A general and effective diffusion-based recommendation scheme on coupled social networks

Deng, Xiaofang; Zhong, Yuansheng; Lü, Linyuan; Xiong, Naixue; Yeung, Chi Ho

doi:10.1016/j.ins.2017.07.021

Cited by 23 publications

(15 citation statements)

References 41 publications

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“…Nowadays, the new recommender algorithms are required for real-world applications, because of the following reasons [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][21][22][23][24][29][30][31][32][43][44][45][46][47][66][67][68][69][70]:…”

Section: Features-based Recommendersmentioning

confidence: 99%

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Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets

2020

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Nowadays, because of the tremendous amount of information that humans and machines produce every day, it has become increasingly hard to choose the more relevant content across a broad range of choices. This research focuses on the design of two different intelligent optimization methods using Artificial Intelligence and Machine Learning for real-life applications that are used to improve the process of generation of recommenders. In the first method, the modified cluster based intelligent collaborative filtering is applied with the sequential clustering that operates on the values of dataset, user′s neighborhood set, and the size of the recommendation list. This strategy splits the given data set into different subsets or clusters and the recommendation list is extracted from each group for constructing the better recommendation list. In the second method, the specific features-based customized recommender that works in the training and recommendation steps by applying the split and conquer strategy on the problem datasets, which are clustered into a minimum number of clusters and the better recommendation list, is created among all the clusters. This strategy automatically tunes the tuning parameter λ that serves the role of supervised learning in generating the better recommendation list for the large datasets. The quality of the proposed recommenders for some of the large scale datasets is improved compared to some of the well-known existing methods. The proposed methods work well when λ = 0.5 with the size of the recommendation list, |L| = 30 and the size of the neighborhood, |S| < 30. For a large value of |S|, the significant difference of the root mean square error becomes smaller in the proposed methods. For large scale datasets, simulation of the proposed methods when varying the user sizes and when the user size exceeds 500, the experimental results show that better values of the metrics are obtained and the proposed method 2 performs better than proposed method 1. The significant differences are obtained in these methods because the structure of computation of the methods depends on the number of user attributes, λ, the number of bipartite graph edges, and |L|. The better values of the (Precision, Recall) metrics obtained with size as 3000 for the large scale Book-Crossing dataset in the proposed methods are (0.0004, 0.0042) and (0.0004, 0.0046) respectively. The average computational time of the proposed methods takes <10 seconds for the large scale datasets and yields better performance compared to the well-known existing methods.

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Section: Features-based Recommendersmentioning

confidence: 99%

“…These issues generally decrease the quality of referrals. To alleviate some of the issues identified, hybrid filtration has been proposed combining two or more filtration methods in various ways to enlarge the accuracy and efficiency of the recommender systems [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets

2020

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“…In (19), (20) and (22), the parameters relevant to m i should also be computed through the vector form of title, keywords, and description of knowledge. Then, the IIG value that takes the distribution of m i into account is computed by using (23).…”

Section: Ig(m Imentioning

confidence: 99%

A Correlation-Experience-Demand Based Personalized Knowledge Recommendation Approach

et al. 2019

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Knowledge recommendation is an important means of knowledge reuse that can improve the efficiency and quality of product design. However, at present, there is no good way to fully consider the personalized demands of designers while ensuring the applicability of the recommendation results. Previous studies have usually been based on the similarity between tasks and knowledge or use collaborative filtering technology to accomplish knowledge recommendation. However, these methods do not consider the personal experience of designers and the characteristics of knowledge. This paper proposes a knowledge recommendation approach that integrates the degree of correlation between knowledge and tasks, the feedback-based personal experience, the collective experience of designers, and the degree of demand for knowledge based on the forgetting curve. A knowledge assistance score is generated based on these factors, and the knowledge recommendation list is obtained by ranking the knowledge in descending order of this score. Finally, the approach is applied to a machine shop layout design task and a computer numerical control (CNC) machine tool's spindle design and bearings selection task. The experimental results on two tasks demonstrate that the proposed approach outperforms three baselines on three ranking oriented evaluation metrics. This approach can effectively shorten the time for designers to acquire knowledge by recommending applicable knowledge to assist designers in completing design tasks with high quality and efficiency. INDEX TERMS Collaborative filtering, degree of assistance, degree of correlation, degree of demand, knowledge recommendation, ontology model.

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“…For instance, mass diffusion (MD) [12], heat conduction(HC) [11] and hybrid methods of both (Hyb) [13] are personalized recommendation algorithms based on diffusion processes on bipartite networks [15]. Besides, there are also plenty of extension methods which are designed to improve the recommendation performance, such as indirect-link-weakened mass diffusion method (IMD) [16] ,non-equilibrium mass diffusion algorithm (NMD) [17] and so on [18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Predictability of diffusion-based recommender systems

Zhang

Xue

Zeng

2019

Knowledge-Based Systems

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The recommendation methods based on network diffusion have been shown to perform well in both recommendation accuracy and diversity. Nowdays, numerous extensions have been made to further improve the performance of such methods. However, to what extent can items be predicted by diffusion-based algorithms still lack of understanding. Here, we mainly propose a method to quantify the predictability of diffusion-based algorithms. Accordingly, we conduct experiments on Movielens and Netflix datasets. The results show that the higher recommendation accuracy based on diffusion algorithms can still be achieved by optimizing the way of resource allocation on a density network. On a sparse network, the possibility of improving accuracy is relatively low due to the fact that the current accuracy of diffusion-based methods is very close its predictability. In this case, we find that the predictability can be improved significantly by multi-steps diffusion, especially for users with less historical information. In contrast to common belief, there are plausible circumstances where the higher predictability of diffusion-based methods do not correspond to those users with more historical recording. Thus, we proposed the diffusion coverage and item average degree to explain this phenomenon. In addition, we demonstrate the recommendation accuracy in real online system is overestimated by random partition used in the literature, suggesting the recommendation in real online system may be a harder task.

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A general and effective diffusion-based recommendation scheme on coupled social networks

Cited by 23 publications

References 41 publications

Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets

Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets

A Correlation-Experience-Demand Based Personalized Knowledge Recommendation Approach

Predictability of diffusion-based recommender systems

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