An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2022
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916299381145600 |
|---|---|
| author | Maurera, Fernando Benjamín Pérez Dacrema, Maurizio Ferrari Cremonesi, Paolo |
| author_facet | Maurera, Fernando Benjamín Pérez Dacrema, Maurizio Ferrari Cremonesi, Paolo |
| contents | This work explores the reproducibility of CFGAN. CFGAN and its family of models (TagRec, MTPR, and CRGAN) learn to generate personalized and fake-but-realistic rankings of preferences for top-N recommendations by using previous interactions. This work successfully replicates the results published in the original paper and discusses the impact of certain differences between the CFGAN framework and the model used in the original evaluation. The absence of random noise and the use of real user profiles as condition vectors leaves the generator prone to learn a degenerate solution in which the output vector is identical to the input vector, therefore, behaving essentially as a simple autoencoder. The work further expands the experimental analysis comparing CFGAN against a selection of simple and well-known properly optimized baselines, observing that CFGAN is not consistently competitive against them despite its high computational cost. To ensure the reproducibility of these analyses, this work describes the experimental methodology and publishes all datasets and source code. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2201_01815 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering Maurera, Fernando Benjamín Pérez Dacrema, Maurizio Ferrari Cremonesi, Paolo Information Retrieval Machine Learning This work explores the reproducibility of CFGAN. CFGAN and its family of models (TagRec, MTPR, and CRGAN) learn to generate personalized and fake-but-realistic rankings of preferences for top-N recommendations by using previous interactions. This work successfully replicates the results published in the original paper and discusses the impact of certain differences between the CFGAN framework and the model used in the original evaluation. The absence of random noise and the use of real user profiles as condition vectors leaves the generator prone to learn a degenerate solution in which the output vector is identical to the input vector, therefore, behaving essentially as a simple autoencoder. The work further expands the experimental analysis comparing CFGAN against a selection of simple and well-known properly optimized baselines, observing that CFGAN is not consistently competitive against them despite its high computational cost. To ensure the reproducibility of these analyses, this work describes the experimental methodology and publishes all datasets and source code. |
| title | An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2201.01815 |