Collaborative Explanations for Sequential Recommendation

Abstract

This evidence synthesis examines sequence-aware collaborative explanation in recommender systems that must explain a next-item prediction using evidence distributed across users and item attributes. It argues that the appropriate object of evaluation is the chain from observed discourse or interaction through representation, prediction, explanation, and user-facing intervention, not a model score in isolation. The review brings together the assigned studies with established work on uncertainty, robustness, provenance, and governance. Across these literatures, a common problem emerges: behavioral traces are selective and explanations can be persuasive without being faithful to the decision process. The proposed framework separates evidence quality, model behavior, decision policy, and operational monitoring, then asks how each layer changes under distribution shift, adversarial pressure, or incomplete information. It recommends evaluation by slices and repeated trials, explicit reject and escalation policies, preservation of data and reasoning lineage, and prospective monitoring tied to defined actions. The result is a research agenda for systems that are efficient enough to use but also bounded enough to audit. No new experiment is claimed; the article develops a comparative conceptual model and identifies tests that would make future empirical claims more credible.