<img src="http://librec.net/images/logo.png" height="25%" width="25%" />
**LibRec** (http://www.librec.net) is a Java library for recommender systems (Java version 1.7 or higher required). It implements a suit of state-of-the-art recommendation algorithms, aiming to resolve two classic recommendation tasks: **rating prediction** and **item ranking**.
[![Join the chat at https://gitter.im/librec/Lobby](https://badges.gitter.im/librec/Lobby.svg)](https://gitter.im/librec/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)[![Build Status](https://travis-ci.org/guoguibing/librec.svg?branch=2.0.0)](https://travis-ci.org/guoguibing/librec)
### LibRec Demo
A movie recommender system is designed and [available here](http://demo.librec.net).
### Documentation
Please refer to [LibRec Documentation](http://wiki.librec.net) and [API Documentation](http://librec.net/doc/librec-v2.0/)
### Authors Words about the NEW Version
It has been a year since the last version was released. In this year, lots of changes have been taken to the LibRec project, and the most significant one is the formulation of the LibRec team. The team pushes forward the development of LibRec with the wisdom of many experts, and the collaboration of experienced and enthusiastic contributors. Without their great efforts and hardworking, it is impossible to reach the state that a single developer may dream of.
LibRec 2.0 is not the end of our teamwork, but just the begining of greater objectives. We aim to continously provide NEXT versions for better experience and performance. There are many directions and goals in plan, and we will do our best to make them happen. It is always exciting to receive any code contributions, suggestions, comments from all our LibRec users.
We hope you enjoy the new version!
PS: Follow us on WeChat to have first-hand and up-to-date information about LibRec.
<img src="http://librec.net/images/mp.jpg" height="25%" width="25%" />
### Features
* **Rich Algorithms:** More than 70 recommendation algorithms have been implemented, and more will be done.
* **High Modularity:** Six main components including data split, data conversion, similarity, algorithms, evaluators and filters.
* **Great Performance:** More efficient implementations than other counterparts while producing comparable accuracy.
* **Flexible Configuration:** Low coupling, flexible and either external textual or internal API configuration.
* **Simple Usage:** Can get executed in a few lines of codes, and a number of demos are provided for easy start.
* **Easy Expansion:** A set of recommendation interfaces for easy expansion to implement new recommenders.
<div style="text-align:center;"><img src="http://librec.net/images/modules.jpg" width="500" /> </div>
The procedure of LibRec is illustrated as follows.
<div style="text-align:center;"><img src="http://librec.net/images/procedure.jpg" width="600" /> </div>
### Download
by maven
```
<dependency>
<groupId>net.librec</groupId>
<artifactId>librec-core</artifactId>
<version>2.0.0</version>
</dependency>
```
by packages
* **[librec-v2.0](https://github.com/guoguibing/librec/archive/librec-src-v2.0.zip)**
* **[librec-v1.3](http://www.librec.net/release/librec-v1.3.zip)**
* **[librec-v1.2](http://www.librec.net/release/librec-v1.2.zip)**
* **[librec-v1.1](http://www.librec.net/release/librec-v1.1.zip)**
* **[librec-v1.0](http://www.librec.net/release/librec-v1.0.zip)**
### Execution
You can run LibRec with configurations from command arguments:
<pre>
librec rec -exec -D rec.recommender.class=itemcluster -D rec.pgm.number=10 -D rec.iterator.maximum=20
</pre>
or from a configuration file:
<pre>
librec rec -exec -conf itemcluster-test.properties
</pre>
### Code Snippet
You can use **LibRec** as a part of your projects, and use the following codes to run a recommender.
<pre>
public void main(String[] args) throws Exception {
// recommender configuration
Configuration conf = new Configuration();
Resource resource = new Resource("rec/cf/userknn-test.properties");
conf.addResource(resource);
// build data model
DataModel dataModel = new TextDataModel(conf);
dataModel.buildDataModel();
// set recommendation context
RecommenderContext context = new RecommenderContext(conf, dataModel);
RecommenderSimilarity similarity = new PCCSimilarity();
similarity.buildSimilarityMatrix(dataModel, true);
context.setSimilarity(similarity);
// training
Recommender recommender = new UserKNNRecommender();
recommender.recommend(context);
// evaluation
RecommenderEvaluator evaluator = new MAEEvaluator();
recommender.evaluate(evaluator);
// recommendation results
List<RecommendedItem> recommendedItemList = recommender.getRecommendedList();
RecommendedFilter filter = new GenericRecommendedFilter();
recommendedItemList = filter.filter(recommendedItemList);
}
</pre>
### News Report
* [An Introduction to Open-source Recommendaion Toolkit: LibRec](http://chuansong.me/n/1701947351918) [by ResysChina in Chinese]
* [LibRec: an Open-source and Cross-platform Software for Recommender Systems](http://chuansong.me/n/1751521251128) [by InfoQ in Chinese]
### Acknowledgement
We would like to express our appreciation to the following people for contributing source codes to LibRec, including [Prof. Robin Burke](http://josquin.cti.depaul.edu/~rburke/), [Bin Wu](https://github.com/wubin7019088), [Diego Monti](https://github.com/dmm42), [Ge Zhou](https://github.com/466152112), Li Wenxi, [Marco Mera](https://github.com/mmera), [Ran Locar](https://github.com/ranlocar), [Shawn Rutledge](https://github.com/shawndr), [ShuLong Chen](https://github.com/ChenSuL), [Tao Lian](https://github.com/taolian), [Takuya Kitazawa](https://github.com/takuti), [Zhaohua Hong](mailto:jstarcraft@gmail.com), Tan Jiale, [Daniel Velten](https://github.com/dvelten), [Qian Shaofeng](https://github.com/shitou112), etc. We gratefully thank Mr. Lijun Dai for designing and contributing the logo of LibRec, and also many thanks to [Mr. Jianbin Zhang](http://www.liaotian2020.com/) for implementing and sharing a [LibRec demo](http://demo.librec.net/).
We also appreciate many others for reporting bugs and issues, and for providing valuable suggestions and support.
### Publications
Please cite the following papers if LibRec is helpful to your research.
1. G. Guo, J. Zhang, Z. Sun and N. Yorke-Smith, [LibRec: A Java Library for Recommender Systems](http://ceur-ws.org/Vol-1388/demo_paper1.pdf), in Posters, Demos, Late-breaking Results and Workshop Proceedings of the 23rd Conference on User Modelling, Adaptation and Personalization (UMAP), 2015.
2. G. Guo, J. Zhang and N. Yorke-Smith, TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings, in Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), 2015, 123-129.
3. Z. Sun, G. Guo and J. Zhang, Exploiting Implicit Item Relationships for Recommender Systems, in Proceedings of the 23rd International Conference on User Modeling, Adaptation and Personalization (UMAP), 2015.
### GPL License
LibRec is [free software](http://www.gnu.org/philosophy/free-sw.html): you can redistribute it and/or modify it under the terms of the [GNU General Public License (GPL)](http://www.gnu.org/licenses/gpl.html) as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LibRec is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with LibRec. If not, see http://www.gnu.org/licenses/.
没有合适的资源?快使用搜索试试~ 我知道了~
资源推荐
资源详情
资源评论
收起资源包目录
推荐算法开源代码库java版 (976个子文件)
3.0.0 181B
3.0.0 41B
_SUCCESS 0B
amazon.arff 40.95MB
digital_music.arff 587KB
ratings.arff 504KB
music.arff 325KB
dc_dense.arff 240KB
dc_dense_rating.arff 87KB
musical_instruments.arff 60KB
hft.arff 1KB
small_set.arff 617B
data.arff 480B
test.arff 348B
librec.cmd 4KB
config 308B
.part-00001.crc 12B
.part-00000.crc 12B
stop-word-list.csv 669B
description 73B
exclude 250B
FETCH_HEAD 97B
dataModel.fxml 7KB
evaluator.fxml 5KB
filter.fxml 4KB
main.fxml 4KB
recommender.fxml 3KB
similarity.fxml 2KB
output_1.fxml 2KB
.gitignore 329B
HEAD 181B
HEAD 181B
HEAD 31B
HEAD 22B
pack-2dfc0910685e2a7ab23795753fed01f8efd0c0c1.idx 339KB
librec-spark.iml 29KB
librec-gui.iml 13KB
librec-core.iml 13KB
librec.iml 574B
index 86KB
fastutil-8.2.1.jar 17.93MB
guava-15.0.jar 2.07MB
librec-core-3.0.0.jar 744KB
hamcrest-all-1.1.jar 529KB
log4j-1.2.17.jar 478KB
junit-4.12.jar 308KB
commons-lang-2.6.jar 278KB
commons-codec-1.10.jar 278KB
librec-spark-3.0.0.jar 207KB
commons-io-2.4.jar 181KB
librec-gui-3.0.0.jar 131KB
okio-1.14.1.jar 84KB
commons-logging-1.2.jar 60KB
commons-cli-1.3.jar 52KB
JobConf.java 75KB
IOUtil.java 43KB
SparseTensor.java 34KB
UserKNNTestCase.java 28KB
FileUtil.java 28KB
EFMRecommender.java 24KB
NMFItemItemRecommender.java 23KB
Randoms.java 23KB
USGRecommender.java 21KB
TimeSVDRecommender.java 19KB
ReMFRecommender.java 19KB
RecommenderJob.java 18KB
DataFrame.java 18KB
SVD.java 18KB
ConvMFDocumentDataSetIterator.java 18KB
BNPPFRecommeder.java 17KB
TextDataModelTestCase.java 17KB
TensorRecommender.java 17KB
DenseMatrix.java 17KB
RatioDataSplitter.java 17KB
Configuration.java 17KB
StringUtil.java 17KB
IRRGRecommender.java 17KB
PNMFRecommender.java 16KB
SequentialAccessSparseMatrix.java 15KB
Lists.java 15KB
RBMRecommender.java 15KB
TrustSVDRecommender.java 15KB
ArffDataConvertor.java 15KB
DenseMatrixTestCase.java 14KB
OrderedIntDoubleMapping.java 14KB
SparseStringMatrix.java 14KB
IncrementalSimpleMFRecommender.java 14KB
ConvMFRecommender.java 13KB
BUCMRecommender.java 13KB
ItemBigramRecommender.java 13KB
TrustMFRecommender.java 13KB
IncrementalBiasedMFRecommender.java 13KB
HFTRecommender.java 13KB
RankGeoFMRecommender.java 13KB
BHFreeRecommender.java 12KB
ArffDataModelTestCase.java 12KB
DLambdaFMRecommender.java 12KB
VectorBasedSequentialSparseVector.java 12KB
SBPRRecommender.java 12KB
LLORMARecommender.java 12KB
共 976 条
- 1
- 2
- 3
- 4
- 5
- 6
- 10
资源评论
- xiaozhu26232019-04-22不好。下了白下载,浪费我9个积分
- 落墨庐州2018-12-25好的好好好
qq_29172981
- 粉丝: 0
- 资源: 14
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功