Mobile Edge Cache Strategy Based on Neural Collaborative Filtering

Chen, Yu; Liu, Yong; Zhao, Junhui; Zhu, Qinghua

doi:10.1109/access.2020.2964711

Cited by 43 publications

(21 citation statements)

References 19 publications

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“…Chen et al [31], have proposed a neural collaborative filtering caching strategy for edge computing. The proposed method incorporates a greedy algorithm, popularity prediction algorithm and a content cache replacement algorithm.…”

Section: Edge Caching Algorithmsmentioning

confidence: 99%

A Novel Predictive-Collaborative-Replacement (PCR) Intelligent Caching Scheme for Multi-Access Edge Computing

2021

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Multi-Access Mobile Edge Computing (MEC) is proclaimed as a key technology for reducing service processing delays in 5G networks. One of the use cases in MEC is content caching as a way of bringing resources closer to the end-users. Consequently, both latency and QoE are reduced. However, MEC has a limited storage space compared to the cloud. Therefore, there is a need to effectively manage the cache storage. This paper proposes and evaluates a novel scheme (PCR) that combines proactive prediction, collaboration among MECs and replacement algorithm to manage content caching in MEC. Results show that the proposed replacement scheme outperforms conventional baseline content caching algorithms LFU, LRU, MQ, FBR, LFRU. This has been validated with experimental results using a real dataset (MovieLens20M dataset) and comparison with contemporary Long Short Term Memory (LSTM) based caching algorithm.

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Section: Edge Caching Algorithmsmentioning

confidence: 99%

A Novel Predictive-Collaborative-Replacement (PCR) Intelligent Caching Scheme for Multi-Access Edge Computing

2021

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“…There is an inextricable relationship between neural networks and MF models [4], [22]. For example, Neural Collaborative Filtering (NCF) [23] generalizes MF from the perspective of neural networks to achieve CF.…”

Section: B Neural-topic Collaborative Filteringmentioning

confidence: 99%

Memory Reorganization: A Symmetric Memory Network for Reorganizing Neighbors and Topics to Complete Rating Prediction

et al. 2020

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Using pre-trained topic information to assist in training neural networks can effectively support the completion of the rating prediction task. However, existing neural-topic methods consider only the use of topic information corresponding to current users and items without neighbors, whereas existing memory-based neighborhood approaches are inappropriate for the direct modeling of neighbors with topics. To address the limitations, we argue that memory networks have the ability to organize neighbors with corresponding topics well and can provide a general solution to this problem. To confirm our hypothesis, we propose two approaches. One is an augmented memory network to couple with and enhance existing neural-topic models. The other is a symmetric memory network activated by a memory reorganization mechanism, which is a compact and generalized method for rating prediction. The experimental results demonstrate the effectiveness of the memory reorganization mechanism and show that the two proposed methods have advantages over existing state-of-the-art topic modeling approaches.

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“…Zarzour et al [16] presented a new effective model-based trust collaborative filtering to improve the quality of recommendation. In addition, there are some collaborative filtering algorithms based on clustering [17], neural networks [18], and various probability models [19]. The above studies optimized the recommendation model to a certain extent and improved the accuracy of the recommendation results, but there are still some problems to be further studied.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics

Cai

Tang

et al. 2021

Applied Sciences

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The recommendation algorithm is a very important and challenging issue for a personal recommender system. The collaborative filtering recommendation algorithm is one of the most popular and effective recommendation algorithms. However, the traditional collaborative filtering recommendation algorithm does not fully consider the impact of popular items and user characteristics on the recommendation results. To solve these problems, an improved collaborative filtering algorithm is proposed, which is based on the Term Frequency-Inverse Document Frequency (TF-IDF) method and user characteristics. In the proposed algorithm, an improved TF-IDF method is used to calculate the user similarity on the basis of rating data first. Secondly, the multi-dimensional characteristics information of users is used to calculate the user similarity by a fuzzy membership method. Then, the above two user similarities are fused based on an adaptive weighted algorithm. Finally, some experiments are conducted on the movie public data set, and the experimental results show that the proposed method has better performance than that of the state of the art.

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Mobile Edge Cache Strategy Based on Neural Collaborative Filtering

Cited by 43 publications

References 19 publications

A Novel Predictive-Collaborative-Replacement (PCR) Intelligent Caching Scheme for Multi-Access Edge Computing

A Novel Predictive-Collaborative-Replacement (PCR) Intelligent Caching Scheme for Multi-Access Edge Computing

Memory Reorganization: A Symmetric Memory Network for Reorganizing Neighbors and Topics to Complete Rating Prediction

Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics

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