{
  "annotation_id": "fcfdd825-5d2d-41b9-8da7-8c466fb9d915",
  "date_created": "2026-03-02T18:01:14.519000Z",
  "date_modified": "2026-03-02T18:01:14.519000Z",
  "file_hash": "8b5b52c6b70a3de6d2180157a2e4ea7b0f52c015bdeb534df746f6142215dabd",
  "private": false,
  "record": {
    "abstract": "We develop a simple statistical method to find affinity relations in a large\nopinion network which is represented by a very sparse matrix. These relations\nallow us to predict missing matrix elements. We test our method on the\nEachmovie data of thousands of movies and viewers. We found that significant\nprediction precision can be achieved and it is rather stable. There is an\nintrinsic limit to further improve the prediction precision by collecting more\ndata, implying perfect prediction can never obtain via statistical means.",
    "arxiv_id": "physics/0610283",
    "authors": [
      "Marcel Blattner",
      "Yi-Cheng Zhang",
      "Sergei Maslov"
    ],
    "categories": [
      "physics.soc-ph"
    ],
    "doi": "10.1016/j.physa.2006.04.121",
    "title": "Exploring an opinion network for taste prediction: an empirical study",
    "url": "https://arxiv.org/abs/physics/0610283"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a0f7f849-9a5a-47b7-a378-4e4d7193101d",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}