"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0.111"

Abstract With the rapid development of the internet, search engines have become the gateway to information, and related technologies have emerged endlessly. Traditional search engines consist of four processes: web crawling, index building, content retrieval, and result ranking. Initially, result ranking involved calculating the relevance of web pages using manually crafted formulas. However, in the current era dominated by machine learning, the combination of machine learning and search engines has led to the emergence of Learning to Rank (LTR), which addresses the increasing complexity of factors to consider in web page ranking. This paper focuses on the design of a question and answer recommendation algorithm based on machine learning, specifically utilizing LambdaMART for ranking web pages. The algorithm incorporates text processing, keyword extraction, web crawling, and search engine indexing to effectively recommend relevant question and answer pairs. Keywords: machine learning, question answering recommendation, LambdaMART, text processing, keyword extraction, web crawling, search engine, indexing.