Personalized Multimodal Generation with Large Language Models

核心概念

The proposed method, Personalized Multimodal Generation (PMG), leverages large language models to extract user preferences from historical behaviors and generates personalized multimodal content by conditioning a generator, such as a multimodal LLM or diffusion model, on the extracted preferences.

要約

The paper proposes a method called Personalized Multimodal Generation (PMG) that leverages large language models (LLMs) to enable personalized multimodal generation. The key aspects of the method are:

1. Extracting user preferences: PMG first converts user behaviors, such as clicks in recommender systems or past conversations, into natural language to facilitate LLM understanding. It then extracts user preference descriptions using the LLM.

2. Representing user preferences: To capture user preferences comprehensively and accurately, PMG proposes to let the LLM output a combination of explicit keywords and implicit embeddings to represent user preferences.

3. Conditioning the generator: The combination of keywords and embeddings are used as prompts to condition the multimodal generator, such as a diffusion model or a multimodal LLM. PMG optimizes a weighted sum of the accuracy score (consistency with the target item) and the preference score (alignment with user preferences) to balance the generation.

The experiments demonstrate that PMG can generate personalized images, movie posters, and emoticons that effectively combine user preferences and target item characteristics. Compared to a baseline without personalization, PMG achieves significant improvements in personalization while retaining generation accuracy.

統計

None.

引用

None.

抽出されたキーインサイト

PMG : Personalized Multimodal Generation with Large Language Models

by Xiaoteng She... 場所 arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.08677.pdf

深掘り質問

How can PMG be extended to generate personalized content in other modalities beyond images, such as audio or video?

To extend PMG for generating personalized content in other modalities like audio or video, we can follow a similar approach to the one used for images. Here are some steps to consider:

Data Preprocessing: Convert the audio or video features into a format that can be understood by the LLM. This may involve summarizing the content or extracting key features that represent user preferences.

Preference Extraction: Use the LLM to extract user preferences from historical behaviors related to audio or video content. This could include past interactions with specific types of audio or video content, genres, or themes.

Conditioning the Generator: Feed the extracted user preferences into a generator designed for audio or video content generation. This could be a specialized model for each modality or a multimodal LLM capable of generating diverse content types.

Balancing Accuracy and Preference: Adjust the weights of the accuracy and preference scores based on the specific requirements of the audio or video content. This ensures that the generated content is both personalized and relevant to the target item.

Evaluation and Optimization: Evaluate the generated content using appropriate metrics for audio or video quality and personalization. Fine-tune the model based on feedback to improve the generation process.

By following these steps and adapting the methodology to suit the characteristics of audio or video data, PMG can be effectively extended to generate personalized content in multiple modalities.

How can the accuracy and preference scores be dynamically adjusted based on the specific application or user needs?

The adjustment of accuracy and preference scores in PMG can be tailored to specific applications or user needs by implementing dynamic weighting mechanisms. Here are some strategies to achieve this flexibility:

User Feedback Loop: Incorporate a feedback loop where users can provide explicit feedback on the generated content. This feedback can be used to dynamically adjust the weights of accuracy and preference scores for future content generation.

Application-Specific Metrics: Define application-specific metrics that capture the effectiveness of the generated content. Based on the performance of the generated content against these metrics, the weights of accuracy and preference scores can be adjusted accordingly.

User Profiles: Maintain user profiles that store information about user preferences and the importance of accuracy in content generation. Based on the user profile, the model can dynamically adjust the weights to prioritize either personalization or accuracy.

Real-Time Context: Consider the real-time context in which the content is being generated. For example, in a recommender system, the weights can be adjusted based on the user's current browsing behavior or interactions.

Machine Learning Techniques: Utilize reinforcement learning or online learning techniques to continuously optimize the weights of accuracy and preference scores based on real-time data and user interactions.

By implementing these strategies, PMG can adapt to the specific requirements of different applications and user preferences, ensuring a personalized and effective content generation process.

What other types of user behaviors, beyond clicks and conversations, could be leveraged to extract more comprehensive user preferences?

In addition to clicks and conversations, several other types of user behaviors can be leveraged to extract more comprehensive user preferences. Some of these behaviors include:

Purchase History: Analyzing the user's purchase history can provide valuable insights into their preferences, such as preferred brands, product categories, price ranges, and buying patterns.

Rating and Reviews: User ratings and reviews on products, services, or content can offer direct feedback on their preferences, likes, and dislikes. Sentiment analysis of reviews can further enhance understanding.

Search Queries: Examining the user's search queries can reveal specific topics, keywords, or interests they are actively seeking information about, providing clues to their preferences.

Social Media Interactions: Analyzing user interactions on social media platforms, such as likes, shares, comments, and posts, can offer a rich source of data on their interests, social connections, and engagement patterns.

App Usage Patterns: Studying how users interact with different features, functionalities, or sections within an application can help identify their preferences for specific content or services.

Location Data: Leveraging location data can provide insights into user preferences related to local services, events, or activities, enabling personalized recommendations based on geographic relevance.

Device Interactions: Monitoring how users interact with different devices, such as smartphones, tablets, or smart home devices, can reveal preferences for specific interfaces, functionalities, or content formats.

By incorporating these diverse user behaviors into the preference extraction process, PMG can create a more holistic and accurate representation of user preferences, leading to more personalized and relevant content generation.