RecQ

Released by School of Software Engineering, Chongqing University ##Introduction## RecQ is a Python library for recommender systems (Python 2.7.x). It implements a suit of state-of-the-art recommendations. To run RecQ easily (no need to setup packages used in RecQ one by one), the leading open data science platform Anaconda is strongly recommended. It integrates Python interpreter, common scientific computing libraries (such as Numpy, Pandas, and Matplotlib), and package manager, all of them make it a perfect tool for data science researcher. ##Architecture of RecQ##

To design it exquisitely, we brought some thoughts from another recommender system library LibRec, which is implemented with Java.

##Features##

• Cross-platform: as a Python software, RecQ can be easily deployed and executed in any platforms, including MS Windows, Linux and Mac OS.
• Fast execution: RecQ is based on the fast scientific computing libraries such as Numpy and some light common data structures, which make it runs much faster than other libraries based on Python.
• Easy configuration: RecQ configs recommenders using a configuration file.
• Easy expansion: RecQ provides a set of well-designed recommendation interfaces by which new algorithms can be easily implemented.

##How to Run it##

• 1.Configure the xx.conf file in the directory named config. (xx is the name of the algorithm you want to run)
• 2.Run the main.py in the project, and then input following the prompt.

##How to Configure it## ###Essential Options

<td>Set the path to input dataset. Format: each row separated by empty, tab or comma symbol. </td>

<td>Set the path to input social dataset. Format: each row separated by empty, tab or comma symbol. </td>

<td>-columns: (user, item, rating) columns of rating data are used;
-header: to skip the first head line when reading data<br>
</td>

<td>-columns: (trustor, trustee, weight) columns of social data are used;
-header: to skip the first head line when reading data<br>
</td>

<td>Set the recommender to use. <br>
</td>

<td>Main option: -testSet;<br>
-testSet -f path/to/test/file;<br>
</td>

<td>Main option: whether to output recommendation results<br>
-dir path: the directory path of output results.
</td>

Entry	Example	Description
ratings	D:/MovieLens/100K.txt
social	D:/MovieLens/trusts.txt
ratings.setup	-columns 0 1 2
social.setup	-columns 0 1 2
recommender	UserKNN/ItemKNN/SlopeOne/etc.
evaluation.setup	../dataset/FilmTrust/testset.txt
item.ranking	off -topN -1 <td>Main option: whether to do item ranking<br> -topN: the length of the recommendation list for item recommendation, default -1 for full list; <br> </td>
output.setup	on -dir ./Results/

###Memory-based Options

similarity	pcc/cos	Set the similarity method to use. Options: PCC, COS;
num.shrinkage	25	Set the shrinkage parameter to devalue similarity value. -1: to disable simialrity shrinkage.
num.neighbors	30	Set the number of neighbors used for KNN-based algorithms such as UserKNN, ItemKNN.

###Model-based Options

num.factors	5/10/20/number	Set the number of latent factors
num.max.iter	100/200/number	Set the maximum number of iterations for iterative recommendation algorithms.
learnRate	-init 0.01 -max 1	-init initial learning rate for iterative recommendation algorithms; -max: maximum learning rate (default 1);
reg.lambda	-u 0.05 -i 0.05 -b 0.1 -s 0.1	-u: user regularizaiton; -i: item regularization; -b: bias regularizaiton; -s: social regularization

##How to extend it## * 1.Make your new algorithm generalize the proper base class. * 2.Rewrite some of the following functions as needed. - **readConfiguration()** - **printAlgorConfig()** - **initModel()** - **buildModel()** - **saveModel()** - **loadModel()** - **predict()**