结合 Mahout

pom.xml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>

编写mapper查询方法

@Mapper
public interface UseroperationnewMapper extends BaseMapper<Useroperationnew>{
    @Select("SELECT user_id,new_id,SUM( CASE operation_type WHEN 0 THEN 3 WHEN 1 THEN 3 WHEN 2 THEN 5 ELSE 0 END ) AS value FROM useroperationnew GROUP BY user_id,new_id ORDER BY user_id")
    public List<Useroperationnew> getAllUserPreference();
}

调用协同过滤算法

public List<Long> recommend( Integer userId) throws TasteException {
        List<UserArticleOperation> userList = userArticleOperationMapper.getAllUserPreference();
        //创建数据模型
        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<UserArticleOperation> userArticleOperations) {
        FastByIDMap<PreferenceArray> fastByIdMap = new FastByIDMap<>();
        Map<Integer, List<UserArticleOperation>> map = userArticleOperations.stream().collect(Collectors.groupingBy(UserArticleOperation::getUserId));
        Collection<List<UserArticleOperation>> list = map.values();
        for(List<UserArticleOperation> userPreferences : list){
            GenericPreference[] array = new GenericPreference[userPreferences.size()];
            for(int i = 0; i < userPreferences.size(); i++){
                UserArticleOperation userPreference = userPreferences.get(i);
                GenericPreference item = new GenericPreference(userPreference.getUserId(), userPreference.getArticleId(), userPreference.getValue());
                array[i] = item;
            }
            fastByIdMap.put(array[0].getUserID(), new GenericUserPreferenceArray(Arrays.asList(array)));
        }
        return new GenericDataModel(fastByIdMap);
    }