Optimization of fuzzy similarity by genetic algorithm in user‐based collaborative filtering recommender systems

Nanehkaran, Farimah Houshmand; Lajevardi, Seyed Mohammadreza; Mahlouji, Mahmoud

doi:10.1111/exsy.12893

Cited by 7 publications

(5 citation statements)

References 77 publications

(124 reference statements)

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“…On the song data set, the accuracy and recall of recommendation algorithms are compared, and the accuracy and recall of different algorithms are shown when the number of songs recommended to users is 5, 10, 15, 20, 25 and 30 respectively. In terms of accuracy and recall, hybrid recommendation is superior to content-based recommendation and collaborative recommendation of users, so this paper chooses hybrid recommendation, and the experimental results also verify the previous ideas [9][10].…”

Section: Results and Analysissupporting

confidence: 52%

Design of Music Heritage Database System Based on Collaborative Filtering Algorithm

Tian,

Liu

2023

Frontiers in Artificial Intelligence and Applications

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In order to understand the design of music heritage database system, a research on the design of music heritage database system based on collaborative filtering algorithm is put forward. In this paper, firstly, collaborative filtering recommendation algorithm is studied, and users are recommended through two models that realize the algorithm, namely user-based collaborative filtering and item-based collaborative filtering. Finally, the recommendation effects of the two models are compared. Secondly, based on collaborative filtering, the content-based music recommendation algorithm is integrated, and a music recommendation system is designed. When the traditional collaborative filtering algorithm is used to recommend to users, some popular songs will affect the similarity calculation between users and between songs, resulting in poor recommendation effect. In this paper, the influence of popular songs on similarity calculation is reduced by punishing popular songs, and the accuracy of recommendation is improved by improving similarity calculation. Finally, the experimental results on the crawled Netease cloud music data show that the accuracy and recall of the hybrid recommendation algorithm are better than those based on collaborative filtering and content.

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Section: Results and Analysissupporting

confidence: 52%

Design of Music Heritage Database System Based on Collaborative Filtering Algorithm

Tian,

Liu

2023

Frontiers in Artificial Intelligence and Applications

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“…(1) User-based CF (User-CF) This method computes user similarity to recommend items that similar users like [8]. The steps are as follows.…”

Section: Collaborative Filtering Algorithmmentioning

confidence: 99%

Explore the Personalized Resource Recommendation of Educational Learning Platforms: Deep Learning

Qi,

Zhao,

2024

IJCAI

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With the development of educational learning platforms, the resources available on them have become increasingly abundant, which has increased the difficulty of personalized resource recommendations. To further improve the effectiveness of personalized recommendation, this paper first analyzed the neural collaborative filtering (NeuCF) algorithm and then improved generalized matrix factorization (GMF) to expanded GMF (EGMF). Furthermore, additional user and project information was incorporated into the input to better capture user preferences and further improve personalized recommendation effects. Experiments were performed using data from a massive open online courses (MOOC) platform. The experimental results demonstrated that the improved NeuCF (INeuCF) algorithm outperformed other algorithms, including the user-collaborative filtering algorithm, in personalized resource recommendation. When the length of the recommendation list was 10, the INeuCF algorithm achieved an F1 value of 0.227 and a normalized discounted cumulative gain (NDCG) value of 0.337. In comparison to the NeuCF algorithm, the EGMF improved the F1 value by 0.008 and the NDCG value by 0.005. Additionally, the incorporation of other information further enhanced the F1 value by 0.01 and the NDCG value by 0.007. These results verify the effectiveness of the proposed improvement to the NeuCF algorithm and suggest that the method can be practically applied to educational learning platforms to achieve more effective personalized resource recommendations. Povzetek: Članek se ukvarja s priporočanjem virov na izobraževalnih platformah z uporabo globokega učenja. Predlagana je izboljšava algoritma nevronskega kolaborativnega filtriranja (INeuCF), ki vključuje dodatne informacije uporabnika in projekta.

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“…There are different types of AI techniques used in recommender systems, depending on the specific approach used to build the system. Some of the common types of AI techniques used in recommender systems are the following: in the memory-based collaborative filtering recommender system (RS) to accurately calculate the similarities between users and finally finding interesting recommendations for active users [90]. * Item-based collaborative filtering utilises the similarity values of items for predicting the target item [91].…”

Section: Ai Techniques For the Ux Of Recommender Systemsmentioning

confidence: 99%

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Virvou¹

2023

IDT

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Among the primary aims of Artificial Intelligence (AI) is the enhancement of User Experience (UX) by providing deep understanding, profound empathy, tailored assistance, useful recommendations, and natural communication with human interactants while they are achieving their goals through computer use. To this end, AI is used in varying techniques to automate sophisticated functions in UX and thereby changing what UX is apprehended by the users. This is achieved through the development of intelligent interactive systems such as virtual assistants, recommender systems, and intelligent tutoring systems. The changes are well received, as technological achievements but create new challenges of trust, explainability and usability to humans, which in turn need to be amended by further advancements of AI in reciprocity. AI can be utilised to enhance the UX of a system while the quality of the UX can influence the effectiveness of AI. The state of the art in AI for UX is constantly evolving, with a growing focus on designing transparent, explainable, and fair AI systems that prioritise user control and autonomy, protect user data privacy and security, and promote diversity and inclusivity in the design process. Staying up to date with the latest advancements and best practices in this field is crucial. This paper conducts a critical analysis of published academic works and research studies related to AI and UX, exploring their interrelationship and the cause-effect cycle between the two. Ultimately, best practices for achieving a successful interrelationship of AI in UX are identified and listed based on established methods or techniques that have been proven to be effective in previous research reviewed.

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Optimization of fuzzy similarity by genetic algorithm in user‐based collaborative filtering recommender systems

Cited by 7 publications

References 77 publications

Design of Music Heritage Database System Based on Collaborative Filtering Algorithm

Design of Music Heritage Database System Based on Collaborative Filtering Algorithm

Explore the Personalized Resource Recommendation of Educational Learning Platforms: Deep Learning

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

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