The internet is abundant with information. But useful content, which caters to our particular requirements, is elusive. A recommender system (RS) is a sophisticated information filtering system that sorts through voluminous data and delivers results tailored to user preferences. The two standard RS techniques are collaborative filtering and content-based filtering. The proposed method, a hybrid model, utilized a real-time dataset from the Bhilai Institute of Technology, Durg, accounting for the number of times a book was issued plus the issue and return dates. A timestamp was used to assign weights to each book. Data sparsity was addressed using pre-processing techniques. Competitive prediction accuracy was obtained through user clustering and direct predictions. The overall accuracy of faculty transaction datasets was 98%.