An Efficient Recommendation Filter Model on Smart Home Big Data Analytics for Enhanced Living Environments

Abstract

With the rapid growth of wireless sensor applications, the user interfaces and configurations of smart homes have become so complicated and inflexible that users usually have to spend a great amount of time studying them and adapting to their expected operation. In order to improve user experience, a weighted hybrid recommender system based on a Kalman Filter model is proposed to predict what users might want to do next, especially when users are located in a smart home with an enhanced living environment. Specifically, a weight hybridization method was introduced, which combines contextual collaborative filter and the contextual content-based recommendations. This method inherits the advantages of the optimum regression and the stability features of the proposed adaptive Kalman Filter model, and it can predict and revise the weight of each system component dynamically. Experimental results show that the hybrid recommender system can optimize the distribution of weights of each component, and achieve more reasonable recall and precision rates.

Recommender systems for smart cities

“…In the literature, several authors have addressed the use of recommender systems for energy efficiency purposes. In the context of smart homes and with the aim to save resources (mainly energy) and reduce consumption, we refer the reader to (Shah et al, 2010;González Alonso et al, 2011;Bhattacharjee et al, 2014;Zehnder et al, 2015;Streltov and Bogdan, 2015;Palaiokrassas et al, 2017;Ayres et al, 2018;Schweizer et al, 2015;Chen et al, 2016;García et al, 2017;Teoca and Ciuciu, 2017;Nakamura et al, 2016;Matsui, 2018;Li et al, 2013). Similarly, but in the context of smart buildings, we found (Fotopoulou et al, 2017;Pinto et al, 2019) as relevant papers.…”

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

“…Different sorts of recommender systems are proposed in the literature using the input data either to simply select actions of possible interests to the target consumer or to predict the consumer interest level for specific actions and then produce appropriate recommendations. Consequently, several recommendation engines are proposed such as collaborative filtering [103,6], context-aware recommendations [104], content-based recommendations [105,106] and multi-agent recommendations [107,108].…”

A Proposed Framework for Reducing Electricity Consumption in Smart Homes using Big Data Analytics

“…• Constructed from simple expected using a Kalman Filter, that can components. estimate the system state with the measurements and this is the key of weighted hybridization (Chen et al, 2016). In order to address the issue of electricity consumption due to high usage and consumption of the appliances mentioned in the research problem; we used clustering algorithm to analyze the data to find out their consumption pattern and then used association rule to help in recommending actions based on inhabitant's interest as shown in Fig.…”

“…The view of a smart home system(Chen et al, 2016) Fig. 2: Overview of big data analytics methods(Marjani et al, 2017) Relationship between Internet of Things and Big Data Analytics…”