A new arXiv preprint argues that the two most common query-document scoring methods, TF-IDF and BM25, both correspond exactly to Kullback-Leibler divergences. This gives the widely used retrieval heuristics a probabilistic justification and places them inside a single statistical framework. The author frames the work as filling a long-standing gap in how these ranking functions are derived.
A new arXiv paper introduces TATK, a framework aimed at the mismatch between text-generation-style next-item prediction and full-catalog top-K ranking in LLM-based sequential recommenders. It combines Top-K Learning with knowledge-grounded verification to better align the model's training objective with ranking tasks. The work is presented as a preprint and no production deployment is reported.
A new arXiv preprint examines how a standard remedy for class imbalance — assigning each label a positive-class weight equal to the ratio of negatives to positives — alters the ranking of the top K items in one-vs-rest rankers. Drawing on Elkan's identity, which shows the weight shifts a label's log-odds, the authors characterize the resulting slippage in top-K results and propose a way to diagnose and repair those errors.