Thursday 06 March 2025
Researchers have developed a new approach to sequential recommendation, a technique used by online services such as Netflix and Amazon to suggest products or content based on a user’s previous interactions. The method, known as Intent-Interest Disentanglement and Item-Aware Intent Contrastive Learning (IDCLRec), is designed to better understand the underlying motivations behind a user’s behavior.
Traditional sequential recommendation systems typically focus on the items that a user has interacted with in the past, such as movies watched or products purchased. However, this approach can be limited in its ability to capture the nuances of human behavior. For instance, a user may watch a particular movie because they are interested in the genre, rather than just because it was recommended.
IDCLRec aims to address this limitation by disentangling two key factors that influence a user’s behavior: their intentions and interests. Intent refers to the specific goal or motivation behind an interaction, such as watching a movie for entertainment purposes. Interest, on the other hand, represents a user’s long-term preferences or tastes.
The researchers developed a novel framework that uses a combination of attention mechanisms and contrastive learning techniques to capture these two factors. The approach involves training a model on a large dataset of user interactions, where each interaction is represented as a sequence of items.
The model learns to identify patterns in the data that are indicative of a user’s intentions and interests. For example, if a user consistently watches action movies at night, the model may infer that their intention is to relax and unwind. Meanwhile, if they also watch sci-fi movies during the day, it could indicate an interest in the genre.
The IDCLRec approach has several advantages over traditional sequential recommendation systems. Firstly, it can better capture the complexities of human behavior by considering both intentions and interests. This allows for more accurate recommendations that are tailored to a user’s specific needs and preferences.
Secondly, IDCLRec is capable of handling cold start problems, where a new item or user is introduced into the system with little to no interaction history. By leveraging contrastive learning techniques, the model can learn to represent items in a way that is similar to how users interact with them, even if they have never been recommended before.
The researchers tested IDCLRec on several real-world datasets and found significant improvements in recommendation accuracy compared to traditional methods. The results suggest that disentangling intentions and interests can lead to more effective and personalized recommendations.
Cite this article: “Decoupling Intentions and Interests for Enhanced Sequential Recommendations”, The Science Archive, 2025.
Here Are The Keywords: Sequential Recommendation, Intent-Interest Disentanglement, Item-Aware Intent Contrastive Learning, User Behavior, Netflix, Amazon, Online Services, Recommendation Systems, Cold Start Problems, Personalization.
