MARS: Multi-resolution Adaptive Routing for Sequential Recommendation
Quick summary
arXiv:2610.07505v1 Announce Type: new Abstract: Long-history recommenders often compress each user's history into a compact, candidate-independent memory that is cached and reused to score large candidate pools. We show that real user histories exhibit multi-scale semantic structure, with short-lived intent, medium-term interests, and long-term preferences coexisting in one sequence, and that monolithic cached memories preserve these scales unevenly: linear probes recover recent and mid-range content far worse than long-range content. We call this failure mode \textit{temporal aliasing}. We pr
Key takeaways
- arXiv:2610.07505v1 Announce Type: new Abstract: Long-history recommenders often compress each user's history into a compact, candidate-independent memory that is cached and reused to score large candidate pools.
- We show that real user histories exhibit multi-scale semantic structure, with short-lived intent, medium-term interests, and long-term preferences coexisting in one sequence, and that monolithic cached memories preserve these scales unevenly: linear probes recover recent and mid-range content far worse than long-range content.
- We call this failure mode \textit{temporal aliasing}.
Why it matters
The importance of “MARS: Multi-resolution Adaptive Routing for Sequential Recommendation” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
Member comments