Multi-intent-aware Session-based Recommendation: Capturing Diverse User Intents to Improve Next-item Prediction

Khái niệm cốt lõi

The proposed Multi-intent-aware Session-based Recommendation Model (MiaSRec) effectively captures diverse user intents within a session to improve next-item prediction, outperforming existing state-of-the-art session-based recommendation models.

Tóm tắt

The paper proposes a novel session-based recommendation (SBR) model called Multi-intent-aware Session-based Recommendation Model (MiaSRec) that aims to address the limitations of existing SBR models in capturing multiple user intents within a session.
Key highlights:

Existing SBR models focus on designing sophisticated neural-based encoders to learn a single session representation, neglecting diverse user intents that may exist within a session. This leads to significant performance drops, especially for longer sessions.
MiaSRec adopts frequency embedding vectors to enhance the information about repeated items and represents various user intents by deriving multiple session representations centered on each item.
MiaSRec dynamically selects the important session representations and aggregates them for the final recommendation, outperforming existing state-of-the-art SBR models on six benchmark datasets.
MiaSRec shows significant gains, up to 24.56% in Recall@20, particularly for datasets with longer average session lengths, demonstrating its effectiveness in capturing multiple user intents.
Ablation studies confirm the importance of the proposed components, including frequency embedding, multiple representations, and the intent selection method.

Thống kê

The average session length in the Tmall dataset is 10.62, indicating longer sessions.
MiaSRec achieves up to 24.56% improvement in Recall@20 compared to the best baseline model, particularly on datasets with longer average session lengths.

Trích dẫn

"Existing SBR models have primarily focused on extracting a single representation from a session to capture and express user preferences, which cannot express multiple user intents."
"MiaSRec represents various user intents by deriving multiple session representations centered on each item and dynamically selecting the important ones."
"Extensive experimental results show that MiaSRec outperforms existing state-of-the-art SBR models on six datasets, particularly those with longer average session length, achieving up to 6.27% and 24.56% gains for MRR@20 and Recall@20."

Thông tin chi tiết chính được chắt lọc từ

Multi-intent-aware Session-based Recommendation

by Minjin Choi,... lúc arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.00986.pdf

Yêu cầu sâu hơn

How can the proposed multi-intent representation approach be extended to other recommendation tasks beyond session-based recommendation

The proposed multi-intent representation approach in MiaSRec can be extended to other recommendation tasks beyond session-based recommendation by adapting the model architecture to suit the specific characteristics of different recommendation scenarios. For instance, in the context of movie recommendations, the multiple user intents could represent different genres or themes that a user is interested in. By incorporating frequency and position embeddings, along with intent selection mechanisms, the model can effectively capture the diverse preferences of users for various types of movies. Additionally, the intent selection process can be tailored to prioritize certain intents over others based on the context of the recommendation task. This flexibility allows the model to adapt to different recommendation domains and provide personalized suggestions based on multiple user intents.

What are the potential limitations or drawbacks of the dynamic intent selection method used in MiaSRec, and how could it be further improved

One potential limitation of the dynamic intent selection method used in MiaSRec is the reliance on a single hyperparameter, 𝛼, to control the sparsity of the importance weights for session representations. While setting 𝛼 to 1.5 empirically worked well in the experiments, it may not be optimal for all datasets or recommendation scenarios. To address this limitation, the model could benefit from a more adaptive approach to determining the sparsity level, such as incorporating a learnable parameter or introducing a mechanism to dynamically adjust 𝛼 during training based on the characteristics of the data. This would allow the model to better capture the varying importance of different user intents in sessions and improve the overall recommendation performance.

What other types of user behavior or contextual information could be incorporated into the MiaSRec model to better capture diverse user intents in a session

Incorporating additional types of user behavior or contextual information into the MiaSRec model can enhance its ability to capture diverse user intents in a session. One potential approach is to integrate temporal dynamics, such as the time of day or day of the week when the interactions occur, to capture time-sensitive user intents. By considering the temporal context, the model can adapt its recommendations based on users' changing preferences throughout the day or week. Furthermore, incorporating contextual information such as user demographics, location, or device type could provide valuable insights into user intents related to specific contexts or user characteristics. By leveraging a richer set of user behavior and contextual features, MiaSRec can offer more personalized and relevant recommendations tailored to individual users' diverse intents and preferences.