The Multi-Margin Cosine Loss (MMCL) can be extended to tackle challenges like popularity bias and diversification by incorporating additional mechanisms that adjust the loss function based on item popularity and user diversity.
Popularity Bias Mitigation: To address popularity bias, MMCL can be modified to assign lower weights to popular items during the training process. This can be achieved by introducing a popularity factor into the loss function, where the weight assigned to each negative sample is inversely proportional to its popularity. By doing so, the model will focus more on less popular items, thereby promoting a more balanced recommendation output.
Diversification: To enhance diversification, MMCL can integrate a diversity-aware component that encourages the selection of diverse items in the recommendation list. This can be implemented by adding a term to the loss function that penalizes the similarity between recommended items. For instance, a diversity penalty could be calculated based on the pairwise similarities of the recommended items, encouraging the model to recommend items that are not only relevant but also diverse.
Hybrid Approaches: Combining MMCL with other loss functions that specifically target popularity bias and diversity can create a hybrid loss function. For example, integrating elements from bias-aware contrastive losses or diversity-promoting losses can enhance the overall performance of the recommender system.
By implementing these strategies, MMCL can effectively address the challenges of popularity bias and diversification, leading to a more equitable and varied recommendation output.