Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2508.08745

Towards Full Candidate Interaction: A Comprehensive Comparison Network for Better Route Recommendation

Hanyu Guo·Chao Chen·Longfei Xu·Chengzhang Wang·Kaikui Liu·Xiangxiang Chu
View original source ↗Read source document ↗Export metadata ↓

Abstract

We argue that the decision-making essence of route recommendation is comparative judgment: users choose a route because it is better than alternatives in specific aspects. The decision-critical information resides in segment-level spatial differences of non-overlapping parts between routes, which is irreversibly lost through item-level feature aggregation. Existing methods, whether attention-based or pairwise ranking approaches, follow an item-first paradigm that can only infer pairwise relations indirectly from individual route representations. To address this, we propose the Comprehensive Comparison Network (CCN), which inverts the information flow by constructing explicit comparison features from non-overlapping segments between route pairs and reasoning directly in the pairwise space. CCN introduces a Comprehensive Comparison Block that enables context-aware pairwise reasoning, where the comparison between two routes is informed by how both compare against all other candidates. We further develop an interpretable Pair Scoring Network that decomposes pairwise preferences into independent physical fields, providing field-level explanations for route selection. CCN has served as the production ranking model in Amap for over two years, achieving 85.70% offline route-trajectory coverage rate and +1.2% online improvement over the previous production model. We also release a large-scale route recommendation dataset comprising 175 million users, 512 million samples, and 6 billion routes across 370 cities.

Explore related subjects

cs.IR

Keep this discovery

BibTeXRIS

Hanyu Guo, Chao Chen, Longfei Xu, Chengzhang Wang, Kaikui Liu, Xiangxiang Chu. 2025-08-12. Towards Full Candidate Interaction: A Comprehensive Comparison Network for Better Route Recommendation. https://arxiv.org/abs/2508.08745

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

UniRec: Cross-stage Multi-Task Fusion with Preference Alignment for Cascaded Recommender Systems

Industrial recommender systems use cascaded stages with different objectives, feature spaces, and latency constraints. Optimizing pre-ranking and ranking separately can create cross-stage inconsistency: upstream models may filter out items preferred by downstream rankers, and independently tuned downstream fusion can offset upstream improvements. Existing multi-task fusion methods focus on multi-objective fusion within the ranking stage, and cross-stage methods typically only add a downstream score factor to upstream ranking. Joint optimization of fusion modules across both stages remains largely unexplored. We propose UniRec, a Unified Cross-stage Recommendation Fusion model. First, the two fusion agents partially share input embeddings and are trained in a single computation graph, so gradients from either stage propagate through the shared representation and influence the other. Second, we introduce a dual-axis preference alignment objective: a vertical cross-stage consistency term transfers downstream pairwise preferences to the upstream fusion score, and a horizontal compact aggregation term reorganizes dozens of pairwise objectives over heterogeneous prior signals into bidirectional preference evidence. Third, we find that unconstrained end-to-end fusion optimization can exploit imbalances in item attribute distributions, over-concentrating on high-reward regions at the cost of other objectives. We therefore add an attribute group-relative regularization that computes advantages within attribute groups and normalizes the policy over the same groups, so uniformly promoting an entire high-reward group yields no optimization gain. Offline, UniRec consistently outperforms single-stage fusion and cross-stage coordination baselines. Online A/B tests show a 0.616\% gain in app usage duration. UniRec is fully deployed on the Kuaishou platform.

cs.IR↗
arXiv2026

VikingRAG: Accurate and Token-efficient Retrieval-augmented Generation over Structured Documents

State-of-the-art retrieval-augmented generation (RAG) methods exploit document structures to acquire sufficient evidence, but often incur substantial token costs. To reduce structural-context tokens without compromising high RAG accuracy, we present {\sf VikingRAG}, a directory-aware semantic data management system that tightly integrates semantic and structural access to support structural-context-efficient, evidence-gap-driven multi-round retrieval. To further reduce token overhead of multi-round interaction, we materialize agentic multi-round retrieval traces as experience edges, and reuse these edges for similar queries, avoiding repeated multi-round exploration. To additionally reduce token costs when agentic multi-round retrieval is unnecessary, we introduce an adaptive escalation strategy that answers from one-round experience-augmented retrieval when the evidence is sufficient, and invokes agentic multi-round retrieval only otherwise. Experiments on real datasets show that the base system {\sf VikingRAG} matches high accuracy of state-of-the-art methods while consuming only 11.6\%--51.9\% of their tokens. With retrieval-trace reuse and adaptive escalation, token costs drop to 5.1\%--32.5\% while maintaining competitive accuracy and practical document-storage performance, showing the utility of this work for emerging AI knowledge bases.

cs.IR↗
arXiv2026

TimelyRAG: Semantic-Temporal Hybrid Retrieval for Time-Critical Question Answering in Overlapping-Evolving Documents

Although large language models (LLMs) and retrieval-augmented generation (RAG) have advanced open-domain question answering (QA), they remain unreliable when documents evolve through amendments. Existing time-sensitive retrieval methods address only the disjoint-evolving environment, where each update is an independent snapshot. However, laws, policies, and regulations often operate in overlapping-evolving environments, where amendments override earlier clauses while preserving most content, creating strong semantic overlap across versions. We propose TimelyRAG, a retriever-agnostic framework that incorporates temporal distance into ranking to align queries with version-appropriate documents. We also introduce TimelyQABench, the first benchmark for regulation-heavy domains with overlapping-evolving challenges. Experiments show consistent gains, up to +28.6% in nDCG@10, highlighting the importance of temporal reasoning for reliable QA over evolving documents. All resources are available at https://github.com/kaist-dmlab/TimelyRAG.

cs.IR↗