Cooperative Cache-Aware Recommendation System for Multiple Internet Content Providers

Zheng, Dongsheng; Chen, Yingyang; Yin, Mingxi; Jiao, Bingli

doi:10.1109/lwc.2020.3014266

Cited by 13 publications

(14 citation statements)

References 12 publications

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“…There are some research contributions related with this topic, such as context-aware schemes [22,23] and content recommendation schemes [24,25]. The authors in [22] proposed a posteriori caching scheme that decides the content placement before the analysis of the placement gain.…”

Section: B Motivation and Contributionmentioning

confidence: 99%

“…Although the data set from MovieLens is used in [22], the semantic environment of twitter data is more complex than that of MovieLens. The authors in [24,25] proposed cache-aware recommendation systems, in which the recommendation strategy is adopted to enhance the gain of content caching. However, the proposed recommendation systems require the users' preference as prior information, which is often hard to obtain.…”

Section: B Motivation and Contributionmentioning

confidence: 99%

“…However, the proposed recommendation systems require the users' preference as prior information, which is often hard to obtain. Therefore, the proposed ML based latent events forecasting can work as a complementary for references [24,25]. The predicted latent events can be used for the recommendation systems in [24,25] to improve the performance of the recommendation policies.…”

Section: B Motivation and Contributionmentioning

confidence: 99%

“…Therefore, the proposed ML based latent events forecasting can work as a complementary for references [24,25]. The predicted latent events can be used for the recommendation systems in [24,25] to improve the performance of the recommendation policies. The comparison between this paper and previous works is given in Table I.…”

Section: B Motivation and Contributionmentioning

confidence: 99%

“…The comparison between this paper and previous works is given in Table I. Noting that references [23][24][25] only considered content placement in caching networks, content delivery is not considered, therefore, [?, 24,25] cannot be extended to NOMA/OMA case. Meanwhile, the transmission delay is analysed in references [8] and [22], therefore [8] and [22] can be extended to NOMA/OMA case.…”

Section: B Motivation and Contributionmentioning

confidence: 99%

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Deep Learning for Latent Events Forecasting in Content Caching Networks

Yang

Liu

Chen

et al. 2022

IEEE Trans. Wireless Commun.

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A novel Twitter context aided content caching (TAC) framework is proposed for enhancing the caching efficiency by taking advantage of the legibility and massive volume of Twitter data. For the purpose of promoting the caching efficiency, three machine learning models are proposed to predict latent events and events popularity, utilizing collected Twitter data with geotags and geographic information of the adjacent base stations (BSs). Firstly, we propose a latent Dirichlet allocation (LDA) model for latent events forecasting because of the superiority of LDA model in natural language processing (NLP). Then, we conceive long short-term memory (LSTM) with skip-gram embedding approach and LSTM with continuous skip-gram-Geoaware embedding approach for the events popularity forecasting. Furthermore, we associate the predict latent events and the popularity of the events with the caching strategy. Lastly, we propose a non-orthogonal multiple access (NOMA) based content transmission scheme. Extensive practical experiments demonstrate that: (1) The proposed TAC framework outperforms conventional caching framework and is capable of being employed in practical applications thanks to the associating ability with public interests. (2) The proposed LDA approach conserves superiority for natural language processing (NLP) in Twitter data. (3) The perplexity of the proposed skip-gram based LSTM is lower compared with conventional LDA approach. (4) Evaluation of the model demonstrates that the hit rates of tweets of the model vary from 50% to 65% and the hit rate of the caching contents is up to approximately 75% with smaller caching space compared to conventional algorithms. Simulation results also shows that the proposed NOMA-enabled caching scheme outperforms conventional least frequently used (LFU) scheme by 25%.

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Section: B Motivation and Contributionmentioning

confidence: 99%

Section: B Motivation and Contributionmentioning

confidence: 99%

Section: B Motivation and Contributionmentioning

confidence: 99%

Section: B Motivation and Contributionmentioning

confidence: 99%

Section: B Motivation and Contributionmentioning

confidence: 99%

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Deep Learning for Latent Events Forecasting in Content Caching Networks

Yang

Liu

Chen

et al. 2022

IEEE Trans. Wireless Commun.

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Adaptive preference transfer for personalized IoT entity recommendation

Yan

Liu²,

Sun³

et al. 2022

Pattern Recognition Letters

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On the design of cost minimization for D2D‐enabled wireless caching networks: A joint recommendation, caching, and routing perspective

Hua,

Fu,

Zhu

2024

IET Communications

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Cache‐enabled device‐to‐device (D2D) network has been deemed as an effective technique to offload the data traffic. However, the gain of the caching schemes is closely related to the homogeneity among users' preference distribution. To tackle this issue, recommendation is a promising proactive approach. It increases the request probability of recommended contents, reshaping users' contents demand patterns, and improving caching performance. Moreover, considering the heterogeneous network settings, i.e. content retrieval costs vary, the routing design becomes a non‐negligible factor on caching performance optimization. On these grounds, the average system cost of D2D‐enabled wireless caching networks with multiple BSs is first described. Then the routing strategies are designed together with caching and recommendation policies by minimizing the average cost of these networks. The optimization problem is proven as NP‐hard. To facilitate the analysis, the original problem is decoupled into two sub‐problems and solve them respectively. Afterwards, all the variables are optimized in an alternating manner until the convergence is achieved. The proposed algorithm's convergence performance and benefits over benchmark strategies in terms of total cost and cache hit ratio are supported by Monte‐Carlo simulation results.

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Cooperative Cache-Aware Recommendation System for Multiple Internet Content Providers

Cited by 13 publications

References 12 publications

Deep Learning for Latent Events Forecasting in Content Caching Networks

Deep Learning for Latent Events Forecasting in Content Caching Networks

Adaptive preference transfer for personalized IoT entity recommendation

On the design of cost minimization for D2D‐enabled wireless caching networks: A joint recommendation, caching, and routing perspective

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