Enhancing Temporal Feature Shifts in Recommender Systems Through DRO Training

In the world of recommendation systems, cold-start items pose a significant challenge. These are items that lack historical interactions and therefore have no stored recommendations. Traditional methods fail to effectively recommend these cold items, causing an imbalance in the system’s ecological balance. To address this issue, researchers have proposed various methods, including those that incorporate item features, such as categories and thumbnails of micro-videos. However, these methods have limitations.
The article introduces a new approach called Multi-Task Pruning with Representations (MTPR). MTPR replaces some warm item collaborative filtering (CF) representations with zero vectors in a multi-task manner. This allows the model to learn robust feature representations that align with CF representations, effectively recommending cold items without relying on historical interactions.
Imagine you’re organizing an event and want to recommend music to attendees based on their past preferences. Traditional methods would struggle to suggest new songs if they lacked previous interactions with similar genres or artists. MTPR addresses this issue by learning feature representations that align with CF representations, ensuring accurate recommendations for both warm and cold items.
Another proposed method is Heater, which utilizes a mixture of experts to extract personalized feature representations. This approach achieves alignment with interactions via minimizing the distance between feature representations and pre-trained CF representations. Think of it as fine-tuning an expert in a specific domain to improve recommendations.
CB2CF introduces a general feature extractor to align feature representations with CF representations learned from interactions via mean squared error (MSE) loss. This method encourages the feature extractor to learn robust features that capture both warm and cold items’ characteristics. Picture it as learning a language model for recommendations, where the goal is to predict the next item a user will interact with based on their past preferences and the context of the recommendation.
Finally, CCFCRec encourages the feature extractor to learn robust features by minimizing the distance between feature representations of co-occurring warm items. This method ensures that the model can generalize well to cold items without relying solely on historical interactions. Consider it as creating a team of experts in recommendation, where each expert specializes in a specific domain or genre, ensuring consistent recommendations across various user preferences and item types.
In summary, MTPR provides an effective solution for improving cold-start item recommendation by leveraging multi-task learning with representations. By learning robust feature representations that align with CF representations, the model can accurately recommend both warm and cold items without relying on historical interactions. This approach has significant implications for recommender systems in various domains, as it enables more personalized and accurate recommendations, leading to increased user satisfaction and engagement.