Survey on recommendation system methods

Nagarnaik, Paritosh; Thomas, Achamma

doi:10.1109/ecs.2015.7124835

Cited by 23 publications

(21 citation statements)

References 14 publications

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“…Altogether, as a result of the aforementioned bibliographic methods, 393 research items were marked as potentially relevant and further analyzed. Out of them, 86 works were selected for the survey due to their quality and appropriateness to this survey topics, among them 12 other surveys [ 3 , 15 , 16 , 17 , 18 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. The papers most relevant to the application of recommender systems for attack mitigation are discussed in the Section 4 .…”

Section: The Conduct Of the Studymentioning

confidence: 99%

A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

Pawlicka

Pawlicki

Kozik

et al. 2021

Sensors

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This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of the art concerning the use of recommender systems in cybersecurity; both the existing solutions and future ideas are presented. The contribution of this paper is two-fold: to date, to the best of our knowledge, there has been no work collecting the applications of recommenders for cybersecurity. Moreover, this paper attempts to complete a comprehensive survey of recommender types, after noticing that other works usually mention two–three types at once and neglect the others.

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Section: The Conduct Of the Studymentioning

confidence: 99%

A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

Pawlicka

Pawlicki

Kozik

et al. 2021

Sensors

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“…To make recommendations using the Collaborative filtering approach, the recommendations are made based on a few customers who are most similar to the active users [4]. It measures the resemblance of two users in various ways; one usual technique is to calculate the cosine of the space(angle) between the two vectors.…”

Section: Collaborative Filteringmentioning

confidence: 99%

“…In the perspective of Content-based recommender systems, the items are represented by its related information or features [4]. The system also learns to know what the user has liked, or rated or ranked and uses that to represent the interest of the user.…”

Section: Content-basedmentioning

confidence: 99%

Complex Network based Recommender System

Kingsley¹,

Francis²,

Appiah³

et al. 2019

IJCA

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The structural abstraction of Recommender Systems has a high resemblance as that of a Complex Network(CN). The underlining principles of the different types of Recommender Systems, such as Collaborative Filtering, Content-Based, Knowledge-Based, Utility-Based, and Hybrid are similar to the attributes and theories of Complex Networks. Considering the enormity of computation in Recommender Systems logic formulation and it's a structural similarity with Complex Networks, the research work seeks to highlight attributes of Complex Networks and Recommender Systems to suggest the applicability of theories, and principles in CN's that can be considered in the construction of Recommender Systems. The research further elaborates on matrices for checking the correctness of the use of these theories.

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“…The users' past navigational patterns are analyzed and users with similar interests are put together. But it has a limitation of Sparsity and scalability [14]. The computation time of similarity increases with increase in number of users.…”

Section: User Interestsmentioning

confidence: 99%

Importance of Domain Knowledge in Web Recommender Systems

Aggarwal¹,

Mangat²

2015

IJCA

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Web usage data is extensively used in every domain to analyze the browsing behavior of the users who visited the website or search engines. Web usage data is contained in server logs called as Web Logs. This data enables the website owners to infer the needs and interests of the users for using this information to increase the revenue from their web business. The website owners employ recommender systems for this purpose. The recommender systems exploit web usage data to predict what web pages the user will visit next and therefore offer the recommendations for those very pages to the user and offers them support while browsing. This in turn helps users to have a better browsing experience, personalized support and hence, probability of user buying out the products from that website increases. Web usage mining alone is used in traditional recommender systems. Modern recommender systems employ semantic knowledge base i.e. domain knowledge in addition to web usage mining for efficient prediction of pages as this helps in avoiding the new page problem. This paper presents a comparative and comprehensive study of modern and traditional recommender systems.

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Survey on recommendation system methods

Cited by 23 publications

References 14 publications

A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

Complex Network based Recommender System

Importance of Domain Knowledge in Web Recommender Systems

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