Cognitively Adjusting Imprecise User Preferences for Service Selection

Zhang, Lingyan; Wang, Shangguang; Wong, Raymond K.; Yang, Fangchun; Chang, Rcc

doi:10.1109/tnsm.2017.2731050

Cited by 5 publications

(2 citation statements)

References 44 publications

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“…Clustering has found tremendous applications in many fields, such as business intelligence [31]- [34], pattern recognition [35] and cloud computing [36]- [39]. This paper proposed a new clustering algorithm that is robust to outliers.…”

Section: Discussionmentioning

confidence: 99%

Efficient Clustering Method Based on Density Peaks With Symmetric Neighborhood Relationship

Wu¹,

Lee²,

Isokawa³

et al. 2019

IEEE Access

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The density peaks clustering (DPC) is a clustering method proposed by Rodriguez and Laio (Science, 2014), which sets up a decision graph to identify the cluster centers of data points. Because the improper selection of its parameter cut-off distance will lead to the wrong selection of initial cluster centers with no corrective actions in the subsequent assignment process, DPC may not identify cluster centers with different densities accurately. Especially, all cluster centers are settled as soon as they are detected, after which the DPC simply assigns each point to the same cluster as its nearest neighbor of higher density. This tends to cause the erroneous assignments of data and thus degrade the efficiency of clustering. In this paper, we propose a robust clustering method which establishes a symmetric neighborhood graph over all data points, based on the k-nearest neighbors and reverse k-nearest neighbors of each point. In order to distinguish the density peaks from all data points, local densities of each point are calculated using the reverse k-nearest neighbors. After that, initial centers for clusters are estimated over the peaks and similar clusters are aggregated on the symmetric neighborhood graph, which ends up with every point being successfully assigned to a cluster. To testify the efficiency of the new clustering method, numerical experiments and comparison works have been done on a variety of artificial and real data sets for clustering.INDEX TERMS Clustering, symmetric neighborhood, reverse k-nearest neighbors, density peaks clustering.

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Section: Discussionmentioning

confidence: 99%

Efficient Clustering Method Based on Density Peaks With Symmetric Neighborhood Relationship

Wu¹,

Lee²,

Isokawa³

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Since users' preference always change with time, we adopt the user preference prediction method at FUEs side to improve the caching hit rate. Currently, little literature considers using topic model to predict user preference [16]- [20]. The topic model is a statistical model for discovering potential topics in a large number of files, which can overcome the shortcomings of inaccurate calculation of files similarity in traditional methods.…”

Section: A User Preference Predictionmentioning

confidence: 99%

Deep Q-Learning-Based Content Caching With Update Strategy for Fog Radio Access Networks

et al. 2019

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In order to improve the edge caching efficiency of the fog radio access network (F-RAN), this paper put forward a distributed deep Q-learning-based content caching scheme based on user preference prediction and content popularity prediction. Given that the constraint that the storage capacity of each device is limited, and the optimization problem is formulated so as to maximize the caching hit rate. Specifically, by taking users' selfishness into consideration, user preference is predicted in an offline manner by applying popular topic models. Then, the online predicted content popularity is achieved by combining the network topology relationship together with the obtained user preference. Finally, with the predicted user preference and content popularity, the deep Q-learning network (DQN)-based content caching algorithm is proposed to achieve the optimal content caching strategy. Moreover, we further present a content update policy with user preference and content popularity prediction, so that the proposed algorithm can handle the variations of contents popularity in a timely manner. Simulation results demonstrate that the proposed scheme achieves better caching hit rate compared with existing algorithms.

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Incorporating both qualitative and quantitative preferences for service recommendation

Wang

Tao

et al. 2018

Journal of Parallel and Distributed Computing

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Cognitively Adjusting Imprecise User Preferences for Service Selection

Cited by 5 publications

References 44 publications

Efficient Clustering Method Based on Density Peaks With Symmetric Neighborhood Relationship

Efficient Clustering Method Based on Density Peaks With Symmetric Neighborhood Relationship

Deep Q-Learning-Based Content Caching With Update Strategy for Fog Radio Access Networks

Incorporating both qualitative and quantitative preferences for service recommendation

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