一种基于协同过滤与BG/NBD模型数据预测的智能手机节能策略

Smartphone energy-efficiency strategy based on collaborative filtering and BG/NBD model

为了节省智能手机电池的能源,基于用户访问数据的可预测性,提出一个基于用户访问数据预测的手机节能策略模型。利用基于混合变量属性的K-means算法对已知用户进行聚类分组,建立相似用户群;利用BG/NBD模型对用户连续搜索期望进行预测;针对有价值的用户,结合协同过滤推荐算法,通过相似用户的历史数据分析预测当前用户未来可能访问的数据信息;利用数据预存储机制预存上述预测数据,通过降低通信次数的方式达到手机节能的目的。初步实验结果表明,所提出的节能策略可以在不影响用户使用满意度的情况下节省约13%以上的能量。

To save the battery and relevant energy-efficient energy of smartphones, a data forecasting-based strategy for smartphone energy-efficiency based on predictability of users＇ accessing data was proposed. The known users were classified into clusters with mixed variable attributes-based improved K-means algorithm, and the similar user group was established. The expectation of user＇s continuous search was predicted by using BG/NBD model. Aiming at the valuable users, combined with collaborative filtering recommendation, the data to be accessed by user was pre-fetched based on historical information of similar users. The data pre-storage mechanism was utilized to prestore the above predicted data in smartphones to achieve the goal of energy saving in the way of reducing the communication times. Compared with other methods, the experimental result showed that the strategy could reduce the energy consumption over 13 % on the condition of meeting user satisfaction.