深度学习在Twitter辅助缓存网络中的潜在事件预测
Deep Learning for Latent Events Forecasting in Twitter Aided Caching Networks
摘要：
提出了一种新的Twitter上下文辅助内容缓存（TAC）框架，利用Twitter数据的易读性和海量性来提高缓存效率。为了提高缓存效率，提出了三种机器学习模型，利用收集的带有地理标签的Twitter数据和相邻基站的地理信息预测潜在事件和事件流行度。首先，我们提出了一个潜在的Dirichlet分配（LDA）模型的潜在事件预测利用LDA模型在自然语言处理（NLP）的优势。然后，我们设想了长短期记忆（LSTM）与skip-gram嵌入方法和LSTM与连续skip-gram-Geo-aware嵌入方法用于事件流行度预测。最后，我们将预测的潜在事件和事件的流行度与缓存策略相关联。大量的实际实验表明：（1）TAC框架的性能优于传统的缓存框架，并且能够与公共利益相关联，因此能够在实际应用中得到应用。(2)所提出的LDA方法保留了Twitter数据中自然语言处理（NLP）的优势。(3)与传统的LDA方法相比，所提出的基于skip-gram的LSTM的复杂度更低。(4)对该模型的评价表明，该模型的推文命中率在50%~ 65%之间，缓存内容命中率可达75%左右，且缓存空间比传统算法小。
英文摘要：
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 collect Twitter data with geo-tags and geographic information of the adjacent base stations (BSs). Firstly, we propose a latent Dirichlet allocation (LDA) model for latent events forecasting taking advantage of the superiority of the 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-Geo-aware embedding approach for the events popularity forecasting. Lastly, we associate the predicted latent events and the popularity of the events with the caching strategy. Extensive practical experiments demonstrate that: (1) The proposed TAC framework outperforms the 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 the 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
