Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm

Belangrijkste concepten

A novel ChatGPT-based training paradigm for sequential recommendation, which generates personalized prompts to enhance the model's ability to capture user preferences.

Samenvatting

The paper proposes a new framework called RecGPT (Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm) that utilizes a generative pre-training model to generate various prompts besides the original user behavior sequence items.
The key highlights are:

Pre-training stage: A multi-layer Transformer decoder network model is pre-trained with auto-regressive generative capabilities, integrating a user ID module to enhance personalized generation abilities.

Prompt-tuning stage: The pre-trained model is fine-tuned to generate personalized prompts specifically tailored for the recommendation task, introducing segment IDs to distinguish the generated prompts from the original behavior sequence.

Inference-validating stage: An auto-regressive recall approach is used to assess the performance of recommendations, effectively capturing the evolution of user preferences over time.

The authors conduct extensive offline and online A/B test experiments to demonstrate the effectiveness of RecGPT, outperforming state-of-the-art sequential recommendation methods.

Statistieken

The proposed RecGPT model outperforms the best counterpart baselines by about 88% and 96% cases in offline experiments.
In online A/B testing on the Kuaishou video APP, RecGPT contributes +0.772% Comment, +0.336% Forward, +0.143% Play, +0.027% Follow and +0.017% Watch time gain compared to the previous ComiRec model.

Citaten

"Considering that recommendation is indeed a conversation between users and the system with items as words, which has similar underlying pattern with ChatGPT, we design a new chat framework in item index level for the recommendation task."
"Unlike modeling only using the user's behavioral sequence, we propose incorporating unclicked items as personalized prompts in the training of models, as shown in Fig. 1(b). This method introduces the migration of user preferences over time."

Belangrijkste Inzichten Gedestilleerd Uit

RecGPT: Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm

by Yabin Zhang,... om arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.08675.pdf

Diepere vragen

How can the proposed RecGPT framework be extended to incorporate additional user context information beyond just the historical behavior sequence

To extend the RecGPT framework to incorporate additional user context information beyond the historical behavior sequence, one approach could be to include user profile data. This could involve integrating demographic information, preferences, past interactions, or any other relevant user attributes into the model. By incorporating user profiles, the model can better personalize recommendations based on individual user characteristics. Additionally, incorporating real-time contextual data such as current browsing behavior, location, or time of day could further enhance the recommendation accuracy. By dynamically updating the user context information, the model can adapt to changing user preferences and provide more relevant recommendations.

What are the potential limitations of the auto-regressive recall approach, and how can it be further improved to handle sparse and noisy sequential data

The auto-regressive recall approach, while effective, may have limitations when dealing with sparse and noisy sequential data. One potential limitation is the challenge of capturing long-term dependencies in the data, especially when the sequence is sparse. To address this, techniques such as incorporating attention mechanisms or memory modules could help the model better capture long-term dependencies and improve performance on sparse data. Additionally, handling noisy data can be challenging as it may introduce biases or inaccuracies in the recommendations. Techniques like data cleaning, outlier detection, or robust training strategies can help mitigate the impact of noisy data on the model's performance. Furthermore, exploring ensemble methods or hybrid approaches that combine auto-regressive recall with other recommendation techniques could provide more robust and accurate recommendations in the presence of sparse and noisy data.

How can the ideas from the ChatGPT training paradigm be applied to other recommendation tasks beyond sequential recommendation, such as cross-domain or multi-task recommendation

The ideas from the ChatGPT training paradigm can be applied to other recommendation tasks beyond sequential recommendation by leveraging the conversational nature of the model. For cross-domain recommendation, the model can be trained on a diverse set of data from different domains to learn a more generalized representation of user preferences. By incorporating prompts specific to each domain, the model can provide personalized recommendations across multiple domains. For multi-task recommendation, the model can be trained on a variety of tasks simultaneously, with prompts tailored to each task. This approach allows the model to learn to perform multiple recommendation tasks efficiently. By adapting the ChatGPT training paradigm to different recommendation scenarios, it opens up possibilities for more flexible and capable recommendation systems that can cater to diverse user needs.