mahout实现协同过滤推荐

引入依赖

<!--引入推荐引擎mahout，注意要先全部引入，再使用exclusion标签-->

<dependency>
  <groupId>org.apache.mahout</groupId>
  <artifactId>mahout-mr</artifactId>
  <version>0.12.2</version>
  <exclusions>
    <exclusion>
      <groupId>javax.servlet</groupId>
      <artifactId>servlet-api</artifactId>
    </exclusion>
    <exclusion>
      <groupId>org.slf4j</groupId>
      <artifactId>slf4j-api</artifactId>
    </exclusion>
    <exclusion>
      <groupId>org.slf4j</groupId>
      <artifactId>slf4j-jcl</artifactId>
    </exclusion>
    <exclusion>
      <groupId>org.apache.lucene</groupId>
      <artifactId>lucene-core</artifactId>
    </exclusion>
    <exclusion>
      <groupId>org.apache.lucene</groupId>
      <artifactId>lucene-analyzers-common</artifactId>
    </exclusion>
    <exclusion>
      <groupId>log4j</groupId>
      <artifactId>log4j</artifactId>
    </exclusion>
    <exclusion>
      <groupId>org.slf4j</groupId>
      <artifactId>slf4j-log4j12</artifactId>
    </exclusion>
    <exclusion>
      <artifactId>jersey-client</artifactId>
      <groupId>com.sun.jersey</groupId>
    </exclusion>
    <exclusion>
      <artifactId>jersey-core</artifactId>
      <groupId>com.sun.jersey</groupId>
    </exclusion>
    <exclusion>
      <artifactId>jersey-apache-client4</artifactId>
      <groupId>com.sun.jersey.contribs</groupId>
    </exclusion>
  </exclusions>
</dependency>

创建对应的实体类

public class UserItemOperation implements Serializable  {

    private static final long serialVersionUID = 1L;


    private Long userId;

    private Long itemId;

    private Integer value;

}

创建对应的Xml

<mapper namespace="com.dao.UserItemDao">

    <select id="getAllUserPreference" resultType="com.entity.UserItemOperation">
        SELECT
            a.id userid,
            c.id itemId,
            count( a.id ) `value`
        FROM
            yonghu a
                JOIN gangweishenqing b ON a.yonghuming = b.yonghuming join zhaopinxinxi c on
                b.zhaopingangwei = c.zhaopingangwei
        GROUP BY
            a.id,
            b.qiyezhanghao
    </select>
</mapper>

基于用户的推荐算法代码

@Service
public class RecommendUtil {


    @Autowired
    private UserItemDao userItemDao;

    public List<Long> recommend(Long userId) throws TasteException {
        List<UserItemOperation> userList = userItemDao.getAllUserPreference();
        //创建数据模型
        System.out.println(userList);
        DataModel dataModel = this.createDataModel(userList);
        //获取用户相似程度
        UserSimilarity similarity = new UncenteredCosineSimilarity(dataModel);
        //获取用户邻居
        UserNeighborhood userNeighborhood = new NearestNUserNeighborhood(2, similarity, dataModel);
        //构建推荐器
        Recommender recommender = new GenericUserBasedRecommender(dataModel, userNeighborhood, similarity);
        //推荐2个
        List<RecommendedItem> recommendedItems = recommender.recommend(userId, 5);
        List<Long> itemIds = recommendedItems.stream().map(RecommendedItem::getItemID).collect(Collectors.toList());
        return itemIds;
    }
    private DataModel createDataModel(List<UserItemOperation> userItemOperations) {
        FastByIDMap<PreferenceArray> fastByIdMap = new FastByIDMap<>();
        Map<Long, List<UserItemOperation>> map = userItemOperations.stream().collect(Collectors.groupingBy(UserItemOperation::getUserId));
        Collection<List<UserItemOperation>> list = map.values();
        for(List<UserItemOperation> userPreferences : list){
            GenericPreference[] array = new GenericPreference[userPreferences.size()];
            for(int i = 0; i < userPreferences.size(); i++){
                UserItemOperation userPreference = userPreferences.get(i);
                System.out.println(userPreference);
                GenericPreference item = new GenericPreference(userPreference.getUserId(), userPreference.getItemId(), userPreference.getValue());
                array[i] = item;
            }
            fastByIdMap.put(array[0].getUserID(), new GenericUserPreferenceArray(Arrays.asList(array)));
        }
        return new GenericDataModel(fastByIdMap);
    }
}

基于物品的推荐算法

@Service
public class RecommendUtil {

    @Autowired
    private UserItemDao userItemDao;

    public List<Long> recommend(Long userId) throws TasteException {
        List<UserItemOperation> userList = userItemDao.getAllUserPreference();
        // 创建数据模型
        DataModel dataModel = this.createDataModel(userList);
        // 获取物品相似程度
        ItemSimilarity similarity = new UncenteredCosineSimilarity(dataModel);
        // 构建推荐器
        Recommender recommender = new GenericItemBasedRecommender(dataModel, similarity);
        // 推荐5个
        List<RecommendedItem> recommendedItems = recommender.recommend(userId, 5);
        List<Long> itemIds = recommendedItems.stream().map(RecommendedItem::getItemID).collect(Collectors.toList());
        return itemIds;
    }

    private DataModel createDataModel(List<UserItemOperation> userItemOperations) {
        FastByIDMap<PreferenceArray> fastByIdMap = new FastByIDMap<>();
        Map<Long, List<UserItemOperation>> map = userItemOperations.stream().collect(Collectors.groupingBy(UserItemOperation::getUserId));
        Collection<List<UserItemOperation>> list = map.values();
        for (List<UserItemOperation> userPreferences : list) {
            GenericPreference[] array = new GenericPreference[userPreferences.size()];
            for (int i = 0; i < userPreferences.size(); i++) {
                UserItemOperation userPreference = userPreferences.get(i);
                GenericPreference item = new GenericPreference(userPreference.getUserId(), userPreference.getItemId(), userPreference.getValue());
                array[i] = item;
            }
            fastByIdMap.put(array[0].getUserID(), new GenericUserPreferenceArray(Arrays.asList(array)));
        }
        return new GenericDataModel(fastByIdMap);
    }
}