【计算机毕业设计】基于大数据的个性化音乐推荐系统

协同过滤推荐算法是诞生最早，并且较为著名的推荐算法。主要的功能是预测和推荐。算法通过对用户历史行为数据的挖掘发现用户的偏好，基于不同的偏好对用户进行群组划分并推荐品味相似的商品。协同过滤推荐算法分为两类，分别是基于用户的协同过滤算法(user-based collaboratIve filtering)，和基于物品的协同过滤算法(item-based collaborative filtering)。简单的说就是：人以类聚，物以群分。
1、基于用户的协同过滤 user-based collaboratIve filtering
基于用户的协同过滤算法是通过用户的历史行为数据发现用户对商品或内容的喜欢(如商品购买，收藏，内容评论或分享)，并对这些喜好进行度量和打分。根据不同用户对相同商品或内容的态度和偏好程度计算用户之间的关系。在有相同喜好的用户间进行商品推荐。
2、基于物品的协同过滤算法与基于用户的协同过滤算法很像，将商品和用户互换。通过计算不同用户对不同物品的评分获得物品间的关系。基于物品间的关系对用户进行相似物品的推荐。这里的评分代表用户对商品的态度和偏好。简单来说就是如果用户A同时购买了商品1和商品2，那么说明商品1和商品2的相关度较高。当用户B也购买了商品1时，可以推断他也有购买商品2的需求。

package com.module.controller;


import com.module.mapper.ArticleMapper;
import com.module.mapper.CommonMapper;
import com.module.mapper.ScoreinfoMapper;
import com.module.pojo.Article;
import com.module.pojo.Scoreinfo;
import com.module.pojo.Userinfo;
import com.module.util.MyFilleUtil;
import com.module.util.ResultUtil;
import org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator;
import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
import org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood;
import org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommender;
import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.EuclideanDistanceSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.cf.taste.similarity.ItemSimilarity;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Controller;
import org.springframework.ui.Model;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.ResponseBody;

import javax.servlet.http.HttpServletRequest;
import javax.servlet.http.HttpSession;
import java.io.File;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;

@Controller
public class AlgorithmController {

    public static int ALGORITHM_TYPE = 1;//临近的用户个数
    final static int NEIGHBORHOOD_NUM = 2;//临近的用户个数
    final static int RECOMMENDER_NUM = 9;//推荐物品的最大个数

    @Autowired   //注入对象
    public ScoreinfoMapper scoreinfoMapper;
    @Autowired
    ArticleMapper articleMapper;

    @Autowired   //注入对象
    public CommonMapper commonMapper;

    //公共查询方法
    @ResponseBody
    @RequestMapping("/pc/selectAction")
    public ResultUtil selectAction(String sql) {
        List<Map> mapList = commonMapper.selectAction(sql);
        return ResultUtil.ok(mapList);
    }

    //公共修改方法
    @ResponseBody
    @RequestMapping("/pc/updateAction")
    public ResultUtil updateAction(String sql) {
        commonMapper.updateAction(sql);
        return ResultUtil.ok("操作成功");
    }


    @RequestMapping("/")
    public String echartsB22() {
        return "redirect:pc/index";
    }


    @RequestMapping("/pc/algorithmList")
    public String algorithmList(HttpSession session, HttpServletRequest request, Model model) throws Exception {
        String filePath = request.getSession().getServletContext().getRealPath("/static") + File.separatorChar + "data.csv";
        System.out.println("filePath = " + filePath);
        Userinfo user = (Userinfo) session.getAttribute("loginUserinfo");
        int myid = user.getId();
        List<Scoreinfo> scoreinfoList = scoreinfoMapper.selectByMap(null);
        String content = "";
        for (Scoreinfo scoreinfo : scoreinfoList) {
            content = content + scoreinfo.getUid() + "," + scoreinfo.getTid() + "," + scoreinfo.getScore() + "\n";
        }
        MyFilleUtil.WriteFile(filePath, content);

        //List arr = userCF(filePath,myid);// 基于用户的
        List<Integer> arr = new ArrayList<Integer>();
        if (ALGORITHM_TYPE == 1) {
            arr = itemCF(filePath, myid);// 基于物品的
        } else {
            arr = userCF(filePath, myid);// 基于用户的
        }
        List<Article> datainfoList = new ArrayList<Article>();
        for (Integer tid : arr) {
            Article datainfo = articleMapper.selectArticleById(tid);
            if (null != datainfo) {
                datainfoList.add(datainfo);
            }
        }
        model.addAttribute("datainfoList", datainfoList);
        return "pc/algorithm";
    }

    @ResponseBody
    @RequestMapping("/pc/algorithmSave")
    public ResultUtil algorithmSave(HttpSession session, Scoreinfo scoreinfo, Model model) throws Exception {
        scoreinfoMapper.insert(scoreinfo);
        return ResultUtil.ok("操作成功");
    }

    //基于用户的 CF（User CF） 推荐算法
    public static List<Integer> userCF(String filePath, int myid) throws Exception {
        List<Integer> arrList = new ArrayList<Integer>();
        DataModel model = new FileDataModel(new File(filePath));//数据模型
        UserSimilarity user = new EuclideanDistanceSimilarity(model);//用户相识度算法
        NearestNUserNeighborhood neighbor = new NearestNUserNeighborhood(NEIGHBORHOOD_NUM, user, model);
        //用户近邻算法
        Recommender r = new GenericUserBasedRecommender(model, neighbor, user);//用户推荐算法
        LongPrimitiveIterator iter = model.getUserIDs();///得到用户ID

        while (iter.hasNext()) {
            long uid = iter.nextLong();
            List<RecommendedItem> list = r.recommend(uid, RECOMMENDER_NUM);
            System.out.printf("uid:%s", uid);
            for (RecommendedItem ritem : list) {
                System.out.printf("(%s,%f)", ritem.getItemID(), ritem.getValue());
                if (myid == uid) {
                    arrList.add((int) ritem.getItemID());
                }
            }
            System.out.println();

        }
        return arrList;
    }

    //基于物品的 CF 的基本原理 推荐算法
    public static List<Integer> itemCF(String filePath, int myid) throws Exception {
        List<Integer> arrList = new ArrayList<Integer>();
        DataModel model = new FileDataModel(new File(filePath));//数据模型
        ItemSimilarity item = new EuclideanDistanceSimilarity(model);//用户相识度算法
        Recommender r = new GenericItemBasedRecommender(model, item);//物品推荐算法
        LongPrimitiveIterator iter = model.getUserIDs();
        while (iter.hasNext()) {
            long uid = iter.nextLong();
            List<RecommendedItem> list = r.recommend(uid, RECOMMENDER_NUM);
            System.out.printf("uid:%s", uid);
            for (RecommendedItem ritem : list) {
                System.out.printf("(%s,%f)", ritem.getItemID(), ritem.getValue());
                if (myid == uid) {
                    arrList.add((int) ritem.getItemID());
                }
            }
            System.out.println();
        }
        return arrList;
    }


}