请使用java语言举一个推荐算法的代码例子

当然可以！以下是一个简单的基于协同过滤的推荐算法示例，使用Java语言：

public class CollaborativeFiltering {

    private double similarityThreshold;

    public CollaborativeFiltering(double similarityThreshold) {
        this.similarityThreshold = similarityThreshold;
    }

    public List<Integer> recommend(List<User> users, int targetUserId) {

        User targetUser = null;

        for (User user : users) {
            if (user.getId() == targetUserId) {
                targetUser = user;
                break;
            }
        }

        if (targetUser == null) {
            throw new IllegalArgumentException("The target user does not exist.");
        }

        List<Integer> recommendedItems = new ArrayList<>();

        for (User user : users) {
            if (user != targetUser) {
                double similarity = computeSimilarity(targetUser, user);

                if (similarity > similarityThreshold) {
                    for (Integer item : user.getRatedItems()) {
                        if (!targetUser.getRatedItems().contains(item)) {
                            recommendedItems.add(item);
                        }
                    }
                }
            }
        }

        return recommendedItems;
    }

    private double computeSimilarity(User user1, User user2) {
        double sumOfProducts = 0.0;
        double sumSquaredUser1 = 0.0;
        double sumSquaredUser2 = 0.0;

        Map<Integer, Double> ratingMapUser1 = user1.getRatingMap();
        Map<Integer, Double> ratingMapUser2 = user2.getRatingMap();

        for (Integer itemId : ratingMapUser1.keySet()) {
            if (ratingMapUser2.containsKey(itemId)) {
                double rating1 = ratingMapUser1.get(itemId);
                double rating2 = ratingMapUser2.get(itemId);
                sumOfProducts += rating1 * rating2;
            }
        }

        for (Double rating : ratingMapUser1.values()) {
            sumSquaredUser1 += Math.pow(rating, 2);
        }

        for (Double rating : ratingMapUser2.values()) {
            sumSquaredUser2 += Math.pow(rating, 2);
        }

        double denominator = Math.sqrt(sumSquaredUser1) * Math.sqrt(sumSquaredUser2);

        return (denominator == 0) ? 0 : sumOfProducts / denominator;
    }

}

class User {

    private int id;
    private Map<Integer, Double> ratingMap;

    public User(int id) {
        this.id = id;
        this.ratingMap = new HashMap<>();
    }

    public User(int id, Map<Integer, Double> ratingMap) {
        this.id = id;
        this.ratingMap = ratingMap;
    }

    public int getId() {
        return id;
    }

    public void setId(int id) {
        this.id = id;
    }

    public Map<Integer, Double> getRatingMap() {
        return ratingMap;
    }

    public void setRatingMap(Map<Integer, Double> ratingMap) {
        this.ratingMap = ratingMap;
    }

    public void addRating(int itemId, double rating) {
        ratingMap.put(itemId, rating);
    }

    public List<Integer> getRatedItems() {
        return new ArrayList<>(ratingMap.keySet());
    }

}

这是一个基于协同过滤的推荐算法，它在整个数据集中搜索与目标用户的评级相似的用户，并推荐目标用户没有评级的项目。该算法使用相似性阈值来过滤出相似用户。