Towards Recommendation in Internet of Things: An Uncertainty Perspective

Abstract

As a bridge between the physical and cyber world, the Internet of Things (IoT) senses and collects a large amount of user data through different types of devices connected to it. As a general information filtering technology, the recommender systems can help to associate information with each other in the IoT and to recommend personalized services for users. However, in practical applications, the collected data is uncertain due to noise, sensor errors, transmission errors, etc., which in turn affects system performance. In order to solve the data uncertainty problem in the IoT-based recommender systems, we propose a new recommender framework with item dithering. In this framework, the list of recommendations generated by the recommender algorithm is stored in a newly opened storage space for the entire session of the interaction between the user and the system. When the user interacts with the system, the list is pushed to the user after being shaken. Based on the proposed framework, we designed IDither, an item-based dithering and recommendation algorithm to shake out irrelevant items through predetermined indicators, thereby retaining the items required by the user and recommending them to the user. Experiment evaluations on real datasets show that IDither is an effective solution for handling uncertainty in the IoT-based recommender systems. We also found that IDither can be viewed as a list updating tool to increase diversity and novelty.