"基于机器学习的问答推荐算法设计-电子科技大学学士论文初稿"

The bachelor thesis titled "Design of Question-Answer Recommendation Algorithm Based on Machine Learning" explores the integration of machine learning with search engine technology to enhance the efficiency and accuracy of web page ranking and recommendation. In the rapidly evolving era of the internet, search engines serve as the gateway to vast amounts of information. Traditional search engines involve processes such as web crawling, index building, content retrieval, and result ranking, with the initial relevance calculation being based on manually fitted formulas. However, with the emergence of machine learning in the field of search engine optimization, a new product known as Learning to Rank (LTR) has been developed to address the increasing complexities involved in web page ranking. This approach eliminates the limitations of manual relevance calculation and considers a wide range of factors to improve the quality of search results. The thesis discusses key concepts such as machine learning, question-answer recommendation, LambdaMART algorithm, text processing, keyword extraction, web crawling, search engine optimization, and indexing. The research aims to optimize the search experience for users by leveraging the power of machine learning to provide accurate and relevant question-answer recommendations.