slope-one java实现

Slope-One算法是一种基于内存的协同过滤算法，用于推荐系统。它通过计算物品之间的平均差异来预测用户对物品的评分。以下是Slope-One算法的Java实现示例：

import java.util.HashMap;
import java.util.Map;

public class SlopeOne {

    private Map<String, Map<String, Double>> data;
    private Map<String, Map<String, Integer>> freq;

    public SlopeOne() {
        data = new HashMap<String, Map<String, Double>>();
        freq = new HashMap<String, Map<String, Integer>>();
    }

    public void addData(Map<String, Map<String, Double>> data) {
        for (String user : data.keySet()) {
            Map<String, Double> ratings = data.get(user);
            for (String item : ratings.keySet()) {
                addRating(user, item, ratings.get(item));
            }
        }
    }

    public void addRating(String user, String item, Double rating) {
        if (!data.containsKey(user)) {
            data.put(user, new HashMap<String, Double>());
            freq.put(user, new HashMap<String, Integer>());
        }
        Map<String, Double> ratings = data.get(user);
        Map<String, Integer> frequencies = freq.get(user);
        if (!ratings.containsKey(item)) {
            ratings.put(item, 0.0);
            frequencies.put(item, 0);
        }
        ratings.put(item, ratings.get(item) + rating);
        frequencies.put(item, frequencies.get(item) + 1);
    }

    public Map<String, Double> predict(Map<String, Double> userRatings) {
        Map<String, Double> predictions = new HashMap<String, Double>();
        Map<String, Integer> frequencies = new HashMap<String, Integer>();
        for (String item : userRatings.keySet()) {
            for (String diffItem : data.keySet()) {
                if (!userRatings.containsKey(diffItem) && data.get(diffItem).containsKey(item)) {
                    double oldCount = frequencies.containsKey(diffItem) ? frequencies.get(diffItem) : 0.0;
                    double oldPrediction = predictions.containsKey(diffItem) ? predictions.get(diffItem) : 0.0;
                    double frequency = freq.get(diffItem).get(item);
                    double diff = data.get(diffItem).get(item);
                    frequencies.put(diffItem, (int) (oldCount + frequency));
                    predictions.put(diffItem, oldPrediction + frequency * (userRatings.get(item) + diff));
                }
            }
        }
        for (String item : predictions.keySet()) {
            double frequency = frequencies.get(item);
            double prediction = predictions.get(item);
            predictions.put(item, prediction / frequency);
        }
        return predictions;
    }
}

这里我们使用了Java的Map数据结构来存储物品之间的评分和频率信息。在addData方法中，我们将整个数据集添加到模型中。在addRating方法中，我们将用户对物品的评分添加到模型中。在predict方法中，我们使用用户已有的评分来预测用户对未评分物品的评分。