推荐算法slope one之java实现

import java.util.*;/** * Daniel Lemire A simple implementation of the weighted slope one algorithm in * Java for item-based collaborative filtering. Assumes Java 1.5. *  * See main function for example. *  * June 1st 2006. Revised by Marco Ponzi on March 29th 2007 */public class SlopeOne {	public static void main(String args[]) {		// this is my data base		Map<UserId, Map<ItemId, Float>> data = new HashMap<UserId, Map<ItemId, Float>>();		// items		ItemId item1 = new ItemId("       candy");		ItemId item2 = new ItemId("         dog");		ItemId item3 = new ItemId("         cat");		ItemId item4 = new ItemId("         war");		ItemId item5 = new ItemId("strange food");		mAllItems = new ItemId[] { item1, item2, item3, item4, item5 };		// I'm going to fill it in		HashMap<ItemId, Float> user1 = new HashMap<ItemId, Float>();		HashMap<ItemId, Float> user2 = new HashMap<ItemId, Float>();		HashMap<ItemId, Float> user3 = new HashMap<ItemId, Float>();		HashMap<ItemId, Float> user4 = new HashMap<ItemId, Float>();		user1.put(item1, 1.0f);		user1.put(item2, 0.5f);		user1.put(item4, 0.1f);		data.put(new UserId("Bob"), user1);		user2.put(item1, 1.0f);		user2.put(item3, 0.5f);		user2.put(item4, 0.2f);		data.put(new UserId("Jane"), user2);		user3.put(item1, 0.9f);		user3.put(item2, 0.4f);		user3.put(item3, 0.5f);		user3.put(item4, 0.1f);		data.put(new UserId("Jo"), user3);		user4.put(item1, 0.1f);		// user4.put(item2,0.4f);		// user4.put(item3,0.5f);		user4.put(item4, 1.0f);		user4.put(item5, 0.4f);		data.put(new UserId("StrangeJo"), user4);		// next, I create my PRedictor engine		SlopeOne so = new SlopeOne(data);		System.out.println("Here's the data I have accumulated...");		so.printData();		// then, I'm going to test it out...		HashMap<ItemId, Float> user = new HashMap<ItemId, Float>();		System.out.println("Ok, now we predict...");		user.put(item5, 0.4f);		System.out.println("Inputting...");		SlopeOne.print(user);		System.out.println("Getting...");		SlopeOne.print(so.predict(user));		//		user.put(item4, 0.2f);		System.out.println("Inputting...");		SlopeOne.print(user);		System.out.println("Getting...");		SlopeOne.print(so.predict(user));	}	Map<UserId, Map<ItemId, Float>> mData;	Map<ItemId, Map<ItemId, Float>> mDiffMatrix;	Map<ItemId, Map<ItemId, Integer>> mFreqMatrix;	static ItemId[] mAllItems;	public SlopeOne(Map<UserId, Map<ItemId, Float>> data) {		mData = data;		buildDiffMatrix();	}	/**	 * Based on existing data, and using weights, try to predict all missing	 * ratings. The trick to make this more scalable is to consider only	 * mDiffMatrix entries having a large (>1) mFreqMatrix entry.	 * 	 * It will output the prediction 0 when no prediction is possible.	 */	public Map<ItemId, Float> predict(Map<ItemId, Float> user) {		HashMap<ItemId, Float> predictions = new HashMap<ItemId, Float>();		HashMap<ItemId, Integer> frequencies = new HashMap<ItemId, Integer>();		for (ItemId j : mDiffMatrix.keySet()) {			frequencies.put(j, 0);			predictions.put(j, 0.0f);		}		for (ItemId j : user.keySet()) {			for (ItemId k : mDiffMatrix.keySet()) {				try {					float newval = (mDiffMatrix.get(k).get(j).floatValue() + user							.get(j).floatValue())							* mFreqMatrix.get(k).get(j).intValue();					predictions.put(k, predictions.get(k) + newval);					frequencies.put(k, frequencies.get(k)							+ mFreqMatrix.get(k).get(j).intValue());				} catch (NullPointerException e) {				}			}		}		HashMap<ItemId, Float> cleanpredictions = new HashMap<ItemId, Float>();		for (ItemId j : predictions.keySet()) {			if (frequencies.get(j) > 0) {				cleanpredictions.put(j, predictions.get(j).floatValue()						/ frequencies.get(j).intValue());			}		}		for (ItemId j : user.keySet()) {			cleanpredictions.put(j, user.get(j));		}		return cleanpredictions;	}	/**	 * Based on existing data, and not using weights, try to predict all missing	 * ratings. The trick to make this more scalable is to consider only	 * mDiffMatrix entries having a large (>1) mFreqMatrix entry.	 */	public Map<ItemId, Float> weightlesspredict(Map<ItemId, Float> user) {		HashMap<ItemId, Float> predictions = new HashMap<ItemId, Float>();		HashMap<ItemId, Integer> frequencies = new HashMap<ItemId, Integer>();		for (ItemId j : mDiffMatrix.keySet()) {			predictions.put(j, 0.0f);			frequencies.put(j, 0);		}		for (ItemId j : user.keySet()) {			for (ItemId k : mDiffMatrix.keySet()) {				// System.out.println("Average diff between "+j+" and "+ k +				// " is "+mDiffMatrix.get(k).get(j).floatValue()+" with n = "+mFreqMatrix.get(k).get(j).floatValue());				float newval = (mDiffMatrix.get(k).get(j).floatValue() + user						.get(j).floatValue());				predictions.put(k, predictions.get(k) + newval);			}		}		for (ItemId j : predictions.keySet()) {			predictions.put(j, predictions.get(j).floatValue() / user.size());		}		for (ItemId j : user.keySet()) {			predictions.put(j, user.get(j));		}		return predictions;	}	public void printData() {		for (UserId user : mData.keySet()) {			System.out.println(user);			print(mData.get(user));		}		for (int i = 0; i < mAllItems.length; i++) {			System.out.print("/n" + mAllItems[i] + ":");			printMatrixes(mDiffMatrix.get(mAllItems[i]),					mFreqMatrix.get(mAllItems[i]));		}	}	private void printMatrixes(Map<ItemId, Float> ratings,			Map<ItemId, Integer> frequencies) {		for (int j = 0; j < mAllItems.length; j++) {			System.out.format("%10.3f", ratings.get(mAllItems[j]));			System.out.print(" ");			System.out.format("%10d", frequencies.get(mAllItems[j]));		}		System.out.println();	}	public static void print(Map<ItemId, Float> user) {		for (ItemId j : user.keySet()) {			System.out.println(" " + j + " --> " + user.get(j).floatValue());		}	}	public void buildDiffMatrix() {		mDiffMatrix = new HashMap<ItemId, Map<ItemId, Float>>();		mFreqMatrix = new HashMap<ItemId, Map<ItemId, Integer>>();		// first iterate through users		for (Map<ItemId, Float> user : mData.values()) {			// then iterate through user data			for (Map.Entry<ItemId, Float> entry : user.entrySet()) {				if (!mDiffMatrix.containsKey(entry.getKey())) {					mDiffMatrix.put(entry.getKey(),							new HashMap<ItemId, Float>());					mFreqMatrix.put(entry.getKey(),							new HashMap<ItemId, Integer>());				}				for (Map.Entry<ItemId, Float> entry2 : user.entrySet()) {					int oldcount = 0;					if (mFreqMatrix.get(entry.getKey()).containsKey(							entry2.getKey()))						oldcount = mFreqMatrix.get(entry.getKey())								.get(entry2.getKey()).intValue();					float olddiff = 0.0f;					if (mDiffMatrix.get(entry.getKey()).containsKey(							entry2.getKey()))						olddiff = mDiffMatrix.get(entry.getKey())								.get(entry2.getKey()).floatValue();					float observeddiff = entry.getValue() - entry2.getValue();					mFreqMatrix.get(entry.getKey()).put(entry2.getKey(),							oldcount + 1);					mDiffMatrix.get(entry.getKey()).put(entry2.getKey(),							olddiff + observeddiff);				}			}		}		for (ItemId j : mDiffMatrix.keySet()) {			for (ItemId i : mDiffMatrix.get(j).keySet()) {				float oldvalue = mDiffMatrix.get(j).get(i).floatValue();				int count = mFreqMatrix.get(j).get(i).intValue();				mDiffMatrix.get(j).put(i, oldvalue / count);			}		}	}}class UserId {	String content;	public UserId(String s) {		content = s;	}	public int hashCode() {		return content.hashCode();	}	public String toString() {		return content;	}}class ItemId {	String content;	public ItemId(String s) {		content = s;	}	public int hashCode() {		return content.hashCode();	}	public String toString() {		return content;	}}