根據使用者協同過濾，根據使用者的相似度，推薦相應的item。

pom.xml加入核心的幾個依賴

<!-- https://mvnrepository.com/artifact/org.apache.mahout/mahout-core -->
        <dependency>
            <groupId>org.apache.mahout</groupId>
            <artifactId>mahout-core</artifactId>
            <version>0.9</version>
        </dependency>
        <dependency>
            <groupId>org.apache.mahout</groupId>
            <artifactId>mahout-integration</artifactId>
            <version>0.9</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.slf4j/slf4j-simple -->
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-simple</artifactId>
            <version>1.7.25</version>
        </dependency>
 

java程式碼：

    /**
     *
     * @param userId  使用者id
     * @param n   返回推薦條數
     * @return  陣列，kl_id
     * @throws TasteException
     * @throws ClassNotFoundException
     */
    public int[] recommendByUser(Integer userId, int n) throws TasteException, ClassNotFoundException {

        Class.forName("com.mysql.jdbc.Driver");
        MysqlDataSource dataSource = new MysqlDataSource();
        dataSource.setServerName("");//本地為localhost
        dataSource.setUser("root");
        dataSource.setPassword("mysql");
        dataSource.setDatabaseName("knowledgeManagement");//資料庫名
        /*
        preferenceTable:表名
        userIDColumn:userId的欄位名
        itemIDColumn:itemId的欄位名
        preferenceColumn：偏好值欄位名
        timestampColumn：時間記錄欄位//可為空
         */
        JDBCDataModel dataModel = new MySQLJDBCDataModel(dataSource , "kl_rating_comment" , "user_id" , "kl_id","kl_rating","kl_comment_time");

        //獲取模型
        DataModel model = dataModel;
        //計算相似度
        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
        //計算閾值,選擇鄰近的2個使用者
        UserNeighborhood neighborhood = new NearestNUserNeighborhood(2 ,similarity,model);
        //推薦集合
        Recommender recommender = new GenericUserBasedRecommender(model,neighborhood,similarity);
        //推薦數量 為n的一個合集,這裡數量可以修改
        List<RecommendedItem> recommendedItems = recommender.recommend(userId,n);
        int kl_idArray[] = new int[recommendedItems.size()];
        for (int i=0;i<recommendedItems.size();i++){
            kl_idArray[i] = (int) recommendedItems.get(i).getItemID();
        }
        //下面是測試用的程式碼
        for (RecommendedItem recommendation : recommendedItems) {
            System.out.println(recommendation);
        }
        System.out.println("-------------");

        for (int i= 0;i<kl_idArray.length;i++){
            System.out.println(kl_idArray[i]);
        }
        return kl_idArray;

    }