Context‐Based Social Media Recommendation System

Kanmani, R. Sujithra; Surendiran, B.

doi:10.1002/9781119711582.ch12

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…The system applied user context, such as speed and trajectory, to create recommendations along the user's path based on the current position and driving speed of the user. Kanmani et al (2020) proposed a context-based social media recommendation system for travel. This system provided recommendations based on geotagged data to determine the similarity of users and POIs along travel routes [28].…”

Section: Ubiquitous Systemsmentioning

confidence: 99%

“…Kanmani et al (2020) proposed a context-based social media recommendation system for travel. This system provided recommendations based on geotagged data to determine the similarity of users and POIs along travel routes [28]. This was achieved using collaborative filtering techniques and similarity computing, and by selecting neighborhoods via the k-nearest neighbors algorithm.…”

Section: Ubiquitous Systemsmentioning

confidence: 99%

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Ubiquitous Tourist System Based on Multicriteria Decision Making and Augmented Reality

et al. 2022

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Increasing global demand for travel has drawn public attention to the tourism industry. This industry needs the design of intelligent systems based on new concepts to facilitate better service delivery. To this end, this study proposes a ubiquitous tourist system based on context-awareness, multicriteria decision making (MCDM), and augmented reality (AR) using a geospatial information system (GIS). This system provides two services to the user. First, it recommends a hotel in the vicinity of the user based on their preferences. Subsequently, it modifies the information property to augment the information concerning the visited object using AR technology. This system offers the advantage of adapting its models based on the user and their environment using context-awareness, thereby facilitating increased system automation during service delivery. Furthermore, this system enables personalization based on user needs. Our system was evaluated via a usability test using a Likert scale based on two system aspects, namely, system design, and user acceptance of the result. The output of this test yielded an average score of 4.112. The proximity of this score to the highest level of the Likert scale indicates the acceptance of the system by users.

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Section: Ubiquitous Systemsmentioning

confidence: 99%

Section: Ubiquitous Systemsmentioning

confidence: 99%

Ubiquitous Tourist System Based on Multicriteria Decision Making and Augmented Reality

et al. 2022

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“…The recommendation system provides recommendations by keeping user interest and using contextual information into account. The abundance of available information indicates the extreme need to overcome irrelevant information [1]. The recommender system is the building block of data filtering [2].…”

Section: Introductionmentioning

confidence: 99%

A Review of Content-Based and Context-Based Recommendation Systems

Javed¹,

Shaukat

Hameed

et al. 2021

Int. J. Emerg. Technol. Learn.

192

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In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests.

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Context-Adaptive Content-Based Filtering Recommender System Based on Weighted Implicit Rating Approach

Navin

Murugesan

2022

Computational Intelligence in Data Mining

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Context‐Based Social Media Recommendation System

Cited by 5 publications

References 11 publications

Ubiquitous Tourist System Based on Multicriteria Decision Making and Augmented Reality

Ubiquitous Tourist System Based on Multicriteria Decision Making and Augmented Reality

A Review of Content-Based and Context-Based Recommendation Systems

Context-Adaptive Content-Based Filtering Recommender System Based on Weighted Implicit Rating Approach

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