A Service Recommendation Algorithm Based on Modeling of Implicit Demands
Yanmei Zhang, Tingpei Lei, Yan Wang · 2016
The results from using the current service recommendation algorithms are still unable to meet the dynamic and diverse demands of users. Therefore, a recommendation algorithm is proposed to take into account the dynamic and diverse demands of users. This algorithm extracts the user-implicit-demand-factors from the Latent Dirichlet Allocation model in the field of machine learning, and uses both explicit and implicit demand as the intermediary variable to generate a service recommendation list for the user. Experimental results on a real-world data set regarding service composition show that the proposed algorithm can represent a variety of user demands, and the performance of the proposed algorithm is better than the existing algorithms in terms of accuracy, novelty and timeliness.