A Fast and Better Hybrid Recommender System Based on Spark

Abstract

With the rapid development of information technology, recommender systems have become critical components to solve information overload. As an important branch, weighted hybrid recommender systems are widely used in electronic commerce sites, social networks and video websites such as Amazon, Facebook and Netflix. In practice, developers typically set a weight for each recommendation algorithm by repeating experiments until obtaining better accuracy. Despite the method could improve accuracy, it overly depends on experience of developers and the improvements are poor. What worse, workload will be heavy if the number of algorithms rises. To further improve performance of recommender systems, we design an optimal hybrid recommender system on Spark. Experimental results show that the system can improve accuracy, reduce execution time and handle large-scale datasets. Accordingly, the hybrid recommender system balances accuracy and execution time.

Integration and Recommendation System of Profiles based on Professional Social Networks

“…The search proposal for the number of recommendations is displayed between two updates by varying K between 5 and 10 (interval taken in the literature) for all the proposals which respectively included ( [1,5], [1,6], [1,7], [1,8], [1,9] and [1,10]), then by recovering each time the return values of the various metrics.…”

“…Another possibility is to combine them by their different positive aspects to increase the efficiency or the satisfaction of the user and at the same time, to decrease or fill their negative aspects. An example of a well-generalized operation is [6]:…”