推荐系统——基于用户的协同过滤算法

1.RelateDTO

package com.example.entity;

/**
 * 协同过滤算法
 */
public class RelateDTO {
    /** 用户id */
    private Integer useId;
    /** 商品id */
    private Integer goodsId;
    /** 指数 */
    private Integer index;

    public RelateDTO(Integer useId, Integer goodsId, Integer index) {
        this.useId = useId;
        this.goodsId = goodsId;
        this.index = index;
    }

    public Integer getUseId() {
        return useId;
    }

    public void setUseId(Integer useId) {
        this.useId = useId;
    }

    public Integer getGoodsId() {
        return goodsId;
    }

    public void setGoodsId(Integer goodsId) {
        this.goodsId = goodsId;
    }

    public Integer getIndex() {
        return index;
    }

    public void setIndex(Integer index) {
        this.index = index;
    }
}

2.CoreMath

package com.example.utils.recommend;

import cn.hutool.core.collection.CollectionUtil;
import com.example.entity.RelateDTO;

import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.TreeMap;
import java.util.stream.IntStream;

/**
 * 基于用户的协同过滤的核心算法
 */
public class CoreMath {


    /**
     * 计算相关系数并排序
     */
    public static Map<Integer, Double> computeNeighbor(Integer key, Map<Integer, List<RelateDTO>> map, int type) {
        Map<Integer, Double> distMap = new TreeMap<>();
        List<RelateDTO> userItems = map.get(key);
        if (CollectionUtil.isNotEmpty(userItems)) {
            map.forEach((k, v) -> {
                //排除此用户
                if (!k.equals(key)) {
                    //关系系数
                    double coefficient = relateDist(v, userItems, type);
                    //关系距离
                    double distance = Math.abs(coefficient);
                    distMap.put(k, distance);
                }
            });
        }
        return distMap;
    }


    /**
     * 

原文地址：https://blog.csdn.net/weixin_59798969/article/details/135711637

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