Collaborative Filtering in Real-Time Library Catalog Recommendations

Abstract

Collaborative filtering is becoming an essential technique for recommendation systems that is based on the activities and preferences of users. The paper transforms collaborative filtering algorithms into real-time library catalog systems in order to enhance the user experience and retrieval of information. Traditional library search systems are based on keywords and subject categories, which can be ineffective in capturing the interests of the user, and provide an opportunity that allows chance-finding in a limited way. Under collaborative filtering applications, libraries are in a position to offer custom suggestions that rely on dynamic proposals that consider borrowing habits, search records, and ratings left behind by users. This paper talks about an item-based collaborative filtering strategy of real-time recommendation, with a backend that scales with real-time user data indexing. The implementation of this system in a university library showed significant improvements in recommendation accuracy and overall user engagement. Results showed that users were more likely to engage with and check out recommended material when the suggestions were aligned with popular trends among academics and what their peers were currently studying. The model proposes solutions to cold start and data sparsity through real-time feedback loops, along with hybrid approaches to these problems. This paper demonstrates further advancements in the development of intelligent library systems that provide users with seamless, collaborative access to academic resources crafted through sophisticated, responsive technologies.