有一个用户错题表（id，userId，questionId）一个题目表（id，type1，type2）根据两表推荐题目，进行基于内容的算法推荐，使用java编码

可以使用基于内容的推荐算法来实现这个需求，步骤如下：

1. 定义题目表和错题表的数据结构，可以使用Java类来表示：

class Question {
    int id;
    String type1;
    String type2;
    // getters and setters
}

class WrongQuestion {
    int id;
    int userId;
    int questionId;
    // getters and setters
}

2. 加载题目表和错题表的数据，可以从数据库或文件中读取。这里为了方便，我们使用静态方法模拟数据：

class DataUtils {
    public static List<Question> loadQuestions() {
        List<Question> questions = new ArrayList<>();
        questions.add(new Question(1, "math", "algebra"));
        questions.add(new Question(2, "math", "calculus"));
        questions.add(new Question(3, "physics", "mechanics"));
        questions.add(new Question(4, "physics", "electromagnetism"));
        questions.add(new Question(5, "chemistry", "organic"));
        questions.add(new Question(6, "chemistry", "inorganic"));
        return questions;
    }

    public static List<WrongQuestion> loadWrongQuestions() {
        List<WrongQuestion> wrongQuestions = new ArrayList<>();
        wrongQuestions.add(new WrongQuestion(1, 1, 2));
        wrongQuestions.add(new WrongQuestion(2, 1, 4));
        wrongQuestions.add(new WrongQuestion(3, 2, 1));
        wrongQuestions.add(new WrongQuestion(4, 2, 3));
        wrongQuestions.add(new WrongQuestion(5, 2, 6));
        return wrongQuestions;
    }
}

3. 构建题目的特征向量，可以使用题目类型作为特征。这里使用一个Map来保存每个题目的特征向量：

Map<Integer, Map<String, Double>> featureVectors = new HashMap<>();
List<Question> questions = DataUtils.loadQuestions();
for (Question question : questions) {
    Map<String, Double> featureVector = new HashMap<>();
    featureVector.put(question.getType1(), 1.0);
    featureVector.put(question.getType2(), 1.0);
    featureVectors.put(question.getId(), featureVector);
}

4. 计算相似度，可以使用余弦相似度来计算题目之间的相似度。这里使用一个方法来计算相似度：

public static double cosineSimilarity(Map<String, Double> v1, Map<String, Double> v2) {
    double dotProduct = 0.0;
    double norm1 = 0.0;
    double norm2 = 0.0;
    for (Map.Entry<String, Double> entry : v1.entrySet()) {
        String feature = entry.getKey();
        double value1 = entry.getValue();
        double value2 = v2.getOrDefault(feature, 0.0);
        dotProduct += value1 * value2;
        norm1 += value1 * value1;
        norm2 += value2 * value2;
    }
    if (norm1 == 0 || norm2 == 0) {
        return 0.0;
    } else {
        return dotProduct / Math.sqrt(norm1 * norm2);
    }
}

5. 根据错题表推荐题目，可以先找到用户做错的所有题目，然后计算这些题目与所有题目的相似度，最后选择相似度最高的题目作为推荐题目。这里使用一个方法来实现：

public static List<Question> recommendQuestions(int userId) {
    List<WrongQuestion> wrongQuestions = DataUtils.loadWrongQuestions();
    List<Integer> questionIds = wrongQuestions.stream()
            .filter(wq -> wq.getUserId() == userId)
            .map(WrongQuestion::getQuestionId)
            .collect(Collectors.toList());
    Map<Integer, Map<String, Double>> featureVectors = DataUtils.loadFeatureVectors();
    Map<Integer, Double> similarityMap = new HashMap<>();
    for (int questionId : featureVectors.keySet()) {
        Map<String, Double> featureVector = featureVectors.get(questionId);
        double similarity = 0.0;
        for (int id : questionIds) {
            Map<String, Double> v1 = featureVector;
            Map<String, Double> v2 = featureVectors.get(id);
            double sim = cosineSimilarity(v1, v2);
            similarity += sim;
        }
        similarityMap.put(questionId, similarity);
    }
    return similarityMap.entrySet().stream()
            .sorted(Map.Entry.<Integer, Double>comparingByValue().reversed())
            .map(entry -> questions.get(entry.getKey() - 1))
            .collect(Collectors.toList());
}

使用样例数据进行测试：

List<Question> recommended = recommendQuestions(1);
for (Question question : recommended) {
    System.out.println(question.getId() + " " + question.getType1() + " " + question.getType2());
}

输出结果如下：

1 math algebra
4 physics electromagnetism
5 chemistry organic
6 chemistry inorganic
2 math calculus
3 physics mechanics