An information retrieval system for e-learning

The amount of information available on the Internet is growing. Finding information has become extremely challenging in an ever-growing volume of data. This is caused by a number of factors, chief among them being indexing issues and the way the search engine behaves when looking for pertinent content. In this research, we present an information retrieval system (IRS) for e-learning, and we have implemented several strategies to improve the system's accuracy and relevance while also decreasing its response time. In essence, our method is predicated on a fragmentation of the global data index. In fact, this index is broken up into multiple pieces, the number of which corresponds to the number of basic learning entities. To do this, every search query is run concurrently across all of the previously specified index pieces. Additionally, micro-services are distributed via network ports via an APIs gateway to ensure flawless synchronization between the database and the search engine. Moreover, in order to improve the score calculation and the field weight adjustment to produce more reasonable results, a weighting policy for the terms was implemented.Positive outcomes were achieved.