QoS-Based Concurrent User-Service Grouping for Web Service Recommendation
Recently, tremendous growth of web services to share the program, data and resources requires the optimal recommendation strategy. The major issues observed in existing recommendation strategies are scalability, sparsity and the cold start. The employment of matrix factorization (MF) models addressed all the issues effectively. But, they increase the scalability of the system. This paper proposes the new framework that contains web service grouping, distance estimation, service utilization level estimation and the item-to-item comparison (Pearson Correlation Coefficient (PCC)) to improve the recommendation performance. The grouping of users according to the Haversine distance formulation to reduce the complexity in the relevant web service recommendation against the complex queries. The locations and the fields in the services utilization in proposed work provide the effective recommendation performance. The comparative analysis between the proposed novel recommendation framework with the existing techniques assures the effectiveness of proposed approach in web service recommendation.
Keywordsdistance-based learning Pearson correlation coefficient QoS-based Web service recommendation response time similarity estimation throughput
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