{
  "annotation_id": "9d88ebc7-5a65-4bf8-8a34-69a3facd3f7b",
  "date_created": "2026-02-17T05:53:12.656000Z",
  "date_modified": "2026-02-17T05:53:12.656000Z",
  "file_hash": "324dac25d2f4a2aad574e284abb9cf70f799f6f9d7079ce8d2459df804cffefb",
  "private": false,
  "record": {
    "abstract": "Multi-behavior recommendation (MBR) aims to improve the performance w.r.t. the target behavior (i.e., purchase) by leveraging auxiliary behaviors (e.g., click, favourite). However, in real-world scenarios, a recommendation method often needs to process different types of behaviors and generate personalized lists for each task (i.e., each behavior type). Such a new recommendation problem is referred to as multi-behavior multi-task recommendation (MMR). So far, the most powerful MBR methods usually model multi-behavior interactions using a cascading graph paradigm. Although significant progress has been made in optimizing the performance of the target behavior, it often neglects the performance of auxiliary behaviors. To compensate for the deficiencies of the cascading paradigm, we propose a novel solution for MMR, i.e., behavior-informed graph embedding learning (BiGEL). Specifically, we first obtain a set of behavior-aware embeddings by using a cascading graph paradigm. Subsequently, we introduce three key modules to improve the performance of the model. The cascading gated feedback (CGF) module enables a feedback-driven optimization process by integrating feedback from the target behavior to refine the auxiliary behaviors preferences. The global context enhancement (GCE) module integrates the global context to maintain the user\u0027s overall preferences, preventing the loss of key preferences due to individual behavior graph modeling. Finally, the contrastive preference alignment (CPA) module addresses the potential changes in user preferences during the cascading process by aligning the preferences of the target behaviors with the global preferences through contrastive learning. Extensive experiments on two real-world datasets demonstrate the effectiveness of our BiGEL compared with ten very competitive methods.",
    "arxiv_id": "2601.07294",
    "authors": [
      "Wenhao Lai",
      "Weike Pan",
      "Zhong Ming"
    ],
    "categories": [
      "cs.IR"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Towards Multi-Behavior Multi-Task Recommendation via Behavior-informed Graph Embedding Learning",
    "url": "https://arxiv.org/abs/2601.07294",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "d58704f1-3e73-42b8-a661-f472cd621a6a",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}