Abstract:

The proliferation of information available on the internet has led to an exponential increase in the number of web pages, making it difficult for users to find relevant information efficiently. Search engines have become the primary means of accessing information on the internet. However, current search engines are not personalized, and search results are often influenced by a user's search history, location, and other factors. This project aims to develop a personalized search engine using machine learning techniques. The search engine will use data from a user's search history, browsing history, and social media activity to build a personalized profile for the user. The system will then use this profile to provide search results that are tailored to the user's interests and preferences. The search engine will be built using Python and will leverage machine learning libraries such as TensorFlow, Keras, and Scikit-Learn. The system will be trained using a dataset of web pages and user activity data. The system will be evaluated using metrics such as precision, recall, and F1 score.

Description:

Query parsing, Search Engine, Crawler, Indexing, and Machine Learning