A Soft-Rough Set Based Approach for Handling Contextual Sparsity in Context-Aware Video Recommender Systems

Abbas, Syed Manzar; Alam, Khubaib Amjad; Shamshirband, Shahaboddin

doi:10.3390/math7080740

Cited by 15 publications

(9 citation statements)

References 42 publications

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“…Che and Mi defined the multi-granularity variable precision rough set by considering the importance of each attribute set relative to all attribute sets and studied the attribute set reduction by combining the smallest element of the distinguishable matrix and distributed distinguishable function [31]. Abbas et al proposed a novel context information selection process using soft rough sets [32].…”

Section: Literature Reviewmentioning

confidence: 99%

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Zhou

Tan

et al. 2020

Symmetry

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The neighborhood rough set theory was adopted for attributes reduction and the weight distribution of condition attributes based on the concept of importance level. Smart elderly care coverage rate is low in China. A decisive role in the adoption of smart elderly care is still a problem that needs to be addressed. This study contributes to the adoption of smart elderly care was selected as the decision attribute. The remaining attributes are used as conditional attributes and the multi-level symmetric attribute set for assessing acceptance of smart elderly care. Prior studies are not included smart elderly care adoption attributes in multi-levels; hence, this problem needs to be addressed. The results of this study indicate that the condition attribute of gender has the greatest influence on the decision attribute. The condition attribute of living expenses for smart elderly care has the second largest impact on decision attribute. Children’s support for the elderly decency of the novel elderly care system and the acceptance of non-traditional elderly care methods belong to the primary condition attribute of traditional concept. The result indicates traditional concepts have a certain impact on the adoption of smart elderly care and a condition attribute of residence also has a slight influence on the symmetric decision attribute. The sensitivity analysis shows the insights for uncertainties and provides as a basis for the analysis of the attributes in the smart elderly care service adoption.

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Section: Literature Reviewmentioning

confidence: 99%

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Zhou

Tan

et al. 2020

Symmetry

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“…As for future work, on the one hand, we plan to incorporate some external knowledge, e.g., category information and content information, to capture the item relations more accurately [44][45][46][47][48]. For example, some items are complement products to each other and should be recommended together, especially in some e-commerce scenarios.…”

Section: Discussionmentioning

confidence: 99%

Collaborative Co-Attention Network for Session-Based Recommendation

Chen

2021

Mathematics

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Session-based recommendation aims to model a user’s intent and predict an item that the user may interact with in the next step based on an ongoing session. Existing session-based recommender systems mainly aim to model the sequential signals based on Recurrent Neural Network (RNN) structures or the item transition relations between items with Graph Neural Network (GNN) based frameworks to identify a user’s intent for recommendation. However, in real scenarios, there may be strong sequential signals existing in users’ adjacent behaviors or multi-step transition relations among different items. Thus, either RNN- or GNN-based methods can only capture limited information for modeling complex user behavior patterns. RNNs pay attention to the sequential relations among consecutive items, while GNNs focus on structural information, i.e., how to enrich the item embedding with its adjacent items. In this paper, we propose a Collaborative Co-attention Network for Session-based Recommendation (CCN-SR) to incorporate both sequential and structural information, as well as capture the co-relations between them for obtaining an accurate session representation. To be specific, we first model the ongoing session with an RNN structure to capture the sequential information among items. Meanwhile, we also construct a session graph to learn the item representations with a GNN structure. Then, we design a co-attention network upon these two structures to capture the mutual information between them. The designed co-attention network can enrich the representation of each node in the session with both sequential and structural information, and thus generate a more comprehensive representation for each session. Extensive experiments are conducted on two public e-commerce datasets, and the results demonstrate that our proposed model outperforms state-of-the-art baseline model for session based recommendation in terms of both Recall and MRR. We also investigate different combination strategies and the experimental results verify the effectiveness of our proposed co-attention mechanism. Besides, our CCN-SR model achieves better performance than baseline models with different session lengths.

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“…The context-aware video recommender system is developed to improve recommendation performance by incorporating contextual features along with the conventional user-item ratings used by video recommender systems [66]. The CF algorithm is discussed to confront the sparsity problem in the resulting graph partitions that may improve the prediction performance of parallel implementations without strongly affecting their time efficiency [67].…”

Section: Features-based Recommendersmentioning

confidence: 99%

“…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%

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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A Soft-Rough Set Based Approach for Handling Contextual Sparsity in Context-Aware Video Recommender Systems

Cited by 15 publications

References 42 publications

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Collaborative Co-Attention Network for Session-Based Recommendation

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

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