A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

Pawlicka, Aleksandra; Pawlicki, Marek; Kozik, Rafał; Choraś, Ryszard S.

doi:10.3390/s21155248

Cited by 32 publications

(25 citation statements)

References 75 publications

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“…It cannot generate better results using trust relations for predictions 14 [23] Content-based, collaborative filtering It includes a collaborative filtering approach and uses the information provided by users It provides suggestions to the users using the two renowned algorithms 15 [24] Matrix factorization Includes dual role preferences (trustee/trustee specific preferences), and trust-aware recommendations are achieved by modeling explicit interactions…”

Section: Methodsmentioning

confidence: 99%

A Deterministic Model for Determining Degree of Friendship Based on Mutual Likings and Recommendations on OTT Platforms

Khalique

Rahmani

Saquib

et al. 2022

Computational Intelligence and Neuroscience

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In recent years, the application of various recommendation algorithms on over-the-top (OTT) platforms such as Amazon Prime and Netflix has been explored, but the existing recommendation systems are less effective because either they fail to take an advantage of exploiting the inherent user relationship or they are not capable of precisely defining the user relationship. On such platforms, users generally express their preferences for movies and TV shows and also give ratings to them. For a recommendation system to be effective, it is important to establish an accurate and precise relationship between the users. Hence, there is a scope of research for effective recommendation systems that can define a relationship between users and then use the relationship to enhance the user experiences. In this research article, we have presented a hybrid recommendation system that determines the degree of friendship among the viewers based on mutual liking and recommendations on OTT platforms. The proposed enhanced model is an effective recommendation model for determining the degree of friendship among viewers with improved user experience.

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Section: Methodsmentioning

confidence: 99%

A Deterministic Model for Determining Degree of Friendship Based on Mutual Likings and Recommendations on OTT Platforms

Khalique

Rahmani

Saquib

et al. 2022

Computational Intelligence and Neuroscience

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“…It is observed when the RS produces recommendations with minimal novelty, i.e., all of the same kind [22]. Recently, there is also an increasing interest in privacy awareness when handling user data and explainability of recommendations [23], [24].…”

Section: Related Workmentioning

confidence: 99%

Utilizing Frequent Pattern Mining for Solving Cold-Start Problem in Recommender Systems

Kannout¹,

Grodzki²,

Grzegorowski³

2022

Annals of Computer Science and Information Systems

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Although several approaches have been proposed throughout the last decade to build recommender systems (RS), most of them suffer from the cold-start problem. This problem occurs when a new item hits the system or a new user signs up. It is generally recognized that the ability to handle cold users and items is one of the key success factors of any new recommender algorithm. This paper introduces a frequent pattern mining framework for recommender systems (FPRS) -a novel approach to address this challenging task. FPRS is a hybrid RS that incorporates collaborative and content-based recommendation algorithms and employs a frequent pattern (FP) growth algorithm. The article proposes several strategies to combine the generated frequent itemsets with content-based methods to mitigate the cold-start problem for both new users and new items. The performed empirical evaluation confirmed its usefulness. Furthermore, the developed solution can be easily combined with any other approach to build a recommender system and can be further extended to make up a complete and standalone RS.Index Terms-recommendation system, cold-start problem, frequent pattern mining, quality of recommendations.

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“…Multi-Task RSs which utilize ensemble method to combine output from several RSs typically through a joint optimization over a shared network representation. Most of the traditional RS approaches [2]- [9] are only focused on accuracy of rating prediction or item with high rating. Recently, findings of several studies [10]- [13] have shown that non-accuracy metrics included novelty and diversity in RS are significantly correlated with the satisfaction level of users.…”

Section: Introductionmentioning

confidence: 99%

“…Among classical RS, content-based filtering (CB) [5], [6], [8] and collaborative filtering (CF) [2]- [4] are commonly used. According to [9], [17], the former approach endured the limitations of handling inter-dependencies event, whereas the latter struggled in cold-start problem and data sparsity [9], [17]- [23] to generate recommendation if it lacks sufficient historical relationship information between the user and item. Another review study [24] on social media analysis by machine learning indicated that typical RS algorithms such as matrix factorization or Support Vector Machine (SVM) also suffered from cold-start, serendipity, and scalability problems.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Deep Reinforcement Learning for Recommendation Systems

et al. 2022

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Most existing recommendation systems (RSs) are primarily concerned about the accuracy of rating prediction and only recommending popular items. However, other non-accuracy metrics such as novelty and diversity should not be overlooked. Existing multi-objective (MO) RSs employed collaborative filtering and combined with evolutionary algorithms to handle bi-objective optimization. Besides cold-start problem from collaborative filtering, it also vulnerable to highly sparse environment, while the evolutionary algorithm suffers from premature convergence and curse of dimensionality. These limitations have prompted this work to propose deep reinforcement learning (DRL) approaches for MO optimization in RSs. Several works in DRL are available but none has addressed MO RS problems. In this study, the performances of proposed DRL approaches that based on Deep Q-Network in MO recommendation problem were investigated. The approaches were evaluated with movie recommendation dataset by using three conflicting metrics, namely precision, novelty, and diversity. The results demonstrated that deep reinforcement learning approaches has superiority performance in MO optimization, and its capability of recommending precise item along with achieving high novelty and diversity against the benchmark that using probabilistic based multiobjective approach based on evolutionary algorithm (PMOEA). Although PMOEA algorithm secured higher average value in precision, it has lower values of novelty and diversity than the proposed DRL approaches. The DRL approaches surpassed the benchmark results in average of maximum novelty and the average of mean diversity metrics, the optimization between accuracy and non-accuracy metrics is inevitable. In addition, the experiments revealed that incorporation of user latent features enhanced the recommendation quality.

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A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

Cited by 32 publications

References 75 publications

A Deterministic Model for Determining Degree of Friendship Based on Mutual Likings and Recommendations on OTT Platforms

A Deterministic Model for Determining Degree of Friendship Based on Mutual Likings and Recommendations on OTT Platforms

Utilizing Frequent Pattern Mining for Solving Cold-Start Problem in Recommender Systems

Multiobjective Deep Reinforcement Learning for Recommendation Systems

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