Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Suhaim, Areej Bin; Berri, Jawad

doi:10.1109/access.2021.3072165

Cited by 27 publications

(23 citation statements)

References 108 publications

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“…Collaborative filtering technology gathers the opinions of large interconnected communities on the webs, and supports filtering of substantial quantities of data. The recommendation system [1] uses a lot of information such as: the items, the users and the rating values to suggest the suitable items to user. However, the unwanted information has been removed by using the computerized methods before presenting the recommendation result to the user.…”

Section: Collaborative Filteringmentioning

confidence: 99%

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Energy-based Collaborative Filtering Recommendation

Tran¹,

Phan²,

Huynh³

2022

IJACSA

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The core value of the recommendation model is the using of the measures to measure the difference between the jumps (e.g. pearson), some other studies based on the magnitude of the angle in space (e.g. cosine), or some other studies study the level of confusion (e.g. entropy) between users and users, between items and items. Recommendation model provides an important feature of suggesting the suitable items to user in common operations. However, the classical recommendation models are only concerned with linear problems, currently there is no research about nonlinear problems on the basis of potential/energy approach to apply for the recommendation model. In this work, we mainly focus on applying the energy distance measure according to the potential difference with the recommendation model to create a separate path for the recommendation problem. The theoretical properties of the energy distance and the incompatibility matrix are presented in this article. Two experiment scenarios are conducted on Jester5k, and Movielens datasets. The experiment result shows the feasibility of the energy distance measures/ the potential in the recommendation systems.

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Section: Collaborative Filteringmentioning

confidence: 99%

“…In collaborative filtering [1], the recommendation system searches for similar users to make predictions. The user's rating model is a useful feature for determining similarity.…”

Section: Collaborative Filteringmentioning

confidence: 99%

Energy-based Collaborative Filtering Recommendation

Tran¹,

Phan²,

Huynh³

2022

IJACSA

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“…RSs use different filtering algorithms, in which the predominant approaches are collaborative filtering, content-based filtering and hybrid filtering. This combines the two approaches so as to overcome any problems that may arise through individual use of the different techniques, such as the problem of data sparsity, or owing to the lack of information provided by users about the recommended items, which is known as cold start [7], and thus improve recommendation performance [8].…”

Section: Introduction and Related Studiesmentioning

confidence: 99%

Using LinkedIn Endorsements to Reinforce an Ontology and Machine Learning-Based Recommender System to Improve Professional Skills

2022

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Nowadays, social networks have become highly relevant in the professional field, in terms of the possibility of sharing profiles, skills and jobs. LinkedIn has become the social network par excellence, owing to its content in professional and training information and where there are also endorsements, which are validations of the skills of users that can be taken into account in the recruitment process, as well as in the recommender system. In order to determine how endorsements influence Lifelong Learning course recommendations for professional skills development and enhancement, a new version of our Lifelong Learning course recommendation system is proposed. The recommender system is based on ontology, which allows modelling the data of knowledge areas and job performance sectors to represent professional skills of users obtained from social networks. Machine learning techniques are applied to group entities in the ontology and make predictions of new data. The recommender system has a semantic core, content-based filtering, and heuristics to perform the formative suggestion. In order to validate the data model and test the recommender system, information was obtained from web-based lifelong learning courses and information was collected from LinkedIn professional profiles, incorporating the skills endorsements into the user profile. All possible settings of the system were tested. The best result was obtained in the setting based on the spatial clustering algorithm based on the density of noisy applications. An accuracy of 94% and 80% recall was obtained.

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“…One of the other problems is the existence of large datasets, and the scoring matrix is too sparse and the problem of data sparsity occurs. Security and reliability are other challenges [25].…”

mentioning

confidence: 99%

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

2022

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In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places to visit, have become noteworthy and useful for users, research areas, industries, and advertising companies. The POI recommendation system combines different information sources and creates numerous research challenges and questions. New research in this field utilizes deep-learning techniques as a solution to the issues because it has the ability to represent the nonlinear relationship between users and items more effectively than other methods. Despite all the obvious improvements that have been made recently, this field still does not have an updated and integrated view of the types of methods, their limitations, features, and future prospects. This paper provides a systematic review focusing on recent research on this topic. First, this approach prepares an overall view of the types of recommendation methods, their challenges, and the various influencing factors that can improve model performance in POI recommendations, then it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses. The recently proposed models are categorized according to the method used, the dataset, and the evaluation metrics. It found that these articles give priority to accuracy in comparison with other dimensions of quality. Finally, this approach introduces the research trends and future orientations, and it realizes that POI recommender systems based on deep learning are a promising future work.

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Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Cited by 27 publications

References 108 publications

Energy-based Collaborative Filtering Recommendation

Energy-based Collaborative Filtering Recommendation

Using LinkedIn Endorsements to Reinforce an Ontology and Machine Learning-Based Recommender System to Improve Professional Skills

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

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