Robust Recommendation under Unknown User Intent via Worst-Objective Optimization

Modern recommender systems must simultaneously serve multiple user objectives, such as relevance, engagement, diversity, novelty, and serendipity. A common solution is to optimize a fixed objective or a user-specific scalarization of several objectives. However, even a personalized scalarization is typically fixed for a user, while the user's immediate intent can change across visits and even within a session. A policy that is excellent for the dominant intent can therefore fail severely when a less frequent intent becomes active. We study recommendation under an unknown, time-varying intent distribution and propose Objective-Diverse Recommendation (ODR), a robust learning principle that directly optimizes the weakest objective for each user rather than committing to a fixed scalarization. Our formulation views the unknown intent distribution as an adversary over a set of candidate objectives and reduces to a max–min ranking objective. We provide a policy-gradient procedure that, at each update and for each context, selects the currently worst-performing objective as the reward, thereby estimating the gradient of the max–min objective without requiring the true intent distribution at deployment. Synthetic experiments with relevance, diversity, novelty, and serendipity objectives, together with experiments on the KuaiRec full-feedback benchmark, show that the proposed approach maintains stable worst-objective performance across substantial shifts in the intent mixture. Its advantage becomes more substantial as the number of plausible intents increases. These results suggest that robust optimization over plausible objectives can be effective even when the user’s active intent distribution is unknown and not explicitly estimated.