ICRS: an intelligent collaborative recommender system for electronic purchasing

Finding the right product that satisfies the user's needs and wants in e-purchasing is a challenging problem. We design and implement an Intelligent Collaborative Recommender System (ICRS) to map users' needs to the products that can satisfy them. A methodology is used to dynamically update the accuracy factor based on user intelligence. The different approaches for recommendation are categorised as memory-based and model-based approaches. Memory-based systems suffer from data sparsity and scalability problems, whereas model-based approaches tend to limit the range of users. Hence, by integrating both these approaches, we overcome the shortfalls. In our paper, we smooth the sparse data and apply the collaborative filtering approach for recommendations. Recommendations are made more accurate by applying regression to weighted aggregated predictions. The system that is considered here is the book recommendation system. The metric that is considered for measuring the performance of our system is the Mean Absolute Error (MAE). In terms of the computation time, clustering similar users is done offline, which greatly reduces the time for computation. This approach thus alleviates scalability and sparsity problems and offers accurate recommendations. Finally, our system is developed for an online book purchasing application and tested by our college students.