Update docs

README.md

@@ -52,7 +52,7 @@ Currently, we are supporting Python 3. There are several ways to install Cornac:
  pip3 install git+https://github.com/PreferredAI/cornac.git
  ```
 
-**Note:** 
+**Note:**
 
 Additional dependencies required by models are listed [here](README.md#Models).
 
@@ -143,7 +143,8 @@ The table below lists the recommendation models/algorithms featured in Cornac. E
 
 | Year | Model and Paper | Type | Environment | Example |
 | :--: | --------------- | :--: | :---------: | :-----: |
-| 2024 | [Hypergraphs with Attention on Reviews (HypAR)](cornac/models/hypar), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.hypar.recom_hypar), [paper](https://doi.org/10.1007/978-3-031-56027-9_14)| Hybrid / Sentiment / Explainable | [requirements](cornac/models/hypar/requirements_cu116.txt), CPU / GPU | [quick-start](https://github.com/PreferredAI/HypAR)
+| 2024 | [Comparative Aspects and Opinions Ranking for Recommendation Explanations (Companion)](cornac/models/companion), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.companion.recom_companion) | Hybrid / Sentiment / Explainable | CPU | [quick-start](examples/companion_example.py)
+| | [Hypergraphs with Attention on Reviews (HypAR)](cornac/models/hypar), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.hypar.recom_hypar), [paper](https://doi.org/10.1007/978-3-031-56027-9_14)| Hybrid / Sentiment / Explainable | [requirements](cornac/models/hypar/requirements_cu116.txt), CPU / GPU | [quick-start](https://github.com/PreferredAI/HypAR)
 | 2022 | [Disentangled Multimodal Representation Learning for Recommendation (DMRL)](cornac/models/dmrl), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.dmrl.recom_dmrl), [paper](https://arxiv.org/pdf/2203.05406.pdf) | Content-Based / Text & Image | [requirements](cornac/models/dmrl/requirements.txt), CPU / GPU | [quick-start](examples/dmrl_example.py)
 | 2021 | [Bilateral Variational Autoencoder for Collaborative Filtering (BiVAECF)](cornac/models/bivaecf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.bivaecf.recom_bivaecf), [paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441759) | Collaborative Filtering / Content-Based | [requirements](cornac/models/bivaecf/requirements.txt), CPU / GPU | [quick-start](https://github.com/PreferredAI/bi-vae), [deep-dive](https://github.com/recommenders-team/recommenders/blob/main/examples/02_model_collaborative_filtering/cornac_bivae_deep_dive.ipynb)
 | | [Causal Inference for Visual Debiasing in Visually-Aware Recommendation (CausalRec)](cornac/models/causalrec), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.causalrec.recom_causalrec), [paper](https://arxiv.org/abs/2107.02390) | Content-Based / Image | [requirements](cornac/models/causalrec/requirements.txt), CPU / GPU | [quick-start](examples/causalrec_clothing.py)

docs/source/api_ref/models.rst

@@ -11,6 +11,11 @@ Recommender (Generic Class)
 .. automodule:: cornac.models.recommender
  :members:
 
+Comparative Aspects and Opinions Ranking for Recommendation Explanations (Companion)
+-------------------------------------------------------------------------
+.. automodule:: cornac.models.companion.recom_companion
+ :members:
+
 Disentangled Multimodal Representation Learning for Recommendation (DMRL)
 -------------------------------------------------------------------------
 .. automodule:: cornac.models.dmrl.recom_dmrl
@@ -307,7 +312,7 @@ Social Recommendation using PMF (SoRec)
 User K-Nearest-Neighbors (UserKNN)
 ----------------------------------
 .. autoclass:: cornac.models.knn.recom_knn.UserKNN
- :members: 
+ :members:
 
 Weighted Matrix Factorization (WMF)
 -----------------------------------

examples/README.md

@@ -66,6 +66,8 @@
 
 [mter_example.py](mter_example.py) - Multi-Task Explainable Recommendation (MTER) with Amazon Toy and Games dataset.
 
+[companion_example.py](companion_example.py) - Comparative Aspects and Opinions Ranking for Recommendation Explanations (Companion) with Amazon Toy and Games dataset.
+
 ### Image
 
 [causalrec_clothing.py](causalrec_clothing.py) - CausalRec with Clothing dataset.