If you publish research that uses RankSys, please cite the papers listed here that best match the parts of the framework that you used.
RankSys is a new framework for the implementation and evaluation of recommendation algorithms and techniques that has resulted from a line of research work that is currently documented in several publications (see above) and a PhD thesis (Vargas 2015). While it is envisioned as a framework for the generic experimentation of recommendation technologies, it includes substantial support focusing on the evaluation and enhancement of novelty and diversity. RankSys derives its name from explicitly targeting the ranking task problem, rather than rating prediction. This decision is reflected in the design of the different core interfaces and components of the framework.
The framework has been programmed with Java 8, which is the most recent version of the popular programming language. We take advantage of many of the new features of the language, such as the use of lambda functions, Stream's and facilities for automatic parallelization of the code. The code licensed under the GPL V3, which allows the free use, study, distribution and modification of the software as long as derived works are distributed under the same license.
The publicly available version of this framework (v0.3) includes implementations of several collaborative filtering recommendation algorithms as well as a wide variety of novelty and diversity metrics and re-ranking techniques. The modules published to date are the following:
- RankSys-core, which contains the common and auxiliary classes of the framework.
- RankSys-fast, which provides support for fast and efficient implementation of data structures and algorithms.
- RankSys-metrics, which contains the interfaces and common components for defining metrics.
- RankSys-rec, which provides support for generating recommendation lists.
- RankSys-nn, which implements nearest neighbors recommendation algorithms.
- RankSys-mf, which implements matrix factorization recommendation algorithms.
- RankSys-novdiv, which provides common resources for novelty and diversity metrics and enhancement techniques.
- RankSys-novelty, which contains novelty metrics and enhancement techniques
- RankSys-diversity, which contains diversity metrics and enhancement techniques.
- RankSys-examples, which provides examples of usage of the previous modules.
If you want to know more, the wiki provides a high-level description of the different components of the current release of the software.
If you want to include RankSys in an existing Maven project, first include the following repository:
<repository>
<id>jitpack.io</id>
<url>https://jitpack.io</url>
</repository>
and, from the following dependencies, those you require:
<dependency>
<groupId>com.github.ir-uam.RankSys</groupId>
<artifactId>RankSys-core</artifactId>
<version>0.3</version>
</dependency>
<dependency>
<groupId>com.github.ir-uam.RankSys</groupId>
<artifactId>RankSys-fast</artifactId>
<version>0.3</version>
</dependency>
<dependency>
<groupId>com.github.ir-uam.RankSys</groupId>
<artifactId>RankSys-metrics</artifactId>
<version>0.3</version>
</dependency>
<dependency>
<groupId>com.github.ir-uam.RankSys</groupId>
<artifactId>RankSys-rec</artifactId>
<version>0.3</version>
</dependency>
<dependency>
<groupId>com.github.ir-uam.RankSys</groupId>
<artifactId>RankSys-nn</artifactId>
<version>0.3</version>
</dependency>
<dependency>
<groupId>com.github.ir-uam.RankSys</groupId>
<artifactId>RankSys-mf</artifactId>
<version>0.3</version>
</dependency>
<dependency>
<groupId>com.github.ir-uam.RankSys</groupId>
<artifactId>RankSys-novdiv</artifactId>
<version>0.3</version>
</dependency>
<dependency>
<groupId>com.github.ir-uam.RankSys</groupId>
<artifactId>RankSys-novelty</artifactId>
<version>0.3</version>
</dependency>
<dependency>
<groupId>com.github.ir-uam.RankSys</groupId>
<artifactId>RankSys-diversity</artifactId>
<version>0.3</version>
</dependency>