Merge pull request #183 from jrzaurin/wide_deep_recsys

Wide deep recsys

Author: jrzaurin

.gitignore

@@ -21,6 +21,7 @@ tmp_dir/
 weights/
 pretrained_weights/
 model_weights/
+prepared_data/
 
 # Unit Tests/Coverage
 .coverage

CITATION.cff

@@ -0,0 +1,34 @@
+cff-version: "1.2.0"
+authors:
+- family-names: Zaurin
+  given-names: Javier Rodriguez
+  orcid: "https://orcid.org/0000-0002-1082-1107"
+- family-names: Mulinka
+  given-names: Pavol
+  orcid: "https://orcid.org/0000-0002-9394-8794"
+doi: 10.5281/zenodo.7908172
+message: If you use this software, please cite our article in the
+  Journal of Open Source Software.
+preferred-citation:
+  authors:
+  - family-names: Zaurin
+    given-names: Javier Rodriguez
+    orcid: "https://orcid.org/0000-0002-1082-1107"
+  - family-names: Mulinka
+    given-names: Pavol
+    orcid: "https://orcid.org/0000-0002-9394-8794"
+  date-published: 2023-06-24
+  doi: 10.21105/joss.05027
+  issn: 2475-9066
+  issue: 86
+  journal: Journal of Open Source Software
+  publisher:
+    name: Open Journals
+  start: 5027
+  title: "pytorch-widedeep: A flexible package for multimodal deep
+    learning"
+  type: article
+  url: "https://joss.theoj.org/papers/10.21105/joss.05027"
+  volume: 8
+title: "pytorch-widedeep: A flexible package for multimodal deep
+  learning"

README.md

@@ -12,6 +12,7 @@
 [![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://github.com/jrzaurin/pytorch-widedeep/graphs/commit-activity)
 [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/jrzaurin/pytorch-widedeep/issues)
 [![Slack](https://img.shields.io/badge/slack-chat-green.svg?logo=slack)](https://join.slack.com/t/pytorch-widedeep/shared_invite/zt-soss7stf-iXpVuLeKZz8lGTnxxtHtTw)
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.05027/status.svg)](https://doi.org/10.21105/joss.05027)
 
 # pytorch-widedeep
 
@@ -38,6 +39,9 @@ The content of this document is organized as follows:
     - [How to Contribute](#how-to-contribute)
     - [Acknowledgments](#acknowledgments)
     - [License](#license)
+    - [Cite](#cite)
+      - [BibTex](#bibtex)
+      - [APA](#apa)
 
 ### Introduction
 
@@ -82,7 +86,7 @@ without a ``deephead`` component can be formulated as:
 
 
 Where σ is the sigmoid function, *'W'* are the weight matrices applied to the wide model and to the final
-activations of the deep models, *'a'* are these final activations, 
+activations of the deep models, *'a'* are these final activations,
 φ(x) are the cross product transformations of the original features *'x'*, and
 , and *'b'* is the bias term.
 In case you are wondering what are *"cross product transformations"*, here is
@@ -331,4 +335,31 @@ Vision](https://www.pyimagesearch.com/deep-learning-computer-vision-python-book/
 This work is dual-licensed under Apache 2.0 and MIT (or any later version).
 You can choose between one of them if you use this work.
 
-`SPDX-License-Identifier: Apache-2.0 AND MIT`
+`SPDX-License-Identifier: Apache-2.0 AND MIT`
+
+### Cite
+
+#### BibTex
+
+```
+@article{Zaurin_pytorch-widedeep_A_flexible_2023,
+author = {Zaurin, Javier Rodriguez and Mulinka, Pavol},
+doi = {10.21105/joss.05027},
+journal = {Journal of Open Source Software},
+month = jun,
+number = {86},
+pages = {5027},
+title = {{pytorch-widedeep: A flexible package for multimodal deep learning}},
+url = {https://joss.theoj.org/papers/10.21105/joss.05027},
+volume = {8},
+year = {2023}
+}
+```
+
+#### APA
+
+```
+Zaurin, J. R., & Mulinka, P. (2023). pytorch-widedeep: A flexible package for
+multimodal deep learning. Journal of Open Source Software, 8(86), 5027.
+https://doi.org/10.21105/joss.05027
+```

VERSION

@@ -1 +1 @@
-1.3.0
+1.3.1