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 content feed requirements for any publisher's seeking to integrate IRIS.TV's Contextual Ad Targeting for Video.
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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.
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Required Meta Data
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Preferred Meta Data
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Optional Meta Data
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Optional Meta Data
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Sample
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Asset from a
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Publisher
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{ |
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"id": |
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5331690110001, "name": "Lorem ipsum dolor sit amet, consectetur adipiscing elit", |
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"shortDescription": "Donec congue orci vel ante posuere consectetur. Vestibulum vel eros velit. In suscipit arcu nec lectus posuere sagittis.", |
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"creationDate": |
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1487717574502, "publishedDate": |
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1487717955852, "lastModifiedDate": |
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1487717955969, "startDate": |
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1487717955969, "endDate": |
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null, "tags": [ |
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"alex rodriguez", |
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"new york yankees", |
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"mlb" |
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], |
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"videoStillURL": "http://xxx/2157889318001_5331738223001_5331690110001-vs.jpg?pubId=2157889318001", |
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"thumbnailURL": "http://xxx/2157889318001_5331738224001_5331690110001-th.jpg?pubId=2157889318001", |
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"length": |
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30805, "economics": "AD_SUPPORTED", |
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"categories": [ |
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"Baseball" |
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], |
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"itemState": "ACTIVE", |
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"SourceURL": "http://xxx/2157889318001_5331740038001_5331690110001.mp4" |
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}, |
Feed Format
Supported Feed Types
IRIS.TV supports feeds formatted as XML or JSON. If the client has an API, they can provide an API route for IRIS.TV to evaluate for level of effort to utilize.
Supported Pagination
IRIS.TV prefers that clients use pagination for asset feeds. This allows IRIS.TV to import the client's full library instead of a set of most recent assets. 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: link for next page is included in the current page.
Recommended Ordering
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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.