Authors:
(1) Daniele Malitesta, Politecnico di Bari, Italy and daniele.malitesta@poliba.it with Corresponding authors: Daniele Malitesta (daniele.malitesta@poliba.it) and Giuseppe Gassi (g.gassi@studenti.poliba.it);
(2) Giuseppe Gassi, Politecnico di Bari, Italy and g.gassi@studenti.poliba.it with Corresponding authors: Daniele Malitesta (daniele.malitesta@poliba.it) and Giuseppe Gassi (g.gassi@studenti.poliba.it);
(3) Claudio Pomo, Politecnico di Bari, Italy and claudio.pomo@poliba.it;
(4) Tommaso Di Noia, Politecnico di Bari, Italy and tommaso.dinoia@poliba.it. Abstract and 1 Introduction and Motivation 2 Architecture and 2.1 Dataset 2.2 Extractor 2.3 Runner 3 Extraction Pipeline 4 Ducho as Docker Application 5 Demonstrations and 5.1 Demo 1: visual + textual items features 5.2 Demo 2: audio + textual items features 5.3 Demo 3: textual items/interactions features 6 Conclusion and Future Work, Acknowledgments and References ABSTRACT In multimodal-aware recommendation, the extraction of meaningful multimodal features is at the basis of high-quality recommendations. Generally, each recommendation framework implements its multimodal extraction procedures with specific strategies and tools. This is limiting for two reasons: (i) different extraction strategies do not ease the interdependence among multimodal recommendation frameworks; thus, they cannot be efficiently and fairly compared; (ii) given the large plethora of pre-trained deep learning models made available by different open source tools, model designers do not have access to shared interfaces to extract features. Motivated by the outlined aspects, we propose Ducho, a unified framework for the extraction of multimodal features in recommendation. By integrating three widely-adopted deep learning libraries as backends, namely, TensorFlow, PyTorch, and Transformers, we provide a shared interface to extract and process features where each backend’s specific methods are abstracted to the end user. Noteworthy, the extraction pipeline is easily configurable with a YAML-based file where the user can specify, for each modality, the list of models (and their specific backends/parameters) to perform the extraction. Finally, to make Ducho accessible to the community, we build a public Docker image equipped with a ready-to-use CUDA environment and propose three demos to test its functionalities for different scenarios and tasks. The GitHub repository and the documentation are accessible at this link: https://github.com/sisinflab/Ducho. 1 INTRODUCTION AND MOTIVATION With the advent of the digital era and the Internet, numerous online services have emerged, including platforms for e-commerce, media streaming, and social networks. The vast majority of such websites rely on recommendation algorithms to provide users with a personalized surfing experience. In specific domains such as fashion [3], music [6], food [5], and micro-video [8] recommendation, recommender systems have demonstrated to be effectively supported in their decision-making process by all types of multimodal data sources the users usually interact with (e.g., product images and descriptions, users’ reviews, audio tracks). The literature refers to multimodal-aware recommender systems (MRSs) as the family of recommendation algorithms leveraging multimodal (i.e., audio, visual, textual) content data to augment the representation of items, thus tackling issues in the field such as the sparsity of the user-item matrix and the inexplicable nature of users’ actions (e.g., clicks, views) on online platforms which may not always be easy to profile for the recommendation algorithms. Despite being the initial stage of any multimodal recommendation pipeline, the extraction of meaningful multimodal features is paramount in delivering high-quality recommendations [2]. However, the current practice of employing diverse multimodal extraction procedures in each recommendation framework poses limitations. Firstly, these diverse implementations hinder the interdependence across various multimodal recommendation frameworks, making their fair comparison difficult [4]. Secondly, despite the availability of numerous pre-trained deep learning models in popular open source libraries, the lack of shared interfaces for feature extraction across them represents a challenge for model designers. To address these shortcomings, we propose Ducho, a unified framework designed to streamline the extraction of multimodal features for recommendation systems. By integrating widely-adopted deep learning libraries as backends such as TensorFlow, PyTorch, and Transformers, we establish a shared interface that empowers users to extract and process audio, visual, and textual features from both items and user-item interactions (see Table 1). This abstraction allows to leverage methods from each backend without being encumbered by the specific implementation that backend poses. A notable feature of our framework lays in its easily configurable extraction pipeline, which can be personalized using a YAML-based file. Users can specify the desired models, their respective backends, and models’ parameters (such as the extraction layer). By looking at the related literature, the most similar application to Ducho is Cornac [7], a framework for multimodal-aware recommendation. For the sake of completeness, we report their main differences. Differently from Cornac, Ducho: (i) is specifically aimed to provide customizable multimodal feature extractions, being completely agnostic to the downstream recommender system that might exploit the extracted features, thus being easily applicable to any model; (ii) provides the user with the possibility to select the deep learning extraction model, its backend, and its output layer; (iii) introduces the audio modality to the modalities set. To foster the adoption of Ducho, we also develop a public Docker image pre-equipped with a ready-to-use CUDA environment[1], and propose three demos to show Ducho’s functionalities. The GitHub repository, which comes with all needed resources is available at: https://github.com/sisinflab/Ducho. This paper is available on arxiv under CC BY 4.0 DEED license. [1] https://hub.docker.com/r/sisinflabpoliba/ducho. Authors: (1) Daniele Malitesta, Politecnico di Bari, Italy and daniele.malitesta@poliba.it with Corresponding authors: Daniele Malitesta (daniele.malitesta@poliba.it) and Giuseppe Gassi (g.gassi@studenti.poliba.it); (2) Giuseppe Gassi, Politecnico di Bari, Italy and g.gassi@studenti.poliba.it with Corresponding authors: Daniele Malitesta (daniele.malitesta@poliba.it) and Giuseppe Gassi (g.gassi@studenti.poliba.it); (3) Claudio Pomo, Politecnico di Bari, Italy and claudio.pomo@poliba.it; (4) Tommaso Di Noia, Politecnico di Bari, Italy and tommaso.dinoia@poliba.it. Authors: Authors: (1) Daniele Malitesta, Politecnico di Bari, Italy and daniele.malitesta@poliba.it with Corresponding authors: Daniele Malitesta (daniele.malitesta@poliba.it) and Giuseppe Gassi (g.gassi@studenti.poliba.it); (2) Giuseppe Gassi, Politecnico di Bari, Italy and g.gassi@studenti.poliba.it with Corresponding authors: Daniele Malitesta (daniele.malitesta@poliba.it) and Giuseppe Gassi (g.gassi@studenti.poliba.it); (3) Claudio Pomo, Politecnico di Bari, Italy and claudio.pomo@poliba.it; (4) Tommaso Di Noia, Politecnico di Bari, Italy and tommaso.dinoia@poliba.it. Abstract and 1 Introduction and Motivation Abstract and 1 Introduction and Motivation 2 Architecture and 2.1 Dataset 2 Architecture and 2.1 Dataset 2.2 Extractor 2.2 Extractor 2.3 Runner 2.3 Runner 3 Extraction Pipeline 3 Extraction Pipeline 4 Ducho as Docker Application 4 Ducho as Docker Application 5 Demonstrations and 5.1 Demo 1: visual + textual items features 5 Demonstrations and 5.1 Demo 1: visual + textual items features 5.2 Demo 2: audio + textual items features 5.2 Demo 2: audio + textual items features 5.3 Demo 3: textual items/interactions features 6 5.3 Demo 3: textual items/interactions features 6 Conclusion and Future Work, Acknowledgments and References Conclusion and Future Work, Acknowledgments and References ABSTRACT In multimodal-aware recommendation, the extraction of meaningful multimodal features is at the basis of high-quality recommendations. Generally, each recommendation framework implements its multimodal extraction procedures with specific strategies and tools. This is limiting for two reasons: (i) different extraction strategies do not ease the interdependence among multimodal recommendation frameworks; thus, they cannot be efficiently and fairly compared; (ii) given the large plethora of pre-trained deep learning models made available by different open source tools, model designers do not have access to shared interfaces to extract features. Motivated by the outlined aspects, we propose Ducho, a unified framework for the extraction of multimodal features in recommendation. By integrating three widely-adopted deep learning libraries as backends, namely, TensorFlow, PyTorch, and Transformers, we provide a shared interface to extract and process features where each backend’s specific methods are abstracted to the end user. Noteworthy, the extraction pipeline is easily configurable with a YAML-based file where the user can specify, for each modality, the list of models (and their specific backends/parameters) to perform the extraction. Finally, to make Ducho accessible to the community, we build a public Docker image equipped with a ready-to-use CUDA environment and propose three demos to test its functionalities for different scenarios and tasks. The GitHub repository and the documentation are accessible at this link: https://github.com/sisinflab/Ducho. 1 INTRODUCTION AND MOTIVATION With the advent of the digital era and the Internet, numerous online services have emerged, including platforms for e-commerce, media streaming, and social networks. The vast majority of such websites rely on recommendation algorithms to provide users with a personalized surfing experience. In specific domains such as fashion [3], music [6], food [5], and micro-video [8] recommendation, recommender systems have demonstrated to be effectively supported in their decision-making process by all types of multimodal data sources the users usually interact with (e.g., product images and descriptions, users’ reviews, audio tracks). The literature refers to multimodal-aware recommender systems (MRSs) as the family of recommendation algorithms leveraging multimodal (i.e., audio, visual, textual) content data to augment the representation of items, thus tackling issues in the field such as the sparsity of the user-item matrix and the inexplicable nature of users’ actions (e.g., clicks, views) on online platforms which may not always be easy to profile for the recommendation algorithms. Despite being the initial stage of any multimodal recommendation pipeline, the extraction of meaningful multimodal features is paramount in delivering high-quality recommendations [2]. However, the current practice of employing diverse multimodal extraction procedures in each recommendation framework poses limitations. Firstly, these diverse implementations hinder the interdependence across various multimodal recommendation frameworks, making their fair comparison difficult [4]. Secondly, despite the availability of numerous pre-trained deep learning models in popular open source libraries, the lack of shared interfaces for feature extraction across them represents a challenge for model designers. To address these shortcomings, we propose Ducho, a unified framework designed to streamline the extraction of multimodal features for recommendation systems. By integrating widely-adopted deep learning libraries as backends such as TensorFlow, PyTorch, and Transformers, we establish a shared interface that empowers users to extract and process audio, visual, and textual features from both items and user-item interactions (see Table 1). This abstraction allows to leverage methods from each backend without being encumbered by the specific implementation that backend poses. A notable feature of our framework lays in its easily configurable extraction pipeline, which can be personalized using a YAML-based file. Users can specify the desired models, their respective backends, and models’ parameters (such as the extraction layer). By looking at the related literature, the most similar application to Ducho is Cornac [7], a framework for multimodal-aware recommendation. For the sake of completeness, we report their main differences. Differently from Cornac, Ducho: (i) is specifically aimed to provide customizable multimodal feature extractions, being completely agnostic to the downstream recommender system that might exploit the extracted features, thus being easily applicable to any model; (ii) provides the user with the possibility to select the deep learning extraction model, its backend, and its output layer; (iii) introduces the audio modality to the modalities set. To foster the adoption of Ducho, we also develop a public Docker image pre-equipped with a ready-to-use CUDA environment[1], and propose three demos to show Ducho’s functionalities. The GitHub repository, which comes with all needed resources is available at: https://github.com/sisinflab/Ducho. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [1] https://hub.docker.com/r/sisinflabpoliba/ducho.

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

A Unified Framework for Multimodal Feature Extraction in Recommendation Systems

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

A New Way to Extract Features for Smarter AI Recommendations

How to Develop Data-Driven AI Apps: A Guide to Making AI Services Directly From the Database

Building Multimodal Generative AI Systems: Architecture, Refinement, and Enhancement

SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Modular Enhancements for Stable Diffusion Architecture

Improving Stable Diffusion Architecture

A New Way to Extract Features for Smarter AI Recommendations

How to Develop Data-Driven AI Apps: A Guide to Making AI Services Directly From the Database

Building Multimodal Generative AI Systems: Architecture, Refinement, and Enhancement

SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Modular Enhancements for Stable Diffusion Architecture

Improving Stable Diffusion Architecture

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps