Release/0.6.0 (#129)

Author: feldlime

.github/workflows/test.yml

@@ -71,4 +71,5 @@ jobs:
         run: make test
 
       - name: Upload coverage
+        if: matrix.python-version == '3.9'
         uses: codecov/codecov-action@v3

CHANGELOG.md

@@ -6,7 +6,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 
-## [0.6.0] - Unreleased
+## [0.6.0] - 13.05.2024
 
 ### Added 
 - Warm users/items support in `Dataset` ([#77](https://github.com/MobileTeleSystems/RecTools/pull/77))

README.md

@@ -114,7 +114,7 @@ The table below lists recommender models that are available in RecTools.
 - For getting recommendations `filter_viewed` and `items_to_recommend` options are available
 - For item-to-item recommendations use `recommend_to_items` method
 - For feeding user/item features to model just specify dataframes when constructing `Dataset`. [Check our tutorial](examples/4_dataset_with_features.ipynb)
-
+- For warm / cold inference just provide all required ids in `users` or `target_items` parameters of `recommend` or `recommend_to_items` methods and make sure you have features in the dataset for warm users/items. **Nothing else is needed, everything works out of the box.** Check [documentation](https://rectools.readthedocs.io/en/stable/features.html#models) to see which models support this scenarios.
 
 ## Contribution
 [Contributing guide](CONTRIBUTING.rst)

docs/source/features.rst

@@ -47,11 +47,27 @@ Item Features
 This table stores data about items.
 It might include category, price or any other features which may prove to be important for a recommender model.
 
+Hot, warm, cold
+~~~~~~~~~~~~~~~
+There is a concept of a temperature we're using for users and items:
+
+* **hot** - the ones that are present in interactions used for training (they may or may not have features);
+* **warm** - the ones that are not in interactions, but have some features;
+* **cold** - the ones we don't know anything about (they are not in interactions and don't have any features).
+
+All the models are able to generate recommendations for the *hot* users (items).  
+But as for warm and cold ones, there may be all possible combinations (neither of them, only cold, only warm, both).  
+The important thing is that if model is able to recommend for cold users (items), but not for warm ones (see table below), 
+it is still able to recommend for warm ones, but they will be considered as cold (no personalisation should be expected).
+
 All of the above concepts are combined in `Dataset`.
 `Dataset` is used to build recommendation models and infer recommendations.
 
 .. include:: dataset.rst
 
+.. include:: models.rst
+
+What are you waiting for? Train and apply them!
 
 Recommendation Table
 ~~~~~~~~~~~~~~~~~~~~
@@ -60,11 +76,6 @@ It has a fixed set of columns, though they are different for i2i and u2i recomme
 Recommendation table can also be used for calculation of metrics.
 
 
-.. include:: models.rst
-
-What are you waiting for? Train and apply them!
-
-
 .. include:: metrics.rst
 
 Oops, yeah, can't forget about them.

docs/source/models.rst

@@ -8,6 +8,30 @@ Models
 Details of RecTools Models
 --------------------------
 
+
++-----------------------------+-------------------+---------------------+---------------------+
+| Model                       | Supports features | Recommends for warm | Recommends for cold |
++=============================+===================+=====================+=====================+
+| DSSMModel                   | Yes               | Yes                 | No                  |
++-----------------------------+-------------------+---------------------+---------------------+
+| EASEModel                   | No                | No                  | No                  |
++-----------------------------+-------------------+---------------------+---------------------+
+| ImplicitALSWrapperModel     | Yes               | No                  | No                  |
++-----------------------------+-------------------+---------------------+---------------------+
+| ImplicitItemKNNWrapperModel | No                | No                  | No                  |
++-----------------------------+-------------------+---------------------+---------------------+
+| LightFMWrapperModel         | Yes               | Yes                 | Yes                 |
++-----------------------------+-------------------+---------------------+---------------------+
+| PopularModel                | No                | No                  | Yes                 |
++-----------------------------+-------------------+---------------------+---------------------+
+| PopularInCategoryModel      | No                | No                  | Yes                 |
++-----------------------------+-------------------+---------------------+---------------------+
+| PureSVDModel                | No                | No                  | No                  |
++-----------------------------+-------------------+---------------------+---------------------+
+| RandomModel                 | No                | No                  | Yes                 |
++-----------------------------+-------------------+---------------------+---------------------+
+
+
 See the API documentation for further details on Models:
 
 .. currentmodule:: rectools

examples/1_simple_example.ipynb

@@ -733,6 +733,13 @@
     "user_recos.sort_values(\"rank\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is the simple example, we only used ratings to train the model and we only prepared recommendations for users who have rated movies before. But some models allow you to use explicit features, e.g. user age or item genre. And some models allow you to generate recommendations for users that have not rated any movies before. See [documentation](https://rectools.readthedocs.io/en/stable/features.html#models) for the details."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -743,9 +750,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "rect",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "rect"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -757,7 +764,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.10"
+   "version": "3.9.13"
   },
   "toc": {
    "base_numbering": 1,

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "RecTools"
-version = "0.5.0"
+version = "0.6.0"
 description = "An easy-to-use Python library for building recommendation systems"
 license = "Apache-2.0"
 authors = [

rectools/version.py

@@ -1 +1 @@
-VERSION = "0.5.0"
+VERSION = "0.6.0"