around the world. Google, Microsoft, Baidu, and other search engine companies all have their eyes on this groundbreaking advancement and its enormous potential. However, this new approach to machine learning is also raising some important questions. With the ability to more accurately emulate the human brain, many are wondering how it will affect our approach to SEO. While we are only just starting to dip into this revolutionary concept, we can provide some insight into how deep learning may affect the digital landscape in the in web search optimization which is the main focus of this work.
Optimizing web search using deep learning is a fascinating topic! Deep learning techniques can enhance various aspects of search engines, from query understanding to ranking results. Here are a few ways in which deep learning can be applied to web search optimization:
- Query Understanding:
- Intent Recognition: Deep learning models can be trained to understand user intent behind search queries. This helps in providing more relevant results.
- Document Representation:
- Embeddings: Deep learning can be used to create meaningful embeddings for documents and queries. This helps in capturing semantic relationships and improving the accuracy of search results.
- Ranking Algorithms:
- Neural Ranking Models: Deep learning models, such as neural networks, can be employed to learn complex patterns in user behavior and content relevance, leading to better ranking algorithms.
- Personalization:
- User Profiling: Deep learning can analyze user behavior and preferences, allowing for personalized search results tailored to individual users.
- Image and Multimedia Search:
- Convolutional Neural Networks (CNNs): For searches involving images or multimedia content, CNNs can be utilized to understand and rank visual content.
- Natural Language Processing (NLP):
- BERT and Transformers: These models excel in understanding context and nuances in language, improving the comprehension of search queries and document content.
- Adapting to User Feedback:
- Reinforcement Learning: Search engines can use reinforcement learning to adapt and improve based on user feedback, continuously refining the ranking and relevance of search results.
- Semantic Search:
- Graph Neural Networks (GNNs): GNNs can be applied to model relationships between entities on the web, enhancing semantic search capabilities.
- Anomaly Detection:
- Detecting Malicious Activity: Deep learning models can help identify and filter out malicious content or spam, improving the overall quality of search results.
Remember, the effectiveness of these techniques often depends on the quality and quantity of data available for training. As technology continues to advance, we can expect more innovative applications of deep learning in the realm of web search optimization