JStarCraft RNS

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!

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

1.跨平台
2.串行与并行计算
3.CPU与GPU硬件加速
4.模型保存与装载
5.丰富的推荐与搜索算法
6.丰富的脚本支持
- Groovy
- JS
- Lua
- MVEL
- Python
- Ruby
7.丰富的评估指标
- 排序指标
- 评分指标

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

PropertieskeyValues = newProperties(); keyValues.load(this.getClass().getResourceAsStream("/data.properties")); keyValues.load(this.getClass().getResourceAsStream("/recommend/benchmark/randomguess-test.properties")); Configuratorconfigurator = newConfigurator(keyValues);

训练与评估模型

构建排序任务

RankingTasktask = newRankingTask(RandomGuessModel.class, configurator); // 训练与评估排序模型task.execute();

构建评分任务

RatingTasktask = newRatingTask(RandomGuessModel.class, configurator); // 训练与评估评分模型task.execute();

获取模型

// 获取模型Modelmodel = 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Filefile = newFile(ScriptTestCase.class.getResource("Model.bsh").toURI()); Stringscript = FileUtils.readFileToString(file, StringUtility.CHARSET); // 设置BeanShell脚本使用到的Java类ScriptContextcontext = newScriptContext(); 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ScriptScopescope = newScriptScope(); scope.createAttribute("loader", loader); // 执行BeanShell脚本ScriptExpressionexpression = newGroovyExpression(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 =newProperties(); keyValues.load(loader.getResourceAsStream("data.properties")); keyValues.load(loader.getResourceAsStream("recommend/benchmark/randomguess-test.properties")); def configurator =newConfigurator(keyValues); // 此对象会返回给Java程序def _data = [:]; // 构建排序任务 task =newRankingTask(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 =newRatingTask(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Filefile = newFile(ScriptTestCase.class.getResource("Model.groovy").toURI()); Stringscript = FileUtils.readFileToString(file, StringUtility.CHARSET); // 设置Groovy脚本使用到的Java类ScriptContextcontext = newScriptContext(); 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ScriptScopescope = newScriptScope(); scope.createAttribute("loader", loader); // 执行Groovy脚本ScriptExpressionexpression = newGroovyExpression(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varkeyValues=newProperties();keyValues.load(loader.getResourceAsStream("data.properties"));keyValues.load(loader.getResourceAsStream("recommend/benchmark/randomguess-test.properties"));varconfigurator=newConfigurator([keyValues]);// 此对象会返回给Java程序var_data={};// 构建排序任务task=newRankingTask(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=newRatingTask(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Filefile = newFile(ScriptTestCase.class.getResource("Model.js").toURI()); Stringscript = FileUtils.readFileToString(file, StringUtility.CHARSET); // 设置JS脚本使用到的Java类ScriptContextcontext = newScriptContext(); 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ScriptScopescope = newScriptScope(); scope.createAttribute("loader", loader); // 执行JS脚本ScriptExpressionexpression = newJsExpression(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varkeyValues=Properties();varloader=bindings["loader"]asClassLoader;keyValues.load(loader.getResourceAsStream("data.properties"));keyValues.load(loader.getResourceAsStream("model/benchmark/randomguess-test.properties"));varoption=Option(keyValues);// 此对象会返回给Java程序var_data=mutableMapOf<String,Float>();// 构建排序任务varrankingTask=RankingTask(RandomGuessModel::class.java,option);// 训练与评估模型并获取排序指标valrankingMeasures=rankingTask.execute();_data["precision"]=rankingMeasures.getFloat(PrecisionEvaluator::class.java);_data["recall"]=rankingMeasures.getFloat(RecallEvaluator::class.java);// 构建评分任务varratingTask=RatingTask(RandomGuessModel::class.java,option);// 训练与评估模型并获取评分指标varratingMeasures=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Filefile = newFile(ScriptTestCase.class.getResource("Model.kt").toURI()); Stringscript = FileUtils.readFileToString(file, StringUtility.CHARSET); // 设置Kotlin脚本使用到的Java类ScriptContextcontext = newScriptContext(); 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ScriptScopescope = newScriptScope(); scope.createAttribute("loader", loader); // 执行Kotlin脚本ScriptExpressionexpression = newKotlinExpression(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localkeyValues=Properties.new(); keyValues:load(loader:getResourceAsStream("data.properties")); keyValues:load(loader:getResourceAsStream("recommend/benchmark/randomguess-test.properties")); localconfigurator=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Filefile = newFile(ScriptTestCase.class.getResource("Model.lua").toURI()); Stringscript = FileUtils.readFileToString(file, StringUtility.CHARSET); // 设置Lua脚本使用到的Java类ScriptContextcontext = newScriptContext(); 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ScriptScopescope = newScriptScope(); scope.createAttribute("loader", loader); // 执行Lua脚本ScriptExpressionexpression = newLuaExpression(context, scope, script); LuaTabledata = 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Filefile = newFile(PythonTestCase.class.getResource("Model.py").toURI()); Stringscript = FileUtils.readFileToString(file, StringUtility.CHARSET); // 设置Python脚本使用到的Java类ScriptContextcontext = newScriptContext(); 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ScriptScopescope = newScriptScope(); scope.createAttribute("loader", loader); // 执行Python脚本ScriptExpressionexpression = newPythonExpression(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Filefile = newFile(ScriptTestCase.class.getResource("Model.rb").toURI()); Stringscript = FileUtils.readFileToString(file, StringUtility.CHARSET); // 设置Ruby脚本使用到的Java类ScriptContextcontext = newScriptContext(); 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ScriptScopescope = newScriptScope(); scope.createAttribute("loader", loader); // 执行Ruby脚本ScriptExpressionexpression = newRubyExpression(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.

Name		Name	Last commit message	Last commit date
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src		src
LICENSE		LICENSE
README.md		README.md
data.7z		data.7z
pom.xml		pom.xml

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laozhaoJava/jstarcraft-rns

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