Recommender systems: An overview of different approaches to recommendations

Shah, Kunal; Salunke, A. L.; Dongare, Saurabh; Antala, Kisandas

doi:10.1109/iciiecs.2017.8276172

Cited by 84 publications

(27 citation statements)

References 6 publications

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“…In order to evaluate the feasibility of the recommendation process, the MENTOR was assessed using four widely used similarity measures: (i) Euclidean distance, (ii) Manhattan distance, (iii) Cosine similarity, and (iv) Pearson correlation. These measures were selected because of their potential to quantify the similarity of two objects [17]. Thus, end-users demand can be compared with protections available in order to decide which fits better for each specific case.…”

Section: A Recommendation Enginementioning

confidence: 99%

“…In this scenario, it is essential to observe not only how often attacks surpass the on-site infrastructure capacity, but also which off-site services can provide the necessary protection, considering their different service flavors, such as the amount of traffic supported, the capacity to address particularities of a determined attack, and price conditions. In this sense, recommender systems [17] provide a valuable security management tool to support decision during the detection and mitigation process.…”

Section: Introductionmentioning

confidence: 99%

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MENTOR: The Design and Evaluation of a Protection Services Recommender System

Franco

Rodrigues

Stiller

2019

2019 15th International Conference on Network and Service Management (CNSM)

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Cyberattacks are the cause of several damages on governments and companies in the last years. Such damage includes not only leaks of sensitive information, but also economic loss due to downtime of services. The security market size worth billions of dollars, which represents investments to acquire protection services and training response teams to operate such services, determines a considerable part of the investment in technologies around the world. Although a vast number of protection services are available, it is neither trivial for network operators nor endusers to choose one of them in order to prevent or mitigate an imminent attack. As the next-generation cybersecurity solutions are on the horizon, systems that simplify their adoption are still required in support of security management tasks. Thus, this paper introduces MENTOR, a support tool for cybersecurity, focusing on the recommendation of protection services. MENTOR is able to (a) to deal with different demands from the user and (b) to recommend the adequate protection service in order to provide a proper level of cybersecurity in different scenarios. Four similarity measurements are implemented in order to prove the feasibility of the MENTOR's engine. An evaluation determines the performance and accuracy of each measurement used during the recommendation process.

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Section: A Recommendation Enginementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MENTOR: The Design and Evaluation of a Protection Services Recommender System

Franco

Rodrigues

Stiller

2019

2019 15th International Conference on Network and Service Management (CNSM)

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“…This not only impedes the diversity of experiences of users, but also causes filter bubbles [12,13], e.g., a reduced spectrum of user consumption and the political bias, which limit discovery or neglect the potential for promoting new items from the long tail [14,15]. Moreover, as the training of a recommender system mostly relies on explicit feedback (typically the ratings of items), it is inevitable to face the fact that only a small portion of users leave their ratings-the so-called the sparsity problem [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…Recommender systems can be largely classified into three categories: the collaborative approach, content-based approach, and hybrid approach [16,17,25]. Firstly, the collaborative approach is based on the idea that people with similar preferences are likely to agree on an evaluation of an item.…”

Section: Introductionmentioning

confidence: 99%

Re-Enrichment Learning: Metadata Saliency for the Evolutive Personalization of a Recommender System

Ban

Lee

2021

Applied Sciences

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Many studies have been conducted on recommender systems in both the academic and industrial fields, as they are currently broadly used in various digital platforms to make personalized suggestions. Despite the improvement in the accuracy of recommenders, the diversity of interest areas recommended to a user tends to be reduced, and the sparsity of explicit feedback from users has been an important issue for making progress in recommender systems. In this paper, we introduce a novel approach, namely re-enrichment learning, which effectively leverages the implicit logged feedback from users to enhance user retention in a platform by enriching their interest areas. The approach consists of (i) graph-based domain transfer and (ii) metadata saliency, which (i) find an adaptive and collaborative domain representing the relations among many users’ metadata and (ii) extract attentional features from a user’s implicit logged feedback, respectively. The experimental results show that our proposed approach has a better capacity to enrich the diversity of interests of a user by means of implicit feedback and to help recommender systems achieve more balanced personalization. Our approach, finally, helps recommenders improve user retention, i.e., encouraging users to click more items or dwell longer on the platform.

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“…Firstly, we are going to make a brief introduction about the different types of recommendations systems in order to know which technique fits better with the problem we want to resolve. According to some state-of-the-art reviews about recommendation systems, like Isinkaye et al [6] and Shah et al [7], there are three kinds. The most famous are content-based filtering (CB) and collaborative filtering (CF).…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Recommendation System for Electrical Energy Optimization and Cost Saving in Smart Homes

et al. 2019

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The European Union Establishes that for the next few years, a cleaner and more efficient energy system should be used. In order to achieve this, this work proposes an energy optimization method that facilitates the achievement of these objectives. Existing technologies allow us to create a system that optimizes the use of energy in homes and offers some type of benefit to its residents. Specifically, this study has developed a recommendation system based on a multiagent system that allows to obtain consumption data from electronic devices in a home, obtain information on electricity prices from the Internet, and provide recommendations based on consumption patterns of users and electricity prices. In this way, the system recommends new hours in which to use the appliances, offering the economic benefit that it would propose recommendations for the user. In this way, it is possible to distribute and optimize the use of energy in homes and reduce the peaks in electricity consumption. The system provides encouraging results in order to resolve the problem proposed by the European Union by optimizing the use of energy among different hours of the day and saving money for the customer.

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Recommender systems: An overview of different approaches to recommendations

Cited by 84 publications

References 6 publications

MENTOR: The Design and Evaluation of a Protection Services Recommender System

MENTOR: The Design and Evaluation of a Protection Services Recommender System

Re-Enrichment Learning: Metadata Saliency for the Evolutive Personalization of a Recommender System

Multi-Agent Recommendation System for Electrical Energy Optimization and Cost Saving in Smart Homes

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