A Social–Aware Recommender System Based on User’s Personal Smart Devices

Abstract

Providing recommendations in cold start situations is one of the most challenging problems for collaborative filtering based recommender systems (RSs). Although user social context information has largely contributed to the cold start problem, most of the RSs still suffer from the lack of initial social links for newcomers. For this study, we are going to address this issue using a proposed user similarity detection engine (USDE). Utilizing users’ personal smart devices enables the proposed USDE to automatically extract real-world social interactions between users. Moreover, the proposed USDE uses user clustering algorithm that includes contextual information for identifying similar users based on their profiles. The dynamically updated contextual information for the user profiles helps with user similarity clustering and provides more personalized recommendations. The proposed RS is evaluated using movie recommendations as a case study. The results show that the proposed RS can improve the accuracy and personalization level of recommendations as compared to two other widely applied collaborative filtering RSs. In addition, the performance of the USDE is evaluated in different scenarios. The conducted experimental results on USDE show that the proposed USDE outperforms widely applied similarity measures in cold start and data sparsity situations.

An Interoperable Architecture for the Internet of COVID-19 Things (IoCT) Using Open Geospatial Standards—Case Study: Workplace Reopening

“…Our future work will explore the interoperability between various BLE systems and standards to achieve plug and play contact tracing apps with various contextual information [79]. Another area for future research would be applying different data analysis to the indoor trajectory data provided by the IoCT platform [80]. For that we would attempt to obtain different metrices for person-to-place scenarios using an aggregation of the camera and BLE sensors for trajectory estimation [61].…”

A Person-to-Person and Person-to-Place COVID-19 Contact Tracing System Based on OGC IndoorGML

“…Although privacy protection is outside this paper's scope, basic authentication and security authorization preserving techniques and user ID anonymization were applied to the proposed contact tracing application. Various user contexts (e.g., cleaning activities and job type) can be automatically extracted without human intervention [68,81]. Although user contexts are manually selected in this research, investigating automatic context extraction approaches can improve the scalability of the proposed systems and is on hold for future work.…”

“…The matrix representation of movement trajectories is widely applied in recommendation systems. Ojagh et al [67,68] transformed GPS trajectories of users into a matrix and then applied a collaborative filtering algorithm to provide users with personalized recommendations. Tensor representation of movement trajectories can be considered a natural extension of matrix-based transformation, with additional information as the third dimension of matrix representation [25].…”

A social hybrid recommendation system using LSTM and CNN

“…Ojagh et al proposed a social recommendation system to solve the cold start problem when the user lacks social links 33 . In this study, a user similarity detection engine (USDE) was introduced, which employs user's personal smart device to extract social interactions between users.…”

“…Ojagh et al proposed a social recommendation system to solve the cold start problem when the user lacks social links. 33 In this study, a user similarity detection engine (USDE) was introduced, which employs user's personal smart device to extract social interactions between users. Two algorithms are developed to this end, one that uses both social interactions created in real-world or virtual networks, and the other uses clustering to identify similar users based on different contexts in the users' profile.…”