Content Feed Integration
Overview: Below is information pertaining to how IRIS.TV’s Contextual Video Marketplace integrates with a publisher's content management system (CMS) via a feed or API.
Setting Up Video Asset Metadata Ingestion
IRIS.TV supports a wide range of feed and data formats to handle content feed ingestion. Below are lists of accepted ingest methods and metadata requirements for adding contextual segments to your video content.
Accepted Methods for Video Asset Metadata Ingestion
IRIS.TV supports the following feed formats for asset library ingest:
MRSS
RSS
XML
JSON
API
Once your have sent IRIS.TV a feed or API to access your assets, IRIS.TV will ingest your video data into a secure account. Integration typically takes less than one week.
Recommendations for Feed Pagination
If you are providing a content feed (rather than an API) for content ingestion, IRIS.TV recommends using pagination so that your full library can be ingested for contextual segmentation. IRIS.TV supports the following pagination methods:
Offset parameter: Feed parameters that allow for calling a specific number of assets and an offset for how far to go into the feed.
Next Link pagination: a link for the next page is included in the current page.
Recommendations for Asset Ordering
IRIS.TV recommends that feeds are ordered by a "updated_at" field on the assets. That ensures that all asset updates are ingested into the system.
Metadata Requirements
IRIS.TV has ranked metadata requirements in three sections:
Required – this data is necessary to create contextual segments for your assets.
Preferred – this data will likely improve the contextual segmentation for your assets.
Optional – this data can improve the contextual segmentation for your assets with some data partners.
Required Meta Data
"id/GUID": (unique numeric identifier of asset)
"title/name": Asset title
"sourceURL": link to asset’s video file from either your CDN or CMS
Preferred Meta Data
"description": Asset description
"categories": Taxonomical structure
"keywords/tags": Keywords or tags associated with the asset
"transcript": Either the transcript text or link to a transcript file for the asset
Optional Meta Data
"publishedDate": The date/time when the asset record was first published on your web site.
"creationDate": The date/time when the asset record was published.
"lastModifiedDate": The date/time when the asset record was last modified
"thumbnailURL": URL to a thumbnail or image that you want to represent your asset. For best results, image should be at least 300 pixels (width) with a 16:9 ratio.
"length": The duration of the asset in milliseconds
Sample Asset from a Publisher
{
"id": 5331690110001,
"name": "Lorem ipsum dolor sit amet, consectetur adipiscing elit",
"shortDescription": "Donec congue orci vel ante posuere consectetur. Vestibulum vel eros velit. In suscipit arcu nec lectus posuere sagittis.",
"creationDate": 1487717574502,
"publishedDate": 1487717955852,
"lastModifiedDate": 1487717955969,
"startDate": 1487717955969,
"endDate": null,
"tags": [
"alex rodriguez",
"new york yankees",
"mlb"
],
"videoStillURL": "http://xxx/2157889318001_5331738223001_5331690110001-vs.jpg?pubId=2157889318001",
"thumbnailURL": "http://xxx/2157889318001_5331738224001_5331690110001-th.jpg?pubId=2157889318001",
"length": 30805,
"economics": "AD_SUPPORTED",
"categories": [
"Baseball"
],
"itemState": "ACTIVE",
"SourceURL": "http://xxx/2157889318001_5331740038001_5331690110001.mp4"
},
After the Initial Ingest
Once the initial ingest is complete, IRIS.TV will provide you with credentials to start the Context Script or Context API integration. These credentials include:
Client Token: used to authenticate your asset library. Required for both the Context Script and Context API integrations.
Access Token: used to authenticate with the API. Required for the Context API integration.
The integration will also require the use of a platform_id. The platform_id is mapped in from the unique ID field during the ingest. Once you have received the above credentials from IRIS.TV, please move to Retrieving Contextual Data.
IRIS.TV