Prompt-based Multi-interest Learning Method for Personalized Sequential Recommendation

Core Concepts

The proposed PoMRec method learns user multi-interest embeddings by incorporating prompt embeddings into user interactions, making the inputs adaptive to the different learning objectives of the multi-interest extractor and aggregator. PoMRec also considers both the centrality and dispersion of user interactions to comprehensively model user multi-interests.

Abstract

The paper proposes a prompt-based multi-interest learning method (PoMRec) for sequential recommendation. Existing multi-interest learning methods have two key limitations: 1) they directly feed the user interactions into the multi-interest extractor and aggregator, while ignoring their different learning objectives, and 2) they merely consider the centrality of the user interactions to embed multiple interests of the user, while overlooking their dispersion.
To address these limitations, PoMRec consists of two main components:

Prompt-based Multi-interest Extractor and Aggregator:

PoMRec introduces learnable prompt embeddings for the multi-interest extractor and aggregator, making the inputted user interactions adaptive to their different learning objectives.
The extractor learns multi-interest embeddings considering both the centrality and dispersion of user interactions.
The aggregator predicts weights to fuse the multi-interest embeddings into the final user embedding.

Optimization and Recommendation:

PoMRec adopts the pair-wise objective function to train the model.
The final user embedding is used to predict the user rating to a given item for recommendation.

Extensive experiments on three public datasets (ML-1M, Beauty, and Movies & TV) demonstrate the effectiveness of PoMRec, outperforming state-of-the-art multi-interest learning methods. The ablation study and deployment on other methods further verify the contributions of the proposed prompt-based learning and centrality-dispersion based extractor.

Stats

The user interaction sequence Su,t contains the most recent M interacted items of user u at time step t.
The prompt embeddings PF and PG consist of Np randomly initialized d-dimensional embeddings for the multi-interest extractor and aggregator, respectively.
The user multi-interest embeddings Vu,t ∈ Rd×K represent the K interests of user u at time step t, derived from both the centrality and dispersion of user interactions.
The final user embedding eu,t ∈ Rd is the aggregation of Vu,t based on the predicted interest weights zu,t.

Quotes

"Existing methods directly feed the user interactions into the multi-interest extractor and aggregator, while ignoring their different learning objectives."
"Existing approaches mainly derive the user multi-interest embeddings by selecting a representative embedding of the user interaction embeddings, e.g., the weighted summation. This representative embedding only capture the centrality of user interactions."

Key Insights Distilled From

Prompt-based Multi-interest Learning Method for Sequential Recommendation

by Xue Dong,Xue... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2401.04312.pdf

Deeper Inquiries

How can the proposed prompt-based learning method be extended to other recommendation tasks beyond sequential recommendation

The proposed prompt-based learning method can be extended to other recommendation tasks beyond sequential recommendation by adapting the prompts to suit the specific requirements of different recommendation scenarios. For instance, in collaborative filtering recommendation, prompts can be designed to capture user preferences or item characteristics. In content-based recommendation, prompts can be tailored to extract relevant features from the content data. By incorporating prompts into the recommendation models, the system can better understand the context and user preferences, leading to more accurate and personalized recommendations across various recommendation tasks.

What are the potential limitations of the centrality-dispersion based multi-interest extractor, and how can it be further improved

The centrality-dispersion based multi-interest extractor has some potential limitations that can be addressed for further improvement. One limitation is that the trade-off parameter λ between centrality and dispersion may need to be carefully tuned for optimal performance, as an improper setting could bias the model towards either centrality or dispersion. Additionally, the method may struggle with highly sparse or noisy data, as the dispersion representation relies on the variability of user interactions. To enhance the extractor, techniques like adaptive regularization or dynamic adjustment of λ based on data characteristics could be explored. Moreover, incorporating additional context information or user feedback could help improve the robustness and accuracy of the multi-interest extractor.

How can the insights from this work on modeling user multi-interests be applied to other domains beyond recommendation, such as user profiling or customer segmentation

The insights gained from modeling user multi-interests in the context of recommendation can be applied to other domains such as user profiling or customer segmentation. By understanding and capturing the diverse interests of users, businesses can create more personalized and targeted marketing strategies. For instance, in user profiling, the learned multi-interest embeddings can be used to segment users based on their preferences and behaviors, enabling tailored communication and product recommendations. In customer segmentation, the multi-interest embeddings can help identify distinct customer segments with specific preferences, allowing businesses to customize their offerings and marketing campaigns to better meet the needs of different customer groups. This approach can lead to improved customer satisfaction, engagement, and loyalty.