Enhancing Anime Illustration Recommendation with User-Aware Multi-Modal Fusion and Painting Style Features

Khái niệm cốt lõi

The proposed UMAIR-FPS model enhances anime illustration recommendation by integrating user-aware multi-modal feature fusion and painting style features to better capture user preferences.

Tóm tắt

The paper introduces the UMAIR-FPS model for user-aware multi-modal anime illustration recommendation. Key highlights:

Feature Extraction:

For image features, a dual-output image encoder is proposed to extract both semantic and painting style features, leveraging a pretext task for multi-class multi-label prediction.
For text features, a multi-perspective text encoder is constructed by fine-tuning Sentence-Transformers with a dataset of anime-specific text pairs covering multilingual mappings, entity relationships, and term explanations.

Multi-Modal Fusion:

A User-aware Multi-modal Contribution Measurement (UMCM) mechanism is introduced to dynamically weight the contributions of different modalities based on user features.
Higher-order multi-modal cross-interactions are modeled using the DCN-V2 module to better capture user preferences.

Evaluation:

Extensive experiments on a large real-world dataset show that UMAIR-FPS outperforms state-of-the-art baselines, improving the AUC by 5.4% and reducing the BCE loss by 25.35%.
Ablation studies validate the effectiveness of the key components, including the dual-output image encoder, multi-perspective text encoder, UMCM, and multi-modal crosses.

The proposed UMAIR-FPS framework demonstrates the importance of scene-specific modal encoders and user-aware multi-modal fusion for enhancing anime illustration recommendation.

Thống kê

The dataset contains 24,233,663 interactions between 215,394 users and 1,882,675 illustrations.
The test set covers the period from 2021/10/01 to 2022/01/01.

Trích dẫn

"We propose the User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style (UMAIR-FPS) to tackle these gaps."
"For image encoders, we first propose simultaneously extracting both painting style and content semantic features to enhance image representation."
"We propose a User-aware Multi-modal Contribution Measurement (UMCM) mechanism that considers the various contribution levels of modalities to user preference behavior, and automatically adjusts the ratio of specific illustrations for users at the interaction level."

Thông tin chi tiết chính được chắt lọc từ

UMAIR-FPS: User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style

by Yan Kang,Hao... lúc arxiv.org 04-18-2024

https://arxiv.org/pdf/2402.10381.pdf

Yêu cầu sâu hơn

How can the proposed UMAIR-FPS framework be extended to other multimedia recommendation scenarios beyond anime illustrations

The UMAIR-FPS framework can be extended to other multimedia recommendation scenarios by adapting its key components to suit different domains. For instance, in the context of movie recommendations, the dual-output image encoder can be modified to extract stylistic and semantic features from movie posters or frames. The multi-perspective text encoder can be utilized to understand movie titles, genres, and plot summaries from various sources. Additionally, the user-aware multi-modal contribution measurement mechanism can be adjusted to weigh different modalities such as audio, video, and text based on user preferences in the movie domain. By customizing these components to fit the characteristics of different multimedia content types, the UMAIR-FPS framework can be effectively applied to a wide range of recommendation scenarios beyond anime illustrations.

What are the potential challenges and considerations in applying user-aware multi-modal fusion techniques to real-time recommendation systems

Applying user-aware multi-modal fusion techniques to real-time recommendation systems poses several challenges and considerations. One challenge is the computational complexity of dynamically weighting multi-modal features based on user interactions in real-time. Efficient algorithms and data structures need to be implemented to ensure timely recommendations without compromising accuracy. Another consideration is the scalability of the system, as real-time recommendation systems often deal with large volumes of data and user interactions. Ensuring that the user-aware fusion techniques can scale effectively to handle increasing data loads is crucial. Additionally, maintaining user privacy and data security while incorporating user features for personalized recommendations is a key consideration in real-time systems. Implementing robust privacy-preserving mechanisms and compliance with data protection regulations is essential to build trust with users and protect their sensitive information.

How can the multi-perspective text encoding approach be leveraged to improve the understanding of user preferences and interests in other domains

The multi-perspective text encoding approach can be leveraged to enhance the understanding of user preferences and interests in various domains by capturing diverse aspects of textual information related to user interactions. In domains such as e-commerce, the text encoder can be used to analyze product descriptions, reviews, and specifications to extract semantic features that reflect user preferences. By incorporating multi-perspective text pairs that encompass different aspects of product information, the text encoder can generate rich representations of items that align with user interests. This approach can improve the accuracy of recommendation systems by considering nuanced textual information and providing more personalized recommendations based on a deeper understanding of user preferences across different domains.