A location-based orientation-aware recommender system using IoT smart devices and Social Networks

Ojagh, Soroush; Malek, M. R.; Saeedi, Sara; Liang, Steve H. L.

doi:10.1016/j.future.2020.02.041

Cited by 34 publications

(22 citation statements)

References 28 publications

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“…Author [13] proposed framework accepts the user query in the form of natural language, then convert the query into a binary encoded format understood by the sensor. Grouping of the sensors is performed based on energy level and distance.…”

Section: Related Workmentioning

confidence: 99%

MULTI CRITERIA DECISION BASED RANKED DISCOVERY FRAMEWORK FOR IoT RESOURCESN

Chikkamannur¹

2021

INDJCSE

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The growth rate of Internet of Things (IoT) ecosystem has led to an enormous pool of heterogeneous IoT resources with similar capabilities. Providing the right IoT resource at the right time is an indispensible requirement for user application performance. In this paper, we propose a framework for returning the most pertinent IoT resource based on ranking, as the user query response. Proposed Multi Criteria Decision making framework computes the weights of the various criteria based on fuzzy logic user preference and entropy based objective weight. This computed weights for the criteria are applied on approach Technique for Order Preference by Similarity to Ideal solution (TOPSIS). Effectiveness of the proposed approach is compared with current state of work. Also real time testbed experiment based study is carried out to demonstrate the effectiveness and robustness of proposed approach.

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Section: Related Workmentioning

confidence: 99%

MULTI CRITERIA DECISION BASED RANKED DISCOVERY FRAMEWORK FOR IoT RESOURCESN

Chikkamannur¹

2021

INDJCSE

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“…A variety of contexts can be extracted from smartphones sensors as well as their installed apps such as social media, phone, calendar or emails. Check-ins information, social graphs of connected users, physical environments, daily schedules, favorite applications, and favorite internet search topics are among the possible context information that can be extracted from smartphones [18]. A mobile application has been developed in this research to extract and access desired contexts from smartphones and tablets.…”

Section: Algorithm Extract-movie Namesmentioning

confidence: 99%

“…J. Geo-Inf. 2020, 9, 519 2 of 28 the CSP [12,18]. For conventional RSs, the similarities between users were identified by either their user-created comments, or by ratings of different items.…”

Section: Introductionmentioning

confidence: 99%

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

Ojagh

Malek

Saeedi

2020

IJGI

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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.

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“…Similarity measurement methods, which attempt to compute the similarity of trajectories, have received considerable attention in recent decades (Furtado, Kopanaki, Alvares, & Bogorny, 2016; Lehmann et al, 2019; Petry et al, 2019; Wan et al, 2017). These methods have been used in many applications, including group identification and predicting group trends, recommendation systems, clustering, information retrieval, and outlier detection (Cao, Si, et al, 2018; Lehmann et al, 2019; Ojagh, Malek, Saeedi, & Liang, 2020; Petry et al, 2019). An outlier detection algorithm, for example, uses a similarity measure to identify trajectories with typical behavior and considers trajectories that are dissimilar to them as outliers.…”

Section: Introductionmentioning

confidence: 99%

Trajectory similarity measurement: An enhanced maximal travel match method

Karami

Malek

2021

Transactions in GIS

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Trajectory similarity measurement is a vital and widely used step in many applications, including recommendation systems. In the discipline of similarity measurement, research has mostly been focused on raw trajectories, consisting of location and time‐stamp information. Due to the explosion in the use of the internet and location‐based social networks, raw trajectories can be easily enriched with semantic information. Nevertheless, few attempts have been made to apply semantic and location information during similarity measurement. In light of this, we present a new similarity measure called the enhanced maximal travel match (EMTM) in this article. The proposed EMTM improves on conventional maximal travel match (MTM) methods by simultaneously considering the location and place category as the most basic semantic information. EMTM first generates a new place category‐location hierarchical framework (CLHF) for each trajectory. Subsequently, it identifies MTMs at each layer of hierarchy to explore the similarity between each pair of CLHFs. Finally, the proposed method calculates similarity scores based on the identified MTMs. In experiments on a Foursquare data set, EMTM outperformed the conventional MTM methods by more than 50% in terms of mean average precision. Additionally, EMTM was the most accurate technique among four state‐of‐the‐art methods.

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A location-based orientation-aware recommender system using IoT smart devices and Social Networks

Cited by 34 publications

References 28 publications

MULTI CRITERIA DECISION BASED RANKED DISCOVERY FRAMEWORK FOR IoT RESOURCESN

MULTI CRITERIA DECISION BASED RANKED DISCOVERY FRAMEWORK FOR IoT RESOURCESN

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

Trajectory similarity measurement: An enhanced maximal travel match method

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