Time-Aware IoE Service Recommendation on Sparse Data

Abstract EN

With the advent of “Internet of Everything” (IoE) age, an excessive number of IoE services are emerging on the web, which places a heavy burden on the service selection decision of target users.

In this situation, various recommendation techniques are introduced to alleviate the burden, for example, Collaborative Filtering- (CF-) based recommendation.

Generally, CF-based recommendation approaches utilize similar friends or similar services to achieve the recommendation goal.

However, due to the sparsity of user feedback, it is possible that a target user has no similar friends and similar services; in this situation, traditional CF-based approaches fail to produce a satisfying recommendation result.

Besides, recommendation accuracy would be decreased if time factor is overlooked, as IoE service quality often varies with time.

In view of these challenges, a time-aware service recommendation approach named Ser_Rectime is proposed in this paper.

Concretely, we first calculate the time-aware user similarity; afterwards, indirect friends of the target user are inferred by Social Balance Theory (e.g., “enemy’s enemy is a friend” rule); finally, the services preferred by indirect friends of the target user are recommended to the target user.

At last, through a set of experiments deployed on dataset WS-DREAM, we validate the feasibility of our proposal.