HERO is a heterogeneous continual graph learning framework that preserves historical knowledge at both structural and semantic levels while adapting to new tasks. It uses DiSCo to select representative target nodes and reconstruct compact heterogeneous contexts, and applies multi-level knowledge distillation to align task-conditioned predictions with semantic responses. Experiments on four datasets with three HGNN backbones show HERO achieves best or tied-best average performance while maintaining competitive forgetting.
By Guiquan Sun, Xikun Zhang, Jingchao Ni, Dongjin Song
The paper introduces rEDMRec, a method that compresses a large language model’s reasoning about user preferences and item comparisons into a compact, editable memory. This memory, organized into four channels—long‑term preference, short‑term context, item perception, and counterfactual hard‑negative comparisons—can be updated by an LLM controller and queried by a lightweight student LLM for ranking, eliminating the need to re‑run the expensive teacher model for each request. Experiments on ML‑1M, Amazon Beauty, and Steam datasets show that rEDMRec consistently outperforms zero‑shot, few‑shot, RAG, and GraphRAG baselines, achieving up to a 13.3% improvement in HR@1 on ML‑1M.
By Minh Hoang Nguyen, Tung Le, Huy Tien Nguyen
arXiv:2606. 15778v1 Announce Type: cross Abstract: Large Language Models (LLMs) struggle to incorporate new knowledge without forgetting or costly retraining.
By Ali Sarabadani, Mahtab Tajvidiyan
The paper proposes treating an LLM agent’s long‑term memory selection as a budgeted slate recommendation problem, framing each request as a user, stored memories as items, and the context window as a slate. By applying recommender‑system techniques—candidate generation, learning‑to‑rank, and slate construction—AgentMemRec selects relevant memories without any LLM calls. Experiments on LongMemEval and LoCoMo show that AgentMemRec outperforms existing memory and context‑selection methods by 10–31 accuracy points and achieves comparable or better performance than an LLM reranker while reducing serving cost and eliminating LLM calls at write time.
By Xinyu Zhang, Weiwei Sun
arXiv:2608.21243v1 Announce Type: cross
Abstract: Sequential recommendation predicts the next item from a user's interaction history, but not every interaction is equally informative. Real logs combi...
By Zichun Jin, Zihan Zhou, Yinan Liu, Bin Wang, Xiaochun Yang
TailSpec-EASE is a lightweight linear recommender that incorporates a relation‑aware spectral knowledge‑graph prior into a local closed‑form reconstruction objective. By adapting the prior strength to item popularity, it provides stronger semantic guidance for long‑tail items. Across four public benchmarks, it achieves a favorable balance of overall accuracy, long‑tail performance, and training cost, improving NDCG@20 by up to 24% over a no‑KG baseline and training in just 37 seconds on CPU compared to thousands of seconds for GPU‑based KGAT and CPU LightGCN.
By Jianru Shen