LAUSR

In recent years, the ever-increasing number of cloud services has led to the service targeting issue. It becomes challenging to provide users with services that fit their needs. Recommender systems address the service targeting problem by helping users to easily retrieve services matching their tastes. Collaborative filtering-based methods are spreadly used recommendation approaches since numerous methods are based on them. However, most of them assume the stationarity of user profiles over time, which is an unrealistic consideration. Indeed, in practice, users’ tastes are dynamic and therefore show a significant variability over time. Time-aware recommender systems effectively integrate the variability of users’ needs to improve the recommendation quality. However, the recommendation credibility is another crucial requirement that is not met by those time-aware recommender systems that moreover badly deal with the data sparsity issue. To address the service targeting problem while meeting requirements on the recommendation reliability and the users’ tastes variability, we propose in this paper a realistic temporal service recommendation approach based on implicit trust relationships inference. Our method integrates the time feature into the recommendation process in order to consider the instantaneity of users’ needs. Our proposal is based on a trusted network that infers implicit trust relationships to ensure the recommendation credibility even in case of data sparsity. Experiments are conducted on real-world service invocations datasets. Our proposal is then compared to existing methods and displays valuable performances in terms of mean absolute error, root mean square error, and normalized discounted cumulative.