Tech-Query Refinements in the Recommender System

The exponential growth of digital information and internet users has led to information overload, making it challenging for users to access relevant content promptly. Existing information retrieval systems have partially addressed this issue, but they lack personalized and prioritized content based on individual user interests. As a solution, recommender systems have gained popularity. Recommender systems filter and extract crucial information from vast amounts of data, predicting user preferences based on their profiles. These systems benefit both service providers and users, reducing transaction costs in online shopping and enhancing decision-making processes. This research paper focuses on the application of recommender systems in e-commerce, where they suggest content to match buyers' preferences. With the popularity of platforms recommendation systems have become integral to our lives. The primary objective of this study is to develop a technical query recommendation system. The proposed system utilizes machine learning algorithms to recommend similar queries based not only on historical data but also on users' present interests. The research includes data extraction from web resources, insights gained from the dataset, and the creation of an efficient machine learning model. The paper also discusses challenges faced by recommender systems, such as scalability, over-specialization, and cold start issues, and proposes solutions to address these challenges. The research emphasizes the significance of recommendation systems in various industries and their impact on decision-making and user experience.