Ground the Past to Predict the Future: Optimizing Natural-Language User Representations Without Downstream Supervision

In search and recommendation systems, user behavioral logs such as search queries and item clicks have long served as the substrate for user modeling. Typically, these interaction histories are distilled into dense representations, such as user embeddings trained end-to-end for the downstream task they serve. More recently, advances in large language models (LLMs) have opened a second axis for user representation in the form of natural-language user profiles, textual summaries of a user's behaviors. However, when checked against the input behaviors they are meant to summarize, LLM-generated profiles often misrepresent the user, overstating or over-inferring beyond what the evidence shows. This same interpretability means a profile's quality can be measured by whether it faithfully abstracts past behavior, a training signal that requires no downstream task at all. It is also a natural hypothesis that such faithfulness to the past would help predict the future. Yet existing methods still optimize profiles using downstream supervision from future interactions, leaving this hypothesis untested. We study this question by operationalizing groundedness as a reward along three axes (cohesion, alignment, and truthfulness) and training a profile generator against it with groupwise Direct Preference Optimization (DPO), without any downstream supervision. Across three behavioral log datasets, including a proprietary industry dataset, and multiple personalized search and recommendation tasks, groundedness-optimized profiles consistently improve over the base model and perform comparably to those trained with downstream supervision, ranking best or second-best in 13 of 16 settings. Groundedness further complements downstream supervision when the two rewards are combined. These results suggest that faithfulness to past behavior can serve as an effective training signal for user representations.