Invariant Representation Learning for Multimedia Recommendation

Multimedia recommendation forms a personalized ranking task with multimedia content representations which are mostly extracted via generic encoders. However, the generic representations introduce spurious correlations --- the meaningless correlation from the recommendation perspective. For example, suppose a user bought two dresses on the same model, this co-occurrence would produce a correlation between the model and purchases, but the correlation is spurious from the view of fashion recommendation. Existing work alleviates this issue by customizing preference-aware representations, requiring high-cost analysis and design.