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专注于解决推荐领域与搜索领域的两个核心问题:排序预测(Ranking)和评分预测(Rating). 为相关领域的研发人员提供完整的通用设计与参考实现. 涵盖了70多种排序预测与评分预测算法,是最快最全的Java推荐与搜索引擎.

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JStarCraft RNS

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希望路过的同学,顺手给JStarCraft框架点个Star,算是对作者的一种鼓励吧!

目录

介绍

JStarCraft RNS是一个面向信息检索领域的轻量级引擎.遵循Apache 2.0协议.

专注于解决信息检索领域的基本问题:推荐与搜索.

提供满足工业级别场景要求的推荐引擎设计与实现.

提供满足工业级别场景要求的搜索引擎设计与实现.

特性

安装

JStarCraft RNS要求使用者具备以下环境:

  • JDK 8或者以上
  • Maven 3

安装JStarCraft-Core框架

git clone https://github.com/HongZhaoHua/jstarcraft-core.git

mvn install -Dmaven.test.skip=true

安装JStarCraft-AI框架

git clone https://github.com/HongZhaoHua/jstarcraft-ai.git

mvn install -Dmaven.test.skip=true

安装JStarCraft-RNS引擎

git clone https://github.com/HongZhaoHua/jstarcraft-rns.git

mvn install -Dmaven.test.skip=true

使用

设置依赖

  • 设置Maven依赖
<dependency>
    <groupId>com.jstarcraft</groupId>
    <artifactId>rns</artifactId>
    <version>1.0</version>
</dependency>
  • 设置Gradle依赖
compile group: 'com.jstarcraft', name: 'rns', version: '1.0'

构建配置器

Properties keyValues = new Properties();
keyValues.load(this.getClass().getResourceAsStream("/data.properties"));
keyValues.load(this.getClass().getResourceAsStream("/recommend/benchmark/randomguess-test.properties"));
Configurator configurator = new Configurator(keyValues);

训练与评估模型

  • 构建排序任务
RankingTask task = new RankingTask(RandomGuessModel.class, configurator);
// 训练与评估排序模型
task.execute();
  • 构建评分任务
RatingTask task = new RatingTask(RandomGuessModel.class, configurator);
// 训练与评估评分模型
task.execute();

获取模型

// 获取模型
Model model = task.getModel();

架构

概念

为什么需要信息检索

随着信息技术和互联网的发展,人们逐渐从信息匮乏(Information Underload)的时代走入了信息过载(Information Overload)的时代.

无论是信息消费者还是信息生产者都遇到了挑战:
* 对于信息消费者,从海量信息中寻找信息,是一件非常困难的事情;
* 对于信息生产者,从海量信息中暴露信息,也是一件非常困难的事情;

信息检索的任务就是联系用户和信息,一方面帮助用户寻找对自己有价值的信息,另一方面帮助信息暴露给对它感兴趣的用户,从而实现信息消费者和信息生产者的双赢.

搜索与推荐的异同

从信息检索的角度:
* 搜索和推荐是获取信息的两种主要手段;
* 搜索和推荐是获取信息的两种不同方式;
    * 搜索(Search)是主动明确的;
    * 推荐(Recommend)是被动模糊的;

搜索和推荐是两个互补的工具.

JStarCraft-RNS引擎解决什么问题

JStarCraft-RNS引擎旨在解决推荐与搜索领域的两个核心任务:排序预测(Ranking)和评分预测(Rating).

Ranking任务与Rating任务之间的区别

根据解决基本问题的不同,将算法与评估指标划分为排序(Ranking)与评分(Rating).

两者之间的根本区别在于目标函数的不同.
通俗点的解释:
Ranking算法基于隐式反馈数据,趋向于拟合用户的排序.(关注度)
Rating算法基于显示反馈数据,趋向于拟合用户的评分.(满意度)

Rating算法能不能用于Ranking问题

关键在于具体场景中,关注度与满意度是否保持一致.
通俗点的解释:
人们关注的东西,并不一定是满意的东西.(例如:个人所得税)

示例

JStarCraft-RNS引擎与BeanShell脚本交互

  • 完整示例

  • 编写BeanShell脚本训练与评估模型并保存到Model.bsh文件

// 构建配置
keyValues = new Properties();
keyValues.load(loader.getResourceAsStream("data.properties"));
keyValues.load(loader.getResourceAsStream("model/benchmark/randomguess-test.properties"));
configurator = new Configurator(keyValues);

// 此对象会返回给Java程序
_data = new HashMap();

// 构建排序任务
task = new RankingTask(RandomGuessModel.class, configurator);
// 训练与评估模型并获取排序指标
measures = task.execute();
_data.put("precision", measures.get(PrecisionEvaluator.class));
_data.put("recall", measures.get(RecallEvaluator.class));

// 构建评分任务
task = new RatingTask(RandomGuessModel.class, configurator);
// 训练与评估模型并获取评分指标
measures = task.execute();
_data.put("mae", measures.get(MAEEvaluator.class));
_data.put("mse", measures.get(MSEEvaluator.class));

_data;
  • 使用JStarCraft框架从Model.bsh文件加载并执行BeanShell脚本
 // 获取BeanShell脚本
File file = new File(ScriptTestCase.class.getResource("Model.bsh").toURI());
String script = FileUtils.readFileToString(file, StringUtility.CHARSET);

// 设置BeanShell脚本使用到的Java类
ScriptContext context = new ScriptContext();
context.useClasses(Properties.class, Assert.class);
context.useClass("Configurator", MapConfigurator.class);
context.useClasses("com.jstarcraft.ai.evaluate");
context.useClasses("com.jstarcraft.rns.task");
context.useClasses("com.jstarcraft.rns.model.benchmark");
// 设置BeanShell脚本使用到的Java变量
ScriptScope scope = new ScriptScope();
scope.createAttribute("loader", loader);

// 执行BeanShell脚本
ScriptExpression expression = new GroovyExpression(context, scope, script);
Map<String, Float> data = expression.doWith(Map.class);
Assert.assertEquals(0.005825241F, data.get("precision"), 0F);
Assert.assertEquals(0.011579763F, data.get("recall"), 0F);
Assert.assertEquals(1.2708743F, data.get("mae"), 0F);
Assert.assertEquals(2.425075F, data.get("mse"), 0F);

JStarCraft-RNS引擎与Groovy脚本交互

  • 完整示例

  • 编写Groovy脚本训练与评估模型并保存到Model.groovy文件

// 构建配置
def keyValues = new Properties();
keyValues.load(loader.getResourceAsStream("data.properties"));
keyValues.load(loader.getResourceAsStream("recommend/benchmark/randomguess-test.properties"));
def configurator = new Configurator(keyValues);

// 此对象会返回给Java程序
def _data = [:];

// 构建排序任务
task = new RankingTask(RandomGuessModel.class, configurator);
// 训练与评估模型并获取排序指标
measures = task.execute();
_data.precision = measures.get(PrecisionEvaluator.class);
_data.recall = measures.get(RecallEvaluator.class);

// 构建评分任务
task = new RatingTask(RandomGuessModel.class, configurator);
// 训练与评估模型并获取评分指标
measures = task.execute();
_data.mae = measures.get(MAEEvaluator.class);
_data.mse = measures.get(MSEEvaluator.class);

_data;
  • 使用JStarCraft框架从Model.groovy文件加载并执行Groovy脚本
// 获取Groovy脚本
File file = new File(ScriptTestCase.class.getResource("Model.groovy").toURI());
String script = FileUtils.readFileToString(file, StringUtility.CHARSET);

// 设置Groovy脚本使用到的Java类
ScriptContext context = new ScriptContext();
context.useClasses(Properties.class, Assert.class);
context.useClass("Configurator", MapConfigurator.class);
context.useClasses("com.jstarcraft.ai.evaluate");
context.useClasses("com.jstarcraft.rns.task");
context.useClasses("com.jstarcraft.rns.model.benchmark");
// 设置Groovy脚本使用到的Java变量
ScriptScope scope = new ScriptScope();
scope.createAttribute("loader", loader);

// 执行Groovy脚本
ScriptExpression expression = new GroovyExpression(context, scope, script);
Map<String, Float> data = expression.doWith(Map.class);

JStarCraft-RNS引擎与JS脚本交互

  • 完整示例

  • 编写JS脚本训练与评估模型并保存到Model.js文件

// 构建配置
var keyValues = new Properties();
keyValues.load(loader.getResourceAsStream("data.properties"));
keyValues.load(loader.getResourceAsStream("recommend/benchmark/randomguess-test.properties"));
var configurator = new Configurator([keyValues]);

// 此对象会返回给Java程序
var _data = {};

// 构建排序任务
task = new RankingTask(RandomGuessModel.class, configurator);
// 训练与评估模型并获取排序指标
measures = task.execute();
_data['precision'] = measures.get(PrecisionEvaluator.class);
_data['recall'] = measures.get(RecallEvaluator.class);

// 构建评分任务
task = new RatingTask(RandomGuessModel.class, configurator);
// 训练与评估模型并获取评分指标
measures = task.execute();
_data['mae'] = measures.get(MAEEvaluator.class);
_data['mse'] = measures.get(MSEEvaluator.class);

_data;
  • 使用JStarCraft框架从Model.js文件加载并执行JS脚本
// 获取JS脚本
File file = new File(ScriptTestCase.class.getResource("Model.js").toURI());
String script = FileUtils.readFileToString(file, StringUtility.CHARSET);

// 设置JS脚本使用到的Java类
ScriptContext context = new ScriptContext();
context.useClasses(Properties.class, Assert.class);
context.useClass("Configurator", MapConfigurator.class);
context.useClasses("com.jstarcraft.ai.evaluate");
context.useClasses("com.jstarcraft.rns.task");
context.useClasses("com.jstarcraft.rns.model.benchmark");
// 设置JS脚本使用到的Java变量
ScriptScope scope = new ScriptScope();
scope.createAttribute("loader", loader);

// 执行JS脚本
ScriptExpression expression = new JsExpression(context, scope, script);
Map<String, Float> data = expression.doWith(Map.class);

JStarCraft-RNS引擎与Kotlin脚本交互

  • 完整示例

  • 编写Kotlin脚本训练与评估模型并保存到Model.kt文件

// 构建配置
var keyValues = Properties();
var loader = bindings["loader"] as ClassLoader;
keyValues.load(loader.getResourceAsStream("data.properties"));
keyValues.load(loader.getResourceAsStream("model/benchmark/randomguess-test.properties"));
var option = Option(keyValues);

// 此对象会返回给Java程序
var _data = mutableMapOf<String, Float>();

// 构建排序任务
var rankingTask = RankingTask(RandomGuessModel::class.java, option);
// 训练与评估模型并获取排序指标
val rankingMeasures = rankingTask.execute();
_data["precision"] = rankingMeasures.getFloat(PrecisionEvaluator::class.java);
_data["recall"] = rankingMeasures.getFloat(RecallEvaluator::class.java);

// 构建评分任务
var ratingTask = RatingTask(RandomGuessModel::class.java, option);
// 训练与评估模型并获取评分指标
var ratingMeasures = ratingTask.execute();
_data["mae"] = ratingMeasures.getFloat(MAEEvaluator::class.java);
_data["mse"] = ratingMeasures.getFloat(MSEEvaluator::class.java);

_data;
  • 使用JStarCraft框架从Model.kt文件加载并执行Kotlin脚本
// 获取Kotlin脚本
File file = new File(ScriptTestCase.class.getResource("Model.kt").toURI());
String script = FileUtils.readFileToString(file, StringUtility.CHARSET);

// 设置Kotlin脚本使用到的Java类
ScriptContext context = new ScriptContext();
context.useClasses(Properties.class, Assert.class);
context.useClass("Option", MapOption.class);
context.useClasses("com.jstarcraft.ai.evaluate");
context.useClasses("com.jstarcraft.rns.task");
context.useClasses("com.jstarcraft.rns.model.benchmark");
// 设置Kotlin脚本使用到的Java变量
ScriptScope scope = new ScriptScope();
scope.createAttribute("loader", loader);

// 执行Kotlin脚本
ScriptExpression expression = new KotlinExpression(context, scope, script);
Map<String, Float> data = expression.doWith(Map.class);

JStarCraft-RNS引擎与Lua脚本交互

  • 完整示例

  • 编写Lua脚本训练与评估模型并保存到Model.lua文件

-- 构建配置
local keyValues = Properties.new();
keyValues:load(loader:getResourceAsStream("data.properties"));

keyValues:load(loader:getResourceAsStream("recommend/benchmark/randomguess-test.properties"));
local configurator = Configurator.new({ keyValues });

-- 此对象会返回给Java程序
local _data = {};

-- 构建排序任务
task = RankingTask.new(RandomGuessModel, configurator);
-- 训练与评估模型并获取排序指标
measures = task:execute();
_data["precision"] = measures:get(PrecisionEvaluator);
_data["recall"] = measures:get(RecallEvaluator);

-- 构建评分任务
task = RatingTask.new(RandomGuessModel, configurator);
-- 训练与评估模型并获取评分指标
measures = task:execute();
_data["mae"] = measures:get(MAEEvaluator);
_data["mse"] = measures:get(MSEEvaluator);

return _data;
  • 使用JStarCraft框架从Model.lua文件加载并执行Lua脚本
// 获取Lua脚本
File file = new File(ScriptTestCase.class.getResource("Model.lua").toURI());
String script = FileUtils.readFileToString(file, StringUtility.CHARSET);

// 设置Lua脚本使用到的Java类
ScriptContext context = new ScriptContext();
context.useClasses(Properties.class, Assert.class);
context.useClass("Configurator", MapConfigurator.class);
context.useClasses("com.jstarcraft.ai.evaluate");
context.useClasses("com.jstarcraft.rns.task");
context.useClasses("com.jstarcraft.rns.model.benchmark");
// 设置Lua脚本使用到的Java变量
ScriptScope scope = new ScriptScope();
scope.createAttribute("loader", loader);

// 执行Lua脚本
ScriptExpression expression = new LuaExpression(context, scope, script);
LuaTable data = expression.doWith(LuaTable.class);

JStarCraft-RNS引擎与Python脚本交互

  • 完整示例

  • 编写Python脚本训练与评估模型并保存到Model.py文件

# 构建配置
keyValues = Properties()
keyValues.load(loader.getResourceAsStream("data.properties"))
keyValues.load(loader.getResourceAsStream("recommend/benchmark/randomguess-test.properties"))
configurator = Configurator([keyValues])

# 此对象会返回给Java程序
_data = {}

# 构建排序任务
task = RankingTask(RandomGuessModel, configurator)
# 训练与评估模型并获取排序指标
measures = task.execute()
_data['precision'] = measures.get(PrecisionEvaluator)
_data['recall'] = measures.get(RecallEvaluator)

# 构建评分任务
task = RatingTask(RandomGuessModel, configurator)
# 训练与评估模型并获取评分指标
measures = task.execute()
_data['mae'] = measures.get(MAEEvaluator)
_data['mse'] = measures.get(MSEEvaluator)
  • 使用JStarCraft框架从Model.py文件加载并执行Python脚本
// 设置Python环境变量
System.setProperty("python.console.encoding", StringUtility.CHARSET.name());

// 获取Python脚本
File file = new File(PythonTestCase.class.getResource("Model.py").toURI());
String script = FileUtils.readFileToString(file, StringUtility.CHARSET);

// 设置Python脚本使用到的Java类
ScriptContext context = new ScriptContext();
context.useClasses(Properties.class, Assert.class);
context.useClass("Configurator", MapConfigurator.class);
context.useClasses("com.jstarcraft.ai.evaluate");
context.useClasses("com.jstarcraft.rns.task");
context.useClasses("com.jstarcraft.rns.model.benchmark");
// 设置Python脚本使用到的Java变量
ScriptScope scope = new ScriptScope();
scope.createAttribute("loader", loader);

// 执行Python脚本
ScriptExpression expression = new PythonExpression(context, scope, script);
Map<String, Double> data = expression.doWith(Map.class);

JStarCraft-Ruby

  • 完整示例

  • 编写Ruby脚本训练与评估模型并保存到Model.rb文件

# 构建配置
keyValues = Properties.new()
keyValues.load($loader.getResourceAsStream("data.properties"))
keyValues.load($loader.getResourceAsStream("model/benchmark/randomguess-test.properties"))
configurator = Configurator.new(keyValues)

# 此对象会返回给Java程序
_data = Hash.new()

# 构建排序任务
task = RankingTask.new(RandomGuessModel.java_class, configurator)
# 训练与评估模型并获取排序指标
measures = task.execute()
_data['precision'] = measures.get(PrecisionEvaluator.java_class)
_data['recall'] = measures.get(RecallEvaluator.java_class)

# 构建评分任务
task = RatingTask.new(RandomGuessModel.java_class, configurator)
# 训练与评估模型并获取评分指标
measures = task.execute()
_data['mae'] = measures.get(MAEEvaluator.java_class)
_data['mse'] = measures.get(MSEEvaluator.java_class)

_data;
  • 使用JStarCraft框架从Model.rb文件加载并执行Ruby脚本
// 获取Ruby脚本
File file = new File(ScriptTestCase.class.getResource("Model.rb").toURI());
String script = FileUtils.readFileToString(file, StringUtility.CHARSET);

// 设置Ruby脚本使用到的Java类
ScriptContext context = new ScriptContext();
context.useClasses(Properties.class, Assert.class);
context.useClass("Configurator", MapConfigurator.class);
context.useClasses("com.jstarcraft.ai.evaluate");
context.useClasses("com.jstarcraft.rns.task");
context.useClasses("com.jstarcraft.rns.model.benchmark");
// 设置Ruby脚本使用到的Java变量
ScriptScope scope = new ScriptScope();
scope.createAttribute("loader", loader);

// 执行Ruby脚本
ScriptExpression expression = new RubyExpression(context, scope, script);
Map<String, Double> data = expression.doWith(Map.class);
Assert.assertEquals(0.005825241096317768D, data.get("precision"), 0D);
Assert.assertEquals(0.011579763144254684D, data.get("recall"), 0D);
Assert.assertEquals(1.270874261856079D, data.get("mae"), 0D);
Assert.assertEquals(2.425075054168701D, data.get("mse"), 0D);

对比

排序模型对比

  • 基准模型
名称 数据集 训练 (毫秒) 预测 (毫秒) AUC MAP MRR NDCG Novelty Precision Recall
MostPopular filmtrust 43 273 0.92080 0.41246 0.57196 0.51583 11.79295 0.33230 0.62385
RandomGuess filmtrust 38 391 0.51922 0.00627 0.02170 0.01121 91.94900 0.00550 0.01262
  • 协同模型
名称 数据集 训练 (毫秒) 预测 (毫秒) AUC MAP MRR NDCG Novelty Precision Recall
AoBPR filmtrust 12448 253 0.89324 0.38967 0.53990 0.48338 21.13004 0.32295 0.56864
AspectRanking filmtrust 177 58 0.85130 0.15498 0.42480 0.26012 37.36273 0.13302 0.31292
BHFreeRanking filmtrust 5720 4257 0.92080 0.41316 0.57231 0.51662 11.79567 0.33276 0.62500
BPR filmtrust 4228 137 0.89390 0.39886 0.54790 0.49180 21.46738 0.32268 0.57623
BUCMRanking filmtrust 2111 1343 0.90782 0.39794 0.55776 0.49651 13.08073 0.32407 0.59141
CDAE filmtrust 89280 376 0.91880 0.40759 0.56855 0.51089 11.82466 0.33051 0.61967
CLiMF filmtrust 48429 140 0.88293 0.37395 0.52407 0.46572 19.38964 0.32049 0.54605
DeepFM filmtrust 69264 99 0.91679 0.40580 0.56995 0.50985 11.90242 0.32719 0.61426
EALS filmtrust 850 185 0.86132 0.31263 0.45680 0.39475 20.08964 0.27381 0.46271
FISMAUC filmtrust 2338 663 0.91216 0.40032 0.55730 0.50114 12.07469 0.32845 0.60294
FISMRMSE filmtrust 4030 729 0.91482 0.40795 0.56470 0.50920 11.91234 0.33044 0.61107
GBPR filmtrust 14827 150 0.92113 0.41003 0.57144 0.51464 11.87609 0.33090 0.62512
HMM game 38697 11223 0.80559 0.18156 0.37516 0.25803 16.01041 0.14572 0.22810
ItemBigram filmtrust 12492 61 0.88807 0.33520 0.46870 0.42854 17.11172 0.29191 0.53308
ItemKNNRanking filmtrust 2683 250 0.87438 0.33375 0.46951 0.41767 20.23449 0.28581 0.49248
LDA filmtrust 696 161 0.91980 0.41758 0.58130 0.52003 12.31348 0.33336 0.62274
LambdaFMStatic game 25052 27078 0.87064 0.27294 0.43640 0.34794 16.47330 0.13941 0.35696
LambdaFMWeight game 25232 28156 0.87339 0.27333 0.43720 0.34728 14.71413 0.13742 0.35252
LambdaFMDynamic game 74218 27921 0.87380 0.27288 0.43648 0.34706 13.50578 0.13822 0.35132
ListwiseMF filmtrust 714 161 0.90820 0.40511 0.56619 0.50521 15.53665 0.32944 0.60092
PLSA filmtrust 1027 116 0.89950 0.41217 0.57187 0.50597 16.01080 0.32401 0.58557
RankALS filmtrust 3285 182 0.85901 0.29255 0.51014 0.38871 25.27197 0.22931 0.42509
RankCD product 1442 8905 0.56271 0.01253 0.04618 0.02682 55.42019 0.01548 0.03520
RankSGD filmtrust 309 113 0.80388 0.23587 0.42290 0.32081 42.83305 0.19363 0.35374
RankVFCD product 54273 6524 0.58022 0.01784 0.06181 0.03664 62.95810 0.01980 0.04852
SLIM filmtrust 62434 91 0.91849 0.44851 0.61083 0.54557 16.67990 0.34019 0.63021
UserKNNRanking filmtrust 1154 229 0.90752 0.41616 0.57525 0.51393 12.90921 0.32891 0.60152
VBPR product 184473 15304 0.54336 0.00920 0.03522 0.01883 45.05101 0.01037 0.02266
WBPR filmtrust 20705 183 0.78072 0.24647 0.33373 0.30442 17.18609 0.25000 0.35516
WRMF filmtrust 482 158 0.90616 0.43278 0.58284 0.52480 15.17956 0.32918 0.60780
RankGeoFM FourSquare 368436 1093 0.72708 0.05485 0.24012 0.11057 37.50040 0.07866 0.08640
SBPR filmtrust 41481 247 0.91010 0.41189 0.56480 0.50726 15.67905 0.32440 0.59699
  • 内容模型
名称 数据集 训练 (毫秒) 预测 (毫秒) AUC MAP MRR NDCG Novelty Precision Recall
EFMRanking dc_dense 2066 2276 0.61271 0.01611 0.04631 0.04045 53.26140 0.02387 0.07357
TFIDF musical_instruments 942 1085 0.52756 0.01067 0.01917 0.01773 72.71228 0.00588 0.03103

评分模型对比

  • 基准模型
名称 数据集 训练 (毫秒) 预测 (毫秒) MAE MPE MSE
ConstantGuess filmtrust 137 45 1.05608 1.00000 1.42309
GlobalAverage filmtrust 60 13 0.71977 0.77908 0.85199
ItemAverage filmtrust 59 12 0.72968 0.97242 0.86413
ItemCluster filmtrust 471 41 0.71976 0.77908 0.85198
RandomGuess filmtrust 38 8 1.28622 0.99597 2.47927
UserAverage filmtrust 35 9 0.64618 0.97242 0.70172
UserCluster filmtrust 326 45 0.71977 0.77908 0.85199
  • 协同模型
名称 数据集 训练 (毫秒) 预测 (毫秒) MAE MPE MSE
AspectRating filmtrust 220 5 0.65754 0.97918 0.71809
ASVDPlusPlus filmtrust 5631 8 0.71975 0.77921 0.85196
BiasedMF filmtrust 92 6 0.63157 0.98387 0.66220
BHFreeRating filmtrust 6667 76 0.71974 0.77908 0.85198
BPMF filmtrust 25942 52 0.66504 0.98465 0.70210
BUCMRating filmtrust 1843 30 0.64834 0.99102 0.67992
CCD product 15715 9 0.96670 0.93947 1.62145
FFM filmtrust 5422 6 0.63446 0.98413 0.66682
FMALS filmtrust 1854 5 0.64788 0.96032 0.73636
FMSGD filmtrust 3496 10 0.63452 0.98426 0.66710
GPLSA filmtrust 2567 7 0.67311 0.98972 0.79883
IRRG filmtrust 40284 6 0.64766 0.98777 0.73700
ItemKNNRating filmtrust 2052 27 0.62341 0.95394 0.67312
LDCC filmtrust 8650 84 0.66383 0.99284 0.70666
LLORMA filmtrust 16618 82 0.64930 0.96591 0.76067
MFALS filmtrust 2944 5 0.82939 0.94549 1.30547
NMF filmtrust 1198 8 0.67661 0.96604 0.83493
PMF filmtrust 215 7 0.72959 0.98165 0.99948
RBM filmtrust 19551 270 0.74484 0.98504 0.88968
RFRec filmtrust 16330 54 0.64008 0.97112 0.69390
SVDPlusPlus filmtrust 452 26 0.65248 0.99141 0.68289
URP filmtrust 1514 25 0.64207 0.99128 0.67122
UserKNNRating filmtrust 1121 135 0.63933 0.94640 0.69280
RSTE filmtrust 4052 10 0.64303 0.99206 0.67777
SocialMF filmtrust 918 13 0.64668 0.98881 0.68228
SoRec filmtrust 1048 10 0.64305 0.99232 0.67776
SoReg filmtrust 635 8 0.65943 0.96734 0.72760
TimeSVD filmtrust 11545 36 0.68954 0.93326 0.87783
TrustMF filmtrust 2038 7 0.63787 0.98985 0.69017
TrustSVD filmtrust 12465 22 0.61984 0.98933 0.63875
AssociationRule filmtrust 2628 195 0.90853 0.41801 0.57777
PersonalityDiagnosis filmtrust 45 642 0.72964 0.76620 1.03071
PRankD filmtrust 3321 170 0.74472 0.22894 0.32406
SlopeOne filmtrust 135 28 0.63788 0.96175 0.71057
  • 内容模型
名称 数据集 训练 (毫秒) 预测 (毫秒) MAE MPE MSE
EFMRating dc_dense 659 8 0.61546 0.85364 0.78279
HFT musical_instruments 162753 13 0.64272 0.94886 0.81393
TopicMFAT musical_instruments 6907 7 0.61896 0.98734 0.72545
TopicMFMT musical_instruments 6323 7 0.61896 0.98734 0.72545

版本

参考

个性化模型说明

  • 基准模型
名称 问题 说明/论文
RandomGuess Ranking Rating 随机猜测
MostPopular Ranking 最受欢迎
ConstantGuess Rating 常量猜测
GlobalAverage Rating 全局平均
ItemAverage Rating 物品平均
ItemCluster Rating 物品聚类
UserAverage Rating 用户平均
UserCluster Rating 用户聚类
  • 协同模型
名称 问题 说明/论文
AspectModel Ranking Rating Latent class models for collaborative filtering
BHFree Ranking Rating Balancing Prediction and Recommendation Accuracy: Hierarchical Latent Factors for Preference Data
BUCM Ranking Rating Modeling Item Selection and Relevance for Accurate Recommendations
ItemKNN Ranking Rating 基于物品的协同过滤
UserKNN Ranking Rating 基于用户的协同过滤
AoBPR Ranking Improving pairwise learning for item recommendation from implicit feedback
BPR Ranking BPR: Bayesian Personalized Ranking from Implicit Feedback
CLiMF Ranking CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering
EALS Ranking Collaborative filtering for implicit feedback dataset
FISM Ranking FISM: Factored Item Similarity Models for Top-N Recommender Systems
GBPR Ranking GBPR: Group Preference Based Bayesian Personalized Ranking for One-Class Collaborative Filtering
HMMForCF Ranking A Hidden Markov Model Purpose: A class for the model, including parameters
ItemBigram Ranking Topic Modeling: Beyond Bag-of-Words
LambdaFM Ranking LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates
LDA Ranking Latent Dirichlet Allocation for implicit feedback
ListwiseMF Ranking List-wise learning to rank with matrix factorization for collaborative filtering
PLSA Ranking Latent semantic models for collaborative filtering
RankALS Ranking Alternating Least Squares for Personalized Ranking
RankSGD Ranking Collaborative Filtering Ensemble for Ranking
SLIM Ranking SLIM: Sparse Linear Methods for Top-N Recommender Systems
WBPR Ranking Bayesian Personalized Ranking for Non-Uniformly Sampled Items
WRMF Ranking Collaborative filtering for implicit feedback datasets
Rank-GeoFM Ranking Rank-GeoFM: A ranking based geographical factorization method for point of interest recommendation
SBPR Ranking Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering
AssociationRule Ranking A Recommendation Algorithm Using Multi-Level Association Rules
PRankD Ranking Personalised ranking with diversity
AsymmetricSVD++ Rating Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model
AutoRec Rating AutoRec: Autoencoders Meet Collaborative Filtering
BPMF Rating Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo
CCD Rating Large-Scale Parallel Collaborative Filtering for the Netflix Prize
FFM Rating Field Aware Factorization Machines for CTR Prediction
GPLSA Rating Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis
IRRG Rating Exploiting Implicit Item Relationships for Recommender Systems
MFALS Rating Large-Scale Parallel Collaborative Filtering for the Netflix Prize
NMF Rating Algorithms for Non-negative Matrix Factorization
PMF Rating PMF: Probabilistic Matrix Factorization
RBM Rating Restricted Boltzman Machines for Collaborative Filtering
RF-Rec Rating RF-Rec: Fast and Accurate Computation of Recommendations based on Rating Frequencies
SVD++ Rating Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model
URP Rating User Rating Profile: a LDA model for rating prediction
RSTE Rating Learning to Recommend with Social Trust Ensemble
SocialMF Rating A matrix factorization technique with trust propagation for recommendation in social networks
SoRec Rating SoRec: Social recommendation using probabilistic matrix factorization
SoReg Rating Recommender systems with social regularization
TimeSVD++ Rating Collaborative Filtering with Temporal Dynamics
TrustMF Rating Social Collaborative Filtering by Trust
TrustSVD Rating TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings
PersonalityDiagnosis Rating A brief introduction to Personality Diagnosis
SlopeOne Rating Slope One Predictors for Online Rating-Based Collaborative Filtering
  • 内容模型
名称 问题 说明/论文
EFM Ranking Rating Explicit factor models for explainable recommendation based on phrase-level sentiment analysis
TF-IDF Ranking 词频-逆文档频率
HFT Rating Hidden factors and hidden topics: understanding rating dimensions with review text
TopicMF Rating TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation

数据集说明

协议

JStarCraft RNS遵循Apache 2.0协议,一切以其为基础的衍生作品均属于衍生作品的作者.

作者

作者 洪钊桦
E-mail [email protected], [email protected]

致谢

特别感谢LibRec团队与推荐系统QQ群(274750470)在推荐方面提供的支持与帮助.

特别感谢陆徐刚在搜索方面提供的支持与帮助.

捐赠

特别感谢杭州橙子信息科技有限公司对JStarCraft项目的捐赠.

About

专注于解决推荐领域与搜索领域的两个核心问题:排序预测(Ranking)和评分预测(Rating). 为相关领域的研发人员提供完整的通用设计与参考实现. 涵盖了70多种排序预测与评分预测算法,是最快最全的Java推荐与搜索引擎.

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python search java content groovy algorithm lua system js script rating engine context ranking collaborative recommend librec mvel

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