An Introduction to Basic Concepts on Recommender Systems

Rana, Pooja; Jain, Nishi; Mittal, Usha

doi:10.1002/9781119711582.ch1

Cited by 4 publications

(5 citation statements)

References 8 publications

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“…The process of CBF technique is to select the same feature type and calculating the similarity for items, then recommends items based on the similar content [7]. Content-based filtering can be classified into 2 tasks: user profiling and job profiling [15]. User profiling often deals with acquiring, extracting and representing the feature of users.…”

Section: Content-based Filtering (Cbf)mentioning

confidence: 99%

“…Hybrid Filtering technique has been categories into seven different types: Weighted, Switching, Mixed, Feature Combination, Feature Augmentation, Cascade, Meta-level. Each of these techniques has its own strengths and weaknesses, and the choice of which one to use depends on the specific application and the available data [15].…”

Section: Hybrid Filtering Technique (Hf)mentioning

confidence: 99%

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A Review on Job Recommendation System

Zhou Zou,

Sharin Hazlin Huspi,

Ahmad Najmi Amerhaider Nuar

2024

ARASET

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With the rapid growth of artificial intelligence and machine learning technologies, the recommendation system aims to help users find items that match their preferences. In order to improve performance, many recommendation system techniques have been proposed. This paper presents a survey of some common recommendation techniques and related issues with advantages and disadvantages. At the same time, the different types of job recommendation systems are described in detail and compared with each other. The goal is to provide a comprehensive overview of the current state of job recommendation systems and to analyze the characteristics of each system. The results of the case studies can contribute to a better understanding of the strengths and weaknesses, as well as techniques used in different job recommendation systems. Through this review, insights are provided to guide the development of more effective recommendation systems. For future research, a system is proposed to generate career move recommendations with upskilling and reskilling suggestions.

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Section: Content-based Filtering (Cbf)mentioning

confidence: 99%

Section: Hybrid Filtering Technique (Hf)mentioning

confidence: 99%

A Review on Job Recommendation System

Zhou Zou,

Sharin Hazlin Huspi,

Ahmad Najmi Amerhaider Nuar

2024

ARASET

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“…These are categorized into memory-based collaborative filtering systems and modelbased filtering systems (Zhang, 2021). Memory-based collaborative filtering recommendation systems are categorized into user-based filtering and item-based filtering (Rana et al, 2020;Roy & Dutta, 2022). In user-based filtering, the similarity between users is rated in relation to a given item.…”

Section: Collaborative Filtering (Cf)mentioning

confidence: 99%

“…Model-based collaborative filtering systems use machine learning and data mining to predict user ratings to unrated items (Rana et al, 2020;Roy & Dutta, 2022).…”

Section: Collaborative Filtering (Cf)mentioning

confidence: 99%

The Barcelona Conference on Education 2023: Official Conference Proceedings

2023

BCE Official Conference Proceedings

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“…Content-based systems suffer from the over-specialization problem, because they only suggest products close to those that users have already scored. The implementation of any randomness may be one solution to this problem [30]. Furthermore, over-specialization is not simply the problem that content-based programs cannot suggest things that are not similar to what the user has already seen.…”

Section: Proposed Approach 41 Over-specialization Problemmentioning

confidence: 99%

An Improved Recommender System Solution to Mitigate the Over-Specialization Problem Using Genetic Algorithms

2022

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Nowadays, recommendation systems offer a method of facilitating the user’s desire. It is useful for recommending items from a variety of areas such as in the e-commerce, medical, education, tourism, and industry domains. The e-commerce area represents the most active research we found, which assists users in locating the things they want. A recommender system can also provide users with helpful knowledge about things that could be of interest. Sometimes, the user gets bored with recommendations which are similar to their profiles, which leads to the over-specialization problem. Over-specialization is caused by limited content data, under which content-based recommendation algorithms suggest goods directly related to the customer profile rather than new things. In this study, we are particularly interested in recommending surprising, new, and unexpected items that may likely be enjoyed by users and will mitigate this limited content. In order to recommend novel and serendipitous items along with familiar items, we need to introduce additional hacks and note of randomness, which can be achieved using genetic algorithms that brings diversity to recommendations being made. This paper describes a Revolutionary Recommender System using a Genetic Algorithm called RRSGA which improves the fitness functions for recommending optimal results. The proposed approach employs a genetic algorithm to address the over-specialization issue of content-based filtering. The proposed method aims to incorporate genetic algorithms that bring variety to recommendations and efficiently adjust and suggest unpredictable and innovative things to the user. Experiments objectively demonstrate that our technology can recommend additional products that every consumer is likely to appreciate. The results of RRSGA have been compared against recommendation results from the content-based filtering approach. The results indicate the effectiveness of RRSGA and its capacity to make more accurate predictions than alternative approaches.

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An Introduction to Basic Concepts on Recommender Systems

Cited by 4 publications

References 8 publications

A Review on Job Recommendation System

A Review on Job Recommendation System

The Barcelona Conference on Education 2023: Official Conference Proceedings

An Improved Recommender System Solution to Mitigate the Over-Specialization Problem Using Genetic Algorithms

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