Leveraging Statistics-Driven Pre-training to Stabilize Sequential Recommendation Models

المفاهيم الأساسية

Utilizing statistics-driven pre-training tasks to reduce the impact of random noise in user action sequences and stabilize the optimization of sequential recommendation models.

الملخص

The paper proposes the StatisTics-Driven Pre-training (STDP) framework to address the challenge of random noise in user action sequences, which can disrupt the optimization of sequential recommendation models.
Key highlights:

The authors reveal that inevitable random actions in user sequences, such as randomly accessing items or clicking items in random order, can lead to unstable supervision signals for the model.
To alleviate this issue, the STDP framework leverages statistics-driven pre-training tasks to stabilize the model optimization:

Co-occurred Items Prediction (CIP): Encourages the model to distribute its attention on multiple suitable targets instead of just focusing on the next item.
Paired Sequence Similarity (PSS): Enhances the model's robustness to random noise by maximizing the similarity between the original sequence and a paired sequence with randomly replaced items.
Frequent Attribute Prediction (FAP): Facilitates the model in capturing stable user long-term preferences by predicting the frequently appearing attributes in the sequence.

Extensive experiments on six datasets demonstrate the effectiveness of the proposed STDP framework, which outperforms state-of-the-art methods by a significant margin.
Further analysis verifies the generalization of the STDP framework by applying it to improve the performance of the GRU4Rec model.

الإحصائيات

The average sequence length across the six datasets ranges from 8.3 to 54.9 items per sequence.
The number of unique items in the datasets varies from 3,646 to 20,062.
The number of attributes per item ranges from 3.7 to 31.5.

اقتباسات

None

الرؤى الأساسية المستخلصة من

Beyond the Sequence

by Sirui Wang,P... في arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05342.pdf

استفسارات أعمق

How can the proposed STDP framework be extended to handle other types of noisy signals in user behavior data, such as implicit feedback or multi-modal interactions

The STDP framework can be extended to handle other types of noisy signals in user behavior data by incorporating specific pre-training tasks tailored to address those types of noise. For instance, in the case of implicit feedback, where user preferences are not explicitly stated but inferred from actions like dwell time or scroll behavior, a pre-training task could focus on predicting the next action based on implicit signals. This task could help the model learn to extract meaningful patterns from implicit feedback and reduce the impact of noise in the data.
Similarly, for multi-modal interactions where users engage with the system through various channels like text, images, or audio, the STDP framework can include pre-training tasks that leverage the different modalities to capture user preferences comprehensively. By designing tasks that encourage the model to integrate information from multiple modalities and learn robust representations, the framework can effectively handle noisy signals in multi-modal interactions.

What are the potential limitations of the statistics-driven pre-training approach, and how can it be further improved to handle more complex user behavior patterns

One potential limitation of the statistics-driven pre-training approach is its reliance on predefined statistical information, which may not capture all the complexities of user behavior patterns. To address this limitation and further improve the approach, several enhancements can be considered:

Dynamic Statistics Generation: Instead of relying on fixed statistical information, the framework can dynamically generate statistics based on the evolving user behavior data. This adaptive approach can capture changing patterns and adapt to new trends in user interactions.

Incorporating Temporal Dynamics: By incorporating temporal information into the statistics-driven pre-training, the model can learn to capture the dynamics of user preferences over time. This can help in better understanding long-term patterns and adapting to shifting user behaviors.

Hierarchical Statistics: Introducing hierarchical statistics that capture patterns at different levels of granularity can provide a more comprehensive view of user behavior. By considering both local and global statistics, the model can better capture complex behavior patterns.

Ensemble of Statistical Models: Combining multiple statistical models that capture different aspects of user behavior can enhance the robustness of the approach. Ensemble techniques can help in leveraging diverse statistical information and improving the overall performance of the framework.

Given the importance of user long-term preferences, how can the STDP framework be combined with other techniques, such as meta-learning or few-shot learning, to better capture and adapt to individual user preferences over time

To better capture and adapt to individual user preferences over time, the STDP framework can be combined with techniques like meta-learning or few-shot learning in the following ways:

Meta-Learning for Personalization: By incorporating meta-learning techniques, the framework can learn to adapt to individual user preferences more effectively. Meta-learning algorithms can help in quickly adapting the model to new users or changing preferences by leveraging meta-knowledge acquired from past interactions.

Few-Shot Learning for Personalized Recommendations: Integrating few-shot learning approaches can enable the model to make accurate recommendations with limited user data. By training the model to generalize from a few examples, it can provide personalized recommendations even for users with sparse interaction histories.

Adaptive Pre-Training with Meta-Learning: Combining the STDP framework with meta-learning for adaptive pre-training can enhance the model's ability to capture and adapt to individual user preferences over time. The meta-learning component can guide the pre-training process to focus on relevant aspects of user behavior and improve the model's personalization capabilities.