源码在CSDN官方下载地址：

https://download.csdn.net/download/qq_31293575/14813447

针对推荐算法未考虑大数据量计算导致系统性能差及基于历史评分的相似性不能反映用户兴趣动态变化的问题，提出了满足用户兴趣漂移的计算自适应快速推荐算法。该算法依据CPU等计算资源使用率动态调整待推荐用户窗口，并按项目类别及其访问热度动态分配计算时间，计算自适应项目与目标用户的优先级和相似性计算难易度，提高计算效率与响应速度；建立访问次数随时间变化的兴

package com.springmvc.controller;

import javax.servlet.http.HttpServletRequest;

import org.springframework.stereotype.Controller;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.servlet.ModelAndView;

@Controller
public class IndexController {
	 @RequestMapping("/")//首页
	    public ModelAndView showTest(HttpServletRequest request){
	        ModelAndView mav = new ModelAndView();
	        mav.setViewName("index");
	        return mav;
	    }
	 @RequestMapping("/index")//index首页
	    public ModelAndView index(HttpServletRequest request){
	        ModelAndView mav = new ModelAndView();
	        mav.setViewName("index");
	        return mav;
	    }
	 @RequestMapping("/dataDown")//数据集下载
	    public ModelAndView datasetDown(HttpServletRequest request){
	        ModelAndView mav = new ModelAndView();
	        mav.setViewName("dataDown");
	        return mav;
	    }
	 @RequestMapping("/dataUpload")//数据集上传
	    public ModelAndView datasetUpload(HttpServletRequest request){
	        ModelAndView mav = new ModelAndView();
	        mav.setViewName("dataUpload");
	        return mav;
	    }
	 @RequestMapping("/reference")//参考文献
	    public ModelAndView referenceIndex(HttpServletRequest request){
	        ModelAndView mav = new ModelAndView();
	        mav.setViewName("reference");
	        return mav;
	    }

}

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