Adaptive Omnisearch: Fusing Semantic, Contex...

Adaptive Omnisearch: Fusing Semantic, Contextual, and Keyword Strategies for Optimal Results

Author Name : Yogananda Domlur Seetharama

ABSTRACT The advancement of search technology has revolutionized how users gain access to information in different domains, focusing on the retail and e-commerce industries. Although the effectiveness of performing searches and optimizing the results based on keywords is quite high, many things could be improved related to the definition of the subject and the analysis of the context of the queries. This paper proposes a new framework named ‘Adaptive Omnisearch’ to overcome these limitations and make the search results more effective by applying semantic-based search with contextual mechanism and keyword search. The framework comprises four main components: the Semantic Understanding Module, the Contextual Awareness Engine, the Keyword Analysis and Matching System, and the Fusion and Ranking Algorithm. This adaptable way changes the weight from one component to another according to the special characteristics of the query or user context. This increases the precision and utility of the search outcomes. Challenges of implementing Adaptive Omnisearch include real-time processing and equal distribution of component impact, which can be solved with the help of machine learning and distributed computing. Precision rate, recall rate, F1 rate, Mean Reciprocal Rank (MRR) rate, and Normalized Discounted Cumulative Gain (NDCG) rate are much better than traditional search engines. This is evident in the case of e-commerce, healthcare, research, and even customer service, as depicted below. There are also ethical concerns, such as user data privacy and fairness of algorithms, which are also highlighted here in the necessity for responsible data processing.