2

以下代码有什么问题，为什么它没有为匿名用户提供建议？
我无法弄清楚出了什么问题，但我无法使用 PlusAnonymousUserDataModel 获得匿名用户的建议。这是示例代码，它不显示对匿名用户的推荐，但为模型中具有完全相同偏好的用户提供推荐：

public static void main(String[] args) throws Exception {
    DataModel model = new GenericBooleanPrefDataModel(
            GenericBooleanPrefDataModel.toDataMap(new FileDataModel(
                    new File(args[0]))));
    PlusAnonymousUserDataModel plusAnonymousModel = new PlusAnonymousUserDataModel(model);

    UserSimilarity similarity = new LogLikelihoodSimilarity(model);
    UserNeighborhood neighborhood =
            new NearestNUserNeighborhood(
                    Integer.parseInt(args[1]), similarity, model);
    //new ThresholdUserNeighborhood(Float.parseFloat(args[1]), similarity, model);


    System.out.println("Neighborhood=" + args[1]);
    System.out.println("");




    Recommender recommender = new GenericBooleanPrefUserBasedRecommender(model,
            neighborhood, similarity);


    PreferenceArray anonymousPrefs =
            new BooleanUserPreferenceArray(12);
    anonymousPrefs.setUserID(0,
            PlusAnonymousUserDataModel.TEMP_USER_ID);
    anonymousPrefs.setItemID(0, 1105L);
    anonymousPrefs.setItemID(1, 1201L);
    anonymousPrefs.setItemID(2, 1301L);
    anonymousPrefs.setItemID(3, 1401L);
    anonymousPrefs.setItemID(4, 1502L);
    anonymousPrefs.setItemID(5, 1602L);
    anonymousPrefs.setItemID(6, 1713L);
    anonymousPrefs.setItemID(7, 1801L);
    anonymousPrefs.setItemID(8, 1901L);
    anonymousPrefs.setItemID(9, 2002L);
    anonymousPrefs.setItemID(10, 9101L);
    anonymousPrefs.setItemID(11, 9301L);

    synchronized(anonymousPrefs){
        plusAnonymousModel.setTempPrefs(anonymousPrefs);
        List<RecommendedItem> recommendations1 = recommender.recommend(PlusAnonymousUserDataModel.TEMP_USER_ID, 20);
        plusAnonymousModel.clearTempPrefs();

        System.out.println("Recm for anonymous:");

        for (RecommendedItem recommendation : recommendations1) {
            System.out.println(recommendation);
        }
        System.out.println("");
    }


    List<RecommendedItem> recommendations = recommender.recommend(
            Integer.parseInt(args[2]), 20);

    System.out.println("Recomedation for user_id="
            + Integer.parseInt(args[2]) + ":");

    for (RecommendedItem recommendation : recommendations) {
        System.out.println(recommendation);
    }
    System.out.println("");

此代码产生的输出如下： Neighborhood=100

匿名推荐：

user_id=1680604 的推荐：RecommendedItem[item:1701, value:24.363672] ... 等等。所以没有对匿名用户的推荐！:(

事实证明，要获得推荐，您必须使用不是“真实的”（在我的情况下是基于文件的）持久性 DataModel 模型，而是使用 PlusAnonymousUserDataModel plusAnonymousModel 来构建相似性、邻域和推荐器！
因此，关于 Mahout 的基本文档（https://builds.apache.org/job/Mahout-Quality/javadoc/org/apache/mahout/cf/taste/impl/model/PlusAnonymousUserDataModel.html）是错误的ItemSimilarity similarity = new LogLikelihoodSimilarity(realModel); // not plusModel

早些时候，SO 上的其他人也遇到了同样的问题，在这里没有得到任何答案：Model creation for User User collaborative filtering 所以我想我应该去那里回答他。Sean Owen，感谢您的关注，您能确认我找到的解决方案是正确的吗？

4

0 回答 0