P2PCF: A collaborative filtering based recommender system for peer to peer social networks

Badis, Lyes; Amad, Mourad; Aı̈ssani, Djamil; Abbar, Sofiane

doi:10.3233/jhs-210649

Cited by 10 publications

(7 citation statements)

References 64 publications

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“…It is used to calculate the score grade difference between different users of the project. His work is mainly to improve the formula for calculating user similarity, which is used to improve user similarity by adding the balance factor and weight [ 8 ]. Hsu and others proposed to use of the matrix decomposition method for collaborative filtering recommendations.…”

Section: Related Workmentioning

confidence: 99%

Data Mining Method of Intelligent Market Management Based on Collaborative Recommendation Algorithm

Wu¹,

Wang²,

Cai³

2022

Computational Intelligence and Neuroscience

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In order to solve the problems of low coverage and accuracy and large mean absolute error and root mean square error when traditional algorithms recommend market management data, this paper proposes an intelligent market management data mining method based on a collaborative recommendation algorithm. According to the preference value of the attribute characteristics of market management data, predict and score the attribute characteristics of market management data; use data mining technology to preprocess the information of market management data, combined with the design of collaborative filtering recommendation algorithm; and realize the collaborative filtering recommendation of market management data. With 50 recommendations, AGCAN improves the accuracy of MovieLens-1M by 43.81%, 5.43%, 1.87%, 0.42%, and 1.67%, respectively, compared with the five benchmark algorithms. For MovieLens-100K, compared with the five benchmark algorithms, AGCAN improves the accuracy by 51.17%, 10.52%, 3.37%, 0.1%, and 0.30%, respectively. Compared with the five benchmark algorithms, Amazon-baby and AGCAN have improved the accuracy by 34.37%, 28.12%, 31.25%, 29.1%, and 3.12%, respectively. The algorithm proposed in this paper uses a graph neural network to mine useful information between users and projects, but it lacks the use of other personalized interest information of users, such as user interest, user purchase time, and so on.

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Section: Related Workmentioning

confidence: 99%

Data Mining Method of Intelligent Market Management Based on Collaborative Recommendation Algorithm

Wu¹,

Wang²,

Cai³

2022

Computational Intelligence and Neuroscience

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“…Lyes Badis et al ( 2021) [43] The authors used a collaborative filtering approach to recommend content in P2P social networks, claimed P2PCF enables privacy preservation, and tackled the cold start problem for both users and content.…”

Section: Study and Year Methodology Remarksmentioning

confidence: 99%

“…(1). Incremental K-means A variation of K-means, namely, incremental K-means, was used, which was described in [43]. The initialization problem of centroids is no longer a problem with this technique.…”

Section: Combination Of Classification Algorithms With Social-based C...mentioning

confidence: 99%

HCoF: Hybrid Collaborative Filtering Using Social and Semantic Suggestions for Friend Recommendation

et al. 2023

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Today, people frequently communicate through interactions and exchange knowledge over the social web in various formats. Social connections have been substantially improved by the emergence of social media platforms. Massive volumes of data have been generated by the expansion of social networks, and many people use them daily. Therefore, one of the current problems is to make it easier to find the appropriate friends for a particular user. Despite collaborative filtering’s huge success, accuracy and sparsity remain significant obstacles, particularly in the social networking sector, which has experienced astounding growth and has a large number of users. Social connections have been substantially improved by the emergence of social media platforms. In this work, a social and semantic-based collaborative filtering methodology is proposed for personalized recommendations in the context of social networking. A new hybrid collaborative filtering (HCoF) approach amalgamates the social and semantic suggestions. Two classification strategies are employed to enhance the performance of the recommendation to a high rate. Initially, the incremental K-means algorithm is applied to all users, and then the KNN algorithm for new users. The mean precision of 0.503 obtained by HCoF recommendation with semantic and social information results in an effective collaborative filtering enhancement strategy for friend recommendations in social networks. The evaluation’s findings showed that the proposed approach enhances recommendation accuracy while also resolving the sparsity and cold start issues.

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“…The curriculum implementation evaluation is the process of implementing the scheduled plan, and the measurement standard is the degree of achievement of teaching objectives. From the political point of view, the curriculum implementation evaluation is regarded as the interest competition between groups, and different competitive relationships maintain different attitudes towards the curriculum implementation evaluation [2,6,20]. From the culture perspective, curriculum implementation evaluation is regarded as cultural regeneration, and curriculum implementation evaluation is the process of promoting teachers to think over teaching arrangements.…”

Section: Qualitative Evaluation Of Course Implementation Based On Col...mentioning

confidence: 99%

Evaluation of Curriculum Implementation Effectiveness of Higher Vocational Education Based on Collaborative Filtering Algorithm

Wang,

2023

SCPE

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The conventional evaluation method for the effectiveness of curriculum implementation mainly focuses on the complete orientation analysis of students’ curriculum content, which does not reflect the value of the educational curriculum and affects the effectiveness of evaluation. Therefore, an evaluation method of curriculum implementation effectiveness of higher vocational education based on a collaborative filtering algorithm is designed. Identify the practical focus of evaluating the implementation of the higher vocational education curriculum and discover the educational curriculum’s significance. Qualitative evaluation of curriculum implementation degree based on collaborative filtering algorithm, find out the hidden characteristics of curriculum implementation evaluation to effectively evaluate higher vocational education curriculum implementation. Using case analysis, it is verified that the method is more effective and can be applied in real life.

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P2PCF: A collaborative filtering based recommender system for peer to peer social networks

Cited by 10 publications

References 64 publications

Data Mining Method of Intelligent Market Management Based on Collaborative Recommendation Algorithm

Data Mining Method of Intelligent Market Management Based on Collaborative Recommendation Algorithm

HCoF: Hybrid Collaborative Filtering Using Social and Semantic Suggestions for Friend Recommendation

Evaluation of Curriculum Implementation Effectiveness of Higher Vocational Education Based on Collaborative Filtering Algorithm

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