Recommender systems play a pivotal role in today's digital age, significantly improving the user experience by suggesting items or content that align with a user's preferences and behavior. In particular, sequential recommender systems are designed to predict user preferences based on their sequential interaction history, taking into account the order of their activities. They are commonly used in domains like e-commerce, music, and movie recommendation, where understanding the sequence of user behavior can lead to more personalized and accurate recommendations.
However, a challenge arises when dealing with 'cold-start' users - those with minimal logged interactions. Predicting the preferences of such users becomes difficult, as existing models struggle to learn from limited interactions.
One promising solution to this challenge is the MetaTL framework. This innovative approach leverages the power of few-shot learning, allowing it to model user transition patterns effectively, even with minimal interaction data.
In our project, we augment the capabilities of the MetaTL framework. We integrate a dynamic anomaly detector designed to specifically enhance the system's performance for these cold-start users. Our goal is to improve the recommendation system's accuracy and efficiency, thereby delivering better results and more personalized recommendations for this challenging user segment.