Docs update (#67)

* refactor: irec rename * docs: introduction text fix * framework description * fix: docs and readme minor changes
irec-org · Feb 9, 2022 · aa3b6e5 · aa3b6e5
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diff --git a/README.md b/README.md
@@ -22,11 +22,20 @@
 
 </div>
 
+<div align="center">
+	<a href="https://irec-org.github.io/irec/">Introduction</a>
+  <span> • </span>
+    	<a href="https://irec-org.github.io/irec/guide/install_irec/">Install</a>
+  <span> • </span>
+       	<a href="https://irec-org.github.io/irec/guide/quick_start/">Quick Start</a>
+  <p></p>
+</div>
+
 ## Introduction
 
 > For Python >= 3.8
 
-Reinforcement Learning Recommender Systems Framework
+Interactive Recommender Systems Framework
 
 Main features:
 
@@ -85,9 +94,9 @@ Check this example of a execution using the example application:
 <!--:TODO:-->
 ## Datasets
 
-Our framework has the ability to use any type of dataset, as long as it is suitable for the recommendation scenario and is formatted correctly. Below we list some of the datasets tested and used in some of our experiments.
+Our framework has the ability to use any type of dataset, as long as it is suitable for the recommendation domain and is formatted correctly. Below we list some datasets tested and used in some of our experiments.
 
-| Dataset | Scenery  | Sparsity | Link
+| Dataset | Domain  | Sparsity | Link
 | :---: | --- | :---: | :---: |
 MovieLens 100k |  Movies | 93.69% | [Link](https://grouplens.org/datasets/movielens/100k/)
 MovieLens 1M | Movies | 95.80% | [Link](https://grouplens.org/datasets/movielens/1m/)
@@ -150,7 +159,7 @@ The [recommender metrics](https://github.com/irec-org/irec/blob/master/irec/metr
 
 ## Configuration Files
 
-IREC has some configuration files to define an experiment, such as dataset settings, agents, policies and evaluation metrics. Below we present brief examples about each of the files available in this framework.
+iRec has some configuration files to define an experiment, such as dataset settings, agents, policies and evaluation metrics. Below we present brief examples about each of the files available in this framework.
 
 For more details on configuration files, go to [**configuration_files**](tutorials/configuration_files.ipynb)
 
@@ -257,7 +266,7 @@ tex_dir: tex/
 
 ## API
 
-For writing anything new to the library (e.g., value function, agent, etc) read the documentation.
+For writing anything new to the library (e.g., value function, agent, etc) read the [documentation](https://irec-org.github.io/irec/).
 
 ## Contributing
 

diff --git a/docs/guide/introduction.md b/docs/guide/introduction.md
@@ -1,7 +1,7 @@
 
 # Introduction
 
-This is a specialized library containing Multi-Armed Bandit, Active Learning and others methods. A full environment to code yours Reinforcement Learning Recommender Systems. Our goal is to encourage the evaluation of reproducible offline experiments by providing simple building blocks for running robust experiments and an extremely intuitive platform. Our framework can be used to share environments reference SRs and reusable implementations of reference RS agents. Thus, we built a complete structure, from data entry and manipulation to the evaluation of the results obtained, using several evaluation metrics that are perfectly capable of measuring the quality of the recommendation.
+This is a specialized library containing Multi-Armed Bandit, Active Learning and others methods. A full environment to code yours Reinforcement Learning Recommender Systems. Our goal is to encourage the evaluation of reproducible offline experiments by providing simple building blocks for running robust experiments and an extremely intuitive platform. Our framework can be used to share environments, baseline recommendation systems (RSs) and reusable implementations of baseline RS agents. Thus, we built a complete structure, from data entry and manipulation to the evaluation of the results obtained, using several evaluation metrics that are perfectly capable of measuring the quality of the recommendation.
 
 Unlike existing frameworks, our structure has the most recent and relevant RL algorithms published so far, in addition to providing different forms and evaluation metrics, generalizable in different situations and ideal for the scenario of recommendation systems.
 

diff --git a/docs/index.md b/docs/index.md
@@ -1,6 +1,6 @@
-# Welcome to IREC documentation!
+# Welcome to iRec documentation!
 
-This is a specialized library containing Multi-Armed Bandit, Active Learning and others methods. A full environment to code yours Reinforcement Learning Recommender Systems. Our goal is to encourage the evaluation of reproducible offline experiments by providing simple building blocks for running robust experiments and an extremely intuitive platform. Our framework can be used to share environments reference SRs and reusable implementations of reference RS agents. Thus, we built a complete structure, from data entry and manipulation to the evaluation of the results obtained, using several evaluation metrics that are perfectly capable of measuring the quality of the recommendation.
+This is a specialized library containing Multi-Armed Bandit, Active Learning and others methods. A full environment to code yours Reinforcement Learning Recommender Systems. Our goal is to encourage the evaluation of reproducible offline experiments by providing simple building blocks for running robust experiments and an extremely intuitive platform. Our framework can be used to share environments reference RSs and reusable implementations of reference RS agents. Thus, we built a complete structure, from data entry and manipulation to the evaluation of the results obtained, using several evaluation metrics that are perfectly capable of measuring the quality of the recommendation.
 
 Unlike existing frameworks, our structure has the most recent and relevant RL algorithms published so far, in addition to providing different forms and evaluation metrics, generalizable in different situations and ideal for the scenario of recommendation systems.
 
@@ -29,7 +29,7 @@ Also, we provide a amazing application created using the iRec library (under the
 ## GET STARTED
 
 * [Introduction](guide/introduction.md)
-* [Install IREC](guide/install_irec.md)
+* [Install iRec](guide/install_irec.md)
 * [Quick Start](guide/quick_start.md)
 * [Release Notes](guide/release_notes.md)
 

diff --git a/docs/modules/evaluation_policies.md b/docs/modules/evaluation_policies.md
@@ -1,6 +1,6 @@
 # Evaluation Policies
 
-The Evaluation Policies supported by IREC are listed below.
+The Evaluation Policies supported by iRec are listed below.
 
 | [Evaluation Policy](https://github.com/irec-org/irec/blob/master/irec/evaluation_policies/EvaluationPolicy.py) | Description
 | :---: | :--- |

diff --git a/docs/modules/metric_evaluators.md b/docs/modules/metric_evaluators.md
@@ -1,6 +1,6 @@
 # Metric Evaluators
 
-The Metric Evaluators supported by IREC are listed below.
+The Metric Evaluators supported by iRec are listed below.
 
 | [Metric Evaluator](https://github.com/irec-org/irec/blob/master/irec/metric_evaluators/MetricEvaluator.py) | Description
 | :---: | :--- |

diff --git a/docs/modules/metrics.md b/docs/modules/metrics.md
@@ -1,6 +1,6 @@
 # Metrics
 
-The recommender metrics supported by IREC are listed below.
+The recommender metrics supported by iRec are listed below.
 
 | Metric | Reference | Description
 | :---: | --- | :--- |

diff --git a/docs/modules/models.md b/docs/modules/models.md
@@ -1,6 +1,6 @@
 # Models
 
-The recommender models supported by IREC are listed below.
+The recommender models supported by iRec are listed below.
 
 | Year | Model  | Paper | Description
 | :---: | --- | :---: | :--- |

diff --git a/docs/run_exp/configuration_files.md b/docs/run_exp/configuration_files.md
@@ -1,6 +1,6 @@
 # Configuration Files
 
-IREC has some configuration files to define an experiment, such as dataset settings, agents, policies and evaluation metrics. Below we present brief examples about each of the files available in this framework.
+iRec has some configuration files to define an experiment, such as dataset settings, agents, policies and evaluation metrics. Below we present brief examples about each of the files available in this framework.
 
 For more details on configuration files, go to [**configuration_files**](tutorials/configuration_files.ipynb)
 

diff --git a/mkdocs.yml b/mkdocs.yml
@@ -24,7 +24,7 @@ theme:
 nav:
   - <span style="color:rgb(51,177,240)">GET STARTED</span>:
       Introduction: guide/introduction.md
-      Install IREC: guide/install_irec.md
+      Install iRec: guide/install_irec.md
       Quick Start: guide/quick_start.md
       Release Notes: guide/release_notes.md
 

diff --git a/setup.cfg b/setup.cfg
@@ -1,6 +1,6 @@
 [metadata]
 name = irec
-version = 1.2.6
+version = 1.2.5
 author = Heitor Werneck
 author_email = werneck@aluno.ufsj.edu.br
 description = Reinforcement Learning Recommender Systems Framework