ThCoRe: Things of interest recommendation based on novel things correlations.

Despite that the emerging internet of things (IoT) increases the chance for users to interact with things, generating massive context data, the ground-truth user-thing interaction data is still quite sparse compared with the enormous numbers of users and things. Current existing things recommendation methods mostly adopt machine learning or deep learning (e.g., matrix factorization or neural network) as their basic models, which continuously results in that the data sparsity has a great impact on their performance. Although they integrate different contexts to alleviate the problem, the real effect is rather limited. To the end, in the paper, we study how to achieve better recommendation results in the case of the data sparsity. Along this line, the very critical challenge is how to learn user preferences without relying too much on big data. As a solution, we first build an interaction event experience graph (IEEG) from the user interaction data. Secondly, we develop a Non-Axiomatic Logic-based learning framework for novel things correlations, named NALC, running over IEEG, to mine the user-thing relationship. We measure the user-user relationship and the thing-thing relationship from real-time contexts. Furthermore, we fuse the user-user, the thing-thing and the user-thing relationships based on a product rule, to model the recommender. Finally, we evaluate the performance of our proposed model on the real-world dataset and demonstrate that the proposed model outperforms existing state-of-the-art models. [ABSTRACT FROM AUTHOR]