data

Data

This repository contains data personalized to my Steam profile.

Source

The data can be found on the Interactive Recommender page. It is at the top of the HTML code of the page. I have copied each of the four variables into different .json files.

Variables

• gAppInfo.json contains a dictionary matching app ids to app info available on the Steam store:
  • n: app name,
  • r: release date (in Unix time),
  • t: tags associated with the app,
  • o: whether the app is owned by the user,
  • w: whether the app is "wishlisted" by the user,
  • i: whether the app is marked as "ignored" by the user.

window.gAppInfo = {
  "363970": {
    "n": "Clicker Heroes",
    "r": 1431565500,
    "t": [ 9, 21, 113, 122, 492, 597, 599, 1684, 3859, 3871, 4136, 4182, 4190, 5350, 379975
    ],
    "o": true
  },
  [...]
  "1059150": {
    "n": "Ritual: Crown of Horns",
    "r": 1558094437,
    "t": [ 19, 492, 493, 1647, 1734, 1756, 1774, 4026, 4182, 4345, 4637, 4667
    ]
  }
};

• gInputApps.json contains a list of info about my 50 most played Steam games, which is the algorithm input:
  • n: counter of the last played games ; the most recently played game is assigned 0, the previous one 1, etc.
  • a: app id,
  • p: playtime (in hours),
  • l: date when the game was last played (in Unix time).

window.gInputApps = [
  {
    "n": 38,
    "a": 570,
    "p": 3707,
    "l": 1555954754
  },
  [...]
  {
    "n": 470,
    "a": 278100,
    "p": 19,
    "l": 1496011976
  }
];

• gRecommendations.json contains a list of 30 recommendation rankings, which is the algorithm output:
  • setting "algorithm_variant", currently useless (as it is always set to 0), to switch between recommender versions,
  • "popularity" bias, which takes one of 5 values (-1/3, 0, 1/3, 2/3 and 1),
  • "release recency" bias, which takes one of 6 values (120, 60, 36, 24, 12, and 6 months),
  • setting "include_already_played_in_results" to choose whether to filter out games owned by the user,
  • score scale, so that the normalized score of the top recommendation is always equal to 1000,
  • there are 200 recommended app_ids, sorted according to their normalized scores.

window.gRecommendations = [
  {
    "algorithm_options": {
      "algorithm_variant": 0,
      "popularity_bias": "-0.333333343267440796",
      "release_recency_bias": "120",
      "include_already_played_in_results": 0
    },
    "recommended_apps": [],
    "score_scale": "0.008876836858689785",
    "app_ids": [ 363970, 247080, 49520, [...] 409690, 373970, 560380 ],
    "scores": [ 1000,  903,  822,  [...] 106, 106, 105 ]
},
[...]
{
  "algorithm_options": {
    "algorithm_variant": 0,
    "popularity_bias": "1",
    "release_recency_bias": "6",
    "include_already_played_in_results": 0
  },
  "recommended_apps": [],
  "score_scale": "0.0626560673117637634",
  "app_ids": [ 933390, 781990, 504620, [...] 840430, 1034900, 1059150 ],
  "scores": [ 1000, 903, 673, [...] 16, 16, 16 ]
  }
];

• gTags.json contains a dictionary matching tag ids to tag names for store tags which arise in your recommendations,

window.gTags = {
  "9": "Strategy",
  "21": "Adventure",
  "113": "Free to Play",
  [...]
  "8093": "Minigames",
  "5230": "Sequel",
  "6276": "Inventory Management"
};