Leo Shao | Searcharxiv

arXiv2026

One Hierarchy, Two Systems: Semantic Product IDs for Discovery-Surface Ranking and Search-Page Query Reformulation

Multi-merchant e-commerce catalogs contain equivalent and related products under different merchant-scoped identifiers, fragmenting behavioral evidence across merchants. Expert-defined taxonomies, meanwhile, are often too coarse for fine-grained discovery. We investigate whether a single hierarchical Semantic ID (\sid{}) representation can support personalized ranking and query reformulation. Learned once from product-content embeddings, the hierarchy defines product concepts at multiple granularities that each application combines with its own behavioral and serving context. For ranking, we aggregate consumer affinity and product performance over \sid{} prefixes and derive sequence features for candidate products and consumer histories. Controlled ablations show improved offline relevance, while online evaluation of the full ranking treatment shows stronger top-slot add-to-cart engagement and broader exposure for less-popular products. For query reformulation, we ground queries and session transitions in \sid{} concepts, use the hierarchy for navigation and refinement, and filter suggestions against the merchant's assortment. Offline evaluation shows finer intent preservation than taxonomy and higher-quality suggestions than raw query-string transitions; online evaluation shows reduced search effort and earlier access to purchasable products. These results show that a shared semantic product hierarchy can support both recommendation and search while preserving the task-specific context required by each application.

cs.IR↗

arXiv2017

Analyzing Adaptive Cache Replacement Strategies

Adaptive Replacement Cache (ARC) and CLOCK with Adaptive Replacement (CAR) are state-of-the- art "adaptive" cache replacement algorithms invented to improve on the shortcomings of classical cache replacement policies such as LRU, LFU and CLOCK. By separating out items that have been accessed only once and items that have been accessed more frequently, both ARC and CAR are able to control the harmful effect of single-access items flooding the cache and pushing out more frequently accessed items. Both ARC and CAR have been shown to outperform their classical and popular counterparts in practice. Both algorithms are complex, yet popular. Even though they can be treated as online algorithms with an "adaptive" twist, a theoretical proof of the competitiveness of ARC and CAR remained unsolved for over a decade. We show that the competitiveness ratio of CAR (and ARC) has a lower bound of N + 1 (where N is the size of the cache) and an upper bound of 18N (4N for ARC). If the size of cache offered to ARC or CAR is larger than the one provided to OPT, then we show improved competitiveness ratios. The important implication of the above results are that no "pathological" worst-case request sequences exist that could deteriorate the performance of ARC and CAR by more than a constant factor as compared to LRU.

cs.DS↗