Abstract:
With the exponential growth of digital information, efficient and accurate retrieval of relevant data has become a critical challenge in information retrieval systems. Traditional systems rely on predefined queries, which require users to formulate explicit search terms that accurately represent their information needs. However, this approach often fails to capture the nuanced and evolving nature of user queries, leading to suboptimal search results.
To address this limitation, this research proposes a novel model and development approach for non-predefined queries in information retrieval systems. The objective is to enhance the system’s ability to understand and interpret user intent, even when the query is not explicitly provided.
The proposed model leverages advanced natural language processing (NLP) techniques, such as deep learning and semantic analysis, to capture the semantic meaning and context of user queries. By analyzing the available textual data, including query logs, user profiles, and contextual information, the model learns to infer the underlying intent behind users’ queries.
The development process involves training the model on large-scale datasets with diverse query patterns and user behaviors. The training data includes both explicit queries and implicit user interactions, enabling the model to generalize and adapt to a wide range of query types and user preferences.
To evaluate the effectiveness of the proposed model, extensive experiments are conducted on real-world datasets. The evaluation metrics include relevance ranking, precision, recall, and user satisfaction. The results demonstrate that the non-predefined query model significantly improves the retrieval accuracy and user experience compared to traditional systems.
The developed non-predefined query model has numerous potential applications, including web search engines, recommendation systems, and intelligent personal assistants. By enabling users to express their information needs more naturally and effortlessly, these systems can better serve their users, leading to increased productivity and satisfaction.
In conclusion, this research presents a model and development approach for non-predefined queries in information retrieval systems. The proposed model leverages advanced NLP techniques to infer user intent from implicit and explicit cues, enhancing the system’s ability to retrieve relevant information. The experimental results validate the effectiveness of the model and highlight its potential for various real-world applications.
MODEL AND DEVELOPMENT OF NON‐PREDEFINED QUERY IN INFORMATION RETRIEVAL SYSTEM. GET MORE COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS
