{
  "id": "1606.08117",
  "version": "v1",
  "published": "2016-06-27T03:06:44.000Z",
  "updated": "2016-06-27T03:06:44.000Z",
  "title": "Improved Recurrent Neural Networks for Session-based Recommendations",
  "authors": [
    "Yong Kiam Tan",
    "Xinxing Xu",
    "Yong Liu"
  ],
  "categories": [
    "cs.LG"
  ],
  "abstract": "Recurrent neural networks (RNNs) were recently proposed for the session-based recommendation task. The models showed promising improvements over traditional recommendation approaches. In this work, we further study RNNbased models for session-based recommendations. We propose the application of two techniques to improve model performance, namely, data augmentation, and a method to account for shifts in the input data distribution. We also empirically study the use of generalised distillation, and a novel alternative model that directly predicts item embeddings. Experiments on the RecSys Challenge 2015 dataset demonstrate relative improvements of 12.8% and 14.8% over previously reported results on the Recall@20 and Mean Reciprocal Rank@20 metrics respectively.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-06-27T03:06:44.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "recurrent neural networks",
      "session-based recommendation",
      "traditional recommendation approaches",
      "input data distribution",
      "directly predicts item embeddings"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}