# 多维推荐

## <mark style="color:blue;">GET</mark> /api/v1/multiDimensionalRecommendation

* 说明： 通过这个 API，你将可以获得一个推荐列表：与用户存在一些关联（非 1 度）的数据（用户）。这些数据由 Relation 基于用户授权的社交数据和关系图谱进行计算得到，包括但不限于：
  * 有共同好友
  * 交互过相同的合约
  * 持有相同的 NFT
  * 关注了相同的用户
  * 有相同的粉丝的用户
* Header

| Field  | Type   | Required | Remarks                                                                        |
| ------ | ------ | -------- | ------------------------------------------------------------------------------ |
| ApiKey | String | true     | 从管理员获取的 [ApiKey](/relation-graph/g-zh/api/multi-dimensional-recommendation.md) |

* Request Parameters

| Field           | Type   | Required                        | Remarks          |
| --------------- | ------ | ------------------------------- | ---------------- |
| address         | String | fasle                           | EOA Address      |
| twitterUsername | String | fasle                           | twitter username |
| steamId         | String | fasle                           | steamId          |
| limit           | int    | 每次查询数量，上限100                    |                  |
| cursor          | string | 上一页返回的 cursor数据，cursor为空表示查询第一页 |                  |

* Request Example

```shell
curl  GET 'https://api.relationlabs.ai/api/v1/multiDimensionalRecommendation?address=0x9bd286ef4e3d9ec1af6c6ae9da2f0b3617deab13&limit=10&cursor=a1228c5910a04c94b70e18694d72cbb0' \
--header 'ApiKey: <ApiKey>'
```

* Response Parameters

| Field | Type   | Remarks |
| ----- | ------ | ------- |
| code  | String | 返回码     |
| desc  | String | 返回信息    |
| data  | List   | 数据      |

The UserInfo field consists of the following subfields:

| Field       | Type     | Remarks      |
| ----------- | -------- | ------------ |
| relation    | Relation | Relation用户信息 |
| address     | List     | 地址列表         |
| twitter     | Twitter  | 推特信息         |
| steam       | Steam    | steam信息      |
| description | String   | 推荐理由         |

* Response Body

```json
{
  "code": "0",
  "desc": "success",
  "data": {
    "cursor": "a8728c5910a04c94b70e18694d72cbb0",
    "list": [
      {
        "address": [],
        "relation": {
          "relationId": "d4swz-zaaaa-aaaaj-at5fa-cai",
          "name": "test_user",
          "avatar": "https://3fypb-gqaaa-aaaag-aaedq-cai.ic1.io/nft/eth/relation-test-club/16.png"
        },
        "twitter": {
          "username": "VitalikButerin",
          "name": "vitalik.eth",
          "avatar": "https://pbs.twimg.com/profile_images/977496875887558661/L86xyLF4_400x400.jpg"
        },
        "steam": {
          "steamId": "178691028912",
          "name": "vitalik",
          "avatar": "https://pbs.twimg.com/profile_images/977496875887558661/L86xyLF4_400x400.jpg"
        },
        "description": "Followed by marutti.eth, sha256111 and 24 others you follow"
      }
    ]
  }
}
```

* Return Code

| code | desc    | Remarks                           |
| ---- | ------- | --------------------------------- |
| 0    | success | the flag for a successful request |


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://relationlabs.gitbook.io/relation-graph/g-zh/api/multi-dimensional-recommendation.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.