Multi-Interest Recommendation: A Survey (2506.15284v1)

Abstract: Existing recommendation methods often struggle to model users' multifaceted preferences due to the diversity and volatility of user behavior, as well as the inherent uncertainty and ambiguity of item attributes in practical scenarios. Multi-interest recommendation addresses this challenge by extracting multiple interest representations from users' historical interactions, enabling fine-grained preference modeling and more accurate recommendations. It has drawn broad interest in recommendation research. However, current recommendation surveys have either specialized in frontier recommendation methods or delved into specific tasks and downstream applications. In this work, we systematically review the progress, solutions, challenges, and future directions of multi-interest recommendation by answering the following three questions: (1) Why is multi-interest modeling significantly important for recommendation? (2) What aspects are focused on by multi-interest modeling in recommendation? and (3) How can multi-interest modeling be applied, along with the technical details of the representative modules? We hope that this survey establishes a fundamental framework and delivers a preliminary overview for researchers interested in this field and committed to further exploration. The implementation of multi-interest recommendation summarized in this survey is maintained at https://github.com/WHUIR/Multi-Interest-Recommendation-A-Survey.

• The paper provides a comprehensive framework categorizing multi-interest recommendation methods and identifying key challenges in modeling diverse user preferences.
• It employs advanced techniques like dynamic routing and attention mechanisms to extract multiple interest representations from user interactions.
• The survey highlights practical applications such as online shopping and news, and outlines future directions for improving robustness and efficiency.

Overview of "Multi-Interest Recommendation: A Survey"

The paper "Multi-Interest Recommendation: A Survey" authored by Zihao Li et al., provides a comprehensive examination of multi-interest recommendation systems within the broader domain of recommender systems. It addresses the challenges that conventional recommendation methods face when dealing with the multifaceted preferences of users, which arise due to the diversity and volatility of user behavior, as well as the inherent uncertainty and ambiguity of item attributes. Multi-interest recommendation aims to overcome these challenges by extracting multiple interest representations from users' historical interactions to enhance preference modeling and deliver more accurate recommendations.

Key Insights

The paper identifies three primary motivations for multi-interest recommendation: fine-grained modeling of users' diverse preferences, fine-grained modeling of items' multiple aspects, enhanced diversity in recommendations, and improved explainability. It provides a structured classification of multi-interest modeling aspects based on the user-side and item-side perspectives. This classification highlights user behaviors, social group interactions, spatial-temporal information, category and attribute modeling, and other auxiliary data as pivotal elements in capturing multi-interest representations.

Methodological Framework

The paper outlines a methodological framework consisting of two main components necessary for multi-interest recommendation: a multi-interest extractor and a multi-interest aggregator. Various techniques are reviewed including dynamic routing, attention mechanisms, iterative attention, and non-linear transformations for multi-interest extraction. For aggregation, the paper discusses representation and recommendation aggregation methods, guided by concatenation, pooling, attention mechanism, and reinforcement learning strategies.

The work further explores challenges such as representation collapse and proposes multi-interest representation regularization techniques to enhance the diversity and discriminative capability of interest modeling. Cosine similarity and contrastive learning methods are highlighted for regularizing representation learning.

Applications and Datasets

Research implications extend to diverse application scenarios, including online shopping, micro-video recommendation, news recommendation, travel and life services, and online education. The paper references numerous public datasets, such as MovieLens, Amazon Review, and Taobao, which serve as benchmarks for validating multi-interest recommendation models.

Challenges and Future Directions

Despite advancements, the paper acknowledges ongoing challenges in multi-interest recommendation:

1. Adaptive Interest Extraction: It argues for the development of models capable of dynamically adjusting the number of interests based on user data, which can accommodate varying domains and evolving user behaviors.
2. Efficiency Optimization: The computational cost associated with multi-interest recommendation is significantly higher. Future efforts can focus on optimizing the extraction and aggregation processes, potentially leveraging advanced methods such as density-based clustering or hierarchical clustering.
3. Robustness Against Noise: Improving the robustness of models to effectively handle noisy interactions and item descriptions remains a critical area of exploration.
4. Explainability and Alignment: Enhancing the explainability and representation alignment between multi-interest and item aspects promises to improve user satisfaction and system transparency.
5. Cold-Start and Long-Tail Challenges: The paper suggests leveraging auxiliary information to mitigate data sparsity issues inherent in these challenges, thereby improving recommendation diversity and fairness.
6. Leveraging Frontier Technologies: Integration of reinforcement learning, LLMs, and diffusion models into multi-interest frameworks offers exciting possibilities for enhancing diversity and predictability in recommendations.

Conclusion

The survey by Li et al. serves as a fundamental resource for researchers by organizing existing efforts in multi-interest recommendations into a cohesive framework and mapping future directions in the field. By addressing multi-interest modeling deficiencies in conventional systems, employing sophisticated extraction and aggregation methods, and leveraging auxiliary and frontier technologies, the paper paves the way for more adaptive, efficient, and robust recommendation systems.