基於用戶評分Kmeans聚類的協同過濾推薦算法實現

基於用戶評分Kmeans聚類的協同過濾推薦算法實現

一：基於用戶評分Kmeans聚類的協同過濾推薦算法實現步驟

1、構建用戶-電影評分矩陣：

public Object readFile(String fileName){

List<string> user = new ArrayList<string>();/<string>/<string>

double[][] weight = new double[user_num][keyword_num];

List<object> obj = new ArrayList<object>();/<object>/<object>

try {

File file = getFile(fileName);

FileReader fr = new FileReader(file);

BufferedReader br = new BufferedReader(fr);

String line = "";

while (br.ready()) {

line = br.readLine();

String[] data = line.split(" ");

String[] str = data[1].split(";");

user.add(data[0]);

for (int i = 0; i < str.length; i++) {

String[] s = str[i].split(":");

weight[Integer.parseInt(data[0])-1][Integer.parseInt(s[0])-1] = Double.parseDouble(s[1]);

}

}

obj.add(user);

obj.add(weight);

br.close();

} catch (Exception e) {

e.printStackTrace();

}

return obj;

}

2、根據用戶評分聚類：

public class GenerateGroup

private List<user> initPlayers;//初始化，一個隨機聚類中心/<user>

private List<user> players;//每個用戶實體類/<user>

public static List<user> clusterHeart;/<user>

public GenerateGroup(List<user> list) {/<user>

players = list;

initPlayers = new ArrayList<user>();/<user>

clusterHeart = new ArrayList<user>();/<user>

for (int i = 0; i < KMeans; i++) {

initPlayers.add(players.get(i));

}

}

public GenerateGroup(){

super();

}

public List<user>[] cluster() {/<user>

List<user>[] results = new ArrayList[KMeans];//存放結果/<user>

boolean centerchange = true;

while(centerchange){//指導聚類中心不再改變，跳出循環

centerchange = false;

for (int i = 0; i < KMeans; i++) {

results[i] = new ArrayList<user>();/<user>

}

for(int i=0;i<players.size>

User p = players.get(i);

double[] dists = new double[KMeans];

for(int j=0;j<initplayers.size>

User initP = initPlayers.get(j);

double dist = distance(initP, p);

dists[j] = dist;

}

int dist_index = computOrder(dists);//找出距離最小的用戶的下標

results[dist_index].add(p);

}

for(int i=0;i<kmeans>

User player_new = findNewCenter(results[i]);

User player_old = initPlayers.get(i);

centerchange = true;

initPlayers.set(i, player_new);

clusterHeart.clear();

}else{

clusterHeart.add(player_new);//保存簇心

}

}

}

return results;

}

//比較新舊聚類中心是否相等

public boolean IsPlayerEqual(User p1, User p2) {

if (p1 == p2) {

return true;

}

if (p1 == null || p2 == null) {

return false;

}

boolean flag = true;

double[] s1=p1.getWeights();

double[] s2=p2.getWeights();

for (int i = 0; i < s2.length; i++) {

if(s1[i]!=s2[i]){

flag = false;

break;

}

}

return flag;

}

//找出新的聚類中心

public User findNewCenter(List<user> ps){/<user>

User t = new User();

if (ps == null || ps.size() == 0) {

return t;

}

double[] ds= new double[ps.get(0).getWeights().length];

for (int i = 0; i < ps.get(0).getWeights().length; i++) {

for (int j = 0; j < ps.size(); j++) {

ds[i]+= ps.get(j).getWeights()[i];

}

}

for (int i = 0; i < ps.get(0).getWeights().length; i++) {

ds[i]=ds[i]/ps.size();

}

t.setWeights(ds);

return t;

}

//比較距離，找出最小距離下標

public int computOrder(double[] dists) {

double min = 0;

int index = 0;

double dist0 = dists[i];

if (i == 0) {

min = dist0;

index = 0;

}

double dist1 = dists[i + 1];

if (min > dist1) {

min = dist1;

index = i + 1;

}

}

return index;

}

//判斷距離，歐幾里得算法,最快

public double distance(User p0,User p1){

double dis = 0;

try{

double[] s1 = p0.getWeights();

double[] s2 = p1.getWeights();

for (int i = 0; i < s2.length; i++) {

dis+=Math.pow(s1[i]-s2[i],2);

}

}catch(Exception exception){}

return Math.sqrt(dis);

}

}

3、計算用戶之間的相似度：

public double[] generateSimilarityMatrix2(String userId,List<user> list,double

List<string> user = new ArrayList<string>();/<string>/<string>

for (int i = 0; i < list.size(); i++) {

user.add(list.get(i).getUserId());

}

double[] similarityMatrix = new double[user.size()];

for (int i = 0; i < user.size(); i++) {//循環核心用戶

if(user.get(i).equals(userId)){

similarityMatrix[i]=1;

continue;

}

similarityMatrix[i] = new ComputeSimilarity().computeSimilarity(weight[user.indexOf(userId)], weight[user.indexOf(user.get(i))]);

}

return

}

4、獲取最近鄰和計算推薦結果：

public List<object> recommendCloserAndKeyword(double[] similarityMatrix,double[][] weight,String userId,List<string> list) {/<string>/<object>

String[] userIds = new String[list.size()];

for(int i=0;i<list.size>

userIds[i] = list.get(i);

}

double[] similarity = new double[similarityMatrix.length];

for(int i=0;i<similarity.length>

similarity[i] = similarityMatrix[i];

}

for(int i=0;i<similarity.length>

for(int j=0;j<similarity.length-1-i>

if(similarity[j]<similarity>

double temp = similarity[j];

similarity[j] = similarity[j+1];

similarity[j+1] = temp;

String tag = userIds[j];

userIds[j] = userIds[j+1];

userIds[j+1] = tag;

}

}

}

int n = 0;

for(int i=0;i<userids.length>

if(similarity[i]==0.0)

break;

n++;

}

List<integer> list_user_temp = new ArrayList<integer>();/<integer>/<integer>

List<double> list_simi_sum = new ArrayList<double>();/<double>/<double>

List<double> list_simi_weight_sum = new ArrayList<double>();/<double>/<double>

for(int i=0;i

for(int j=0;j<weight>

if(weight[Integer.parseInt(userId)-1][j]==0.0&&weight[Integer.parseInt(userIds[i])-1][j]!=0.0){

if(list_user_temp.size()==0||!list_user_temp.contains(j)){

list_user_temp.add(j);

list_simi_sum.add(similarity[i]);

list_simi_weight_sum.add(similarity[i]*weight[Integer.parseInt(userIds[i])-1][j]);

}else{

int index = list_user_temp.indexOf(j);

double

double d2 = list_simi_weight_sum.get(index);

list_simi_sum.set(index, d1+similarity[i]);

list_simi_weight_sum.set(index, d2+similarity[i]*weight[Integer.parseInt(userIds[i])-1][j]);

}

}

}

}

List<double> list_result = new ArrayList<double>();/<double>/<double>

for(int i=0;i<list>

list_result.add(list_simi_sum.get(i)!=0.0?list_simi_weight_sum.get(i)/list_simi_sum.get(i):0);

}

Object[] obj = list_result.toArray();

Object[] obj2 = list_user_temp.toArray();

for(int i=0;i<obj.length>

for(int j=0;j<obj.length-1-i>

if((Double)obj[j]

Object o = obj[j];

obj[j] = obj[j+1];

obj[j+1] = o;

o = obj2[j];

obj2[j] = obj2[j+1];

obj2[j+1] = o;

}

}

}

List<object> result = new ArrayList<object>();/<object>/<object>

result.add(obj);

result.add(obj2);

result.add(similarity);

result.add(userIds);

result.add(num);

return result;

}

二：推薦結果：

1、聚類結果：

===========類別1================

1

===========類別2================

2 95 193 288 306 404

===========類別3================

3 11 12 13 14 15 16 17 18 19

20 21 22 23 24 25 26 27 28 29

30 31 32 34 35 36 37 38 39 40

41 42 43 44 45 46 47 48 49 50

52 53 54 55 56 57 58 59 60 61

62 63 64 65 66 67 68 69 70 71

72 73 74 76 77 78 79 80 81 82

83 84 85 86 87 88 89 90 91 92

93 94 96 97 98 100 101 102 103 104

105 106 107 108 109 110 111 112 113 114

115 116 117 118 119 120 121 122 123 124

125 126 127 129 130 131 132 133 134 135

136 138 139 140 141 142 143 144 145 146

147 148 149 150 151 152 153 154 155 156

157 158 159 160 161 162 163 164 165 166

167 168 169 170 171 172 173 174 175 176

177 178 179 180 181 182 183 184 185 186

187 188 189 190 191 192 194 195 196 198

199 200 201 202 203 204 205 206 207 208

209 210 211 212 213 214 215 216 217 218

219 220 221 222 223 224 225 226 227 228

229 230 231 232 233 234 235 236 237 238

239 240 241 242 243 244 245 246 247 248

249 250 251 252 253 254 255 256 257 258

259 260 261 262 263 264 265 266 267 268

269 270 271 272 273 274 275 276 277 278

279 280 281 282 283 284 285 286 287 290

291 292 293 294 295 296 297 298 300 301

302 303 304 305 307 308 309 310 311 312

313 314 315 316 317 318 319 320 321 322

323 324 325 326 327 328 329 330 331 333

334 335 336 337 338 339 340 341 342 343

344 345 346 347 348 349 350 351 352 353

354 355 356 357 358 359 360 361 362 363

364 365 366 367 368 369 370 371 372 373

374 375 376 377 378 379 380 381 382 383

384 385 386 387 388 389 390 391 392 393

394 395 396 397 398 399 400 401 402 403

405 406 407 408 409 410 411 412 413 414

415 416 417 418 419 420 421 422 423 424

425 426 427 428 429 430 431 432 433 434

435 436 437 438 439 440 441 442 443 444

445 446 447 448 449 450 451 452 453 454

455 456 457 458 459 460 461 462 463 464

465 466 467 468 469 470 471 472 474 475

476 477 478 479 480 481 482 483 484 485

486 488 489 490 491 492 493 494 495 496

497 498 499 500

===========類別4================

4 51 137 197

===========類別5================

5 99 128 289 299

===========類別6================

6 332

===========類別7================

7

===========類別8================

8 33 75 473 487

===========類別9================

9

===========類別10================

10

2、最近鄰：

===============TOP-N 10個==============

478:0.3177413723944363 499:0.3156693955485105 177:0.31544323919777684 226:0.31313536250109436 22:0.3106645329420879

342:0.31016327270390476 470:0.3099875760697812 414:0.3097300678691507 464:0.30873879229693146 143:0.3084047430145349

3、推薦結果：

================推薦關鍵字====================

568 預測權重:0.815 880 預測權重:0.775 350 預測權重:0.720 1399 預測權重:0.716 954 預測權重:0.626

1386 預測權重:0.607 343 預測權重:0.575 1173 預測權重:0.559 417 預測權重:0.529 1412 預測權重:0.526

471 預測權重:0.525 1733 預測權重:0.518 1677 預測權重:0.515 662 預測權重:0.493 73 預測權重:0.408

1289 預測權重:0.393 282 預測權重:0.382 283 預測權重:0.330 594 預測權重:0.327 437 預測權重:0.266

79 預測權重:0.262 761 預測權重:0.262 1322 預測權重:0.258 738 預測權重:0.251 1892 預測權重:0.247

1787 預測權重:0.242 280 預測權重:0.238 577 預測權重:0.234 1732 預測權重:0.231 373 預測權重:0.227

1757 預測權重:0.211 911 預測權重:0.193 1462 預測權重:0.189 1631 預測權重:0.177 843 預測權重:0.175

129 預測權重:0.175 1526 預測權重:0.168 962 預測權重:0.160 1662 預測權重:0.158 752 預測權重:0.142

488 預測權重:0.137 848 預測權重:0.135 1640 預測權重:0.134 631 預測權重:0.103 675 預測權重:0.103

983 預測權重:0.090 4 預測權重:0.089 862 預測權重:0.077 1063 預測權重:0.065 1026 預測權重:0.053

885 預測權重:0.048 719 預測權重:0.046 1539 預測權重:0.038 1361 預測權重:0.020

項目源代碼：https://download.csdn.net/download/u011291472/11967865

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關鍵字: 算法 Line 用戶