This guide contains instructions for developers to integrate their recommended video content, using the Engage SDK, to populate recommendations experiences across Google surfaces, such as TV, mobile, and tablet.
Recommendation leverages the Recommendation cluster to show movies and TV shows, from multiple apps in one UI grouping. Each developer partner can broadcast a maximum of 25 entities in each recommendations cluster and there can be a maximum of 7 recommendation clusters per request.
Pre-work
Before you begin, complete the following steps. 1. Verify your app targets API level 19 or higher for this integration.
Add the
com.google.android.engage
library to your app.There are separate SDKs to use in the integration: one for mobile apps and one for TV apps.
For Mobile
dependencies { implementation 'com.google.android.engage:engage-core:1.5.5 }
for TV
dependencies { implementation 'com.google.android.engage:engage-tv:1.0.2 }
Set the Engage service environment to production in the
AndroidManifest.xml
file.For mobile apk
<meta-data android:name="com.google.android.engage.service.ENV" android:value="PRODUCTION"> </meta-data>
For tv apk
<meta-data android:name="com.google.android.engage.service.ENV" android:value="PRODUCTION"> </meta-data>
Execute publishing on a foreground service.
Publish Recommendations data at most once daily, triggered by either of
- User's first login of the day. (or)
- When the user starts interactive with the application.
Integration
AppEngagePublishClient
publishes the recommendation
cluster. Use the publishRecommendationClusters
method to publish a
recommendations object.
Use isServiceAvailable()
2 to check if the service is available for
integration.
val client = AppEngagePublishClient(context)
client.isServiceAvailable().addOnCompleteListener { task ->
if (task.isSuccessful) {
// Handle IPC call success
if(task.result) {
// Service is available on the device, proceed with content publish
// calls.
client.publishRecommendationClusters(recommendationRequest)
} else {
// Service is not available
}
} else {
// The IPC call itself fails, proceed with error handling logic here,
// such as retry.
}
}
Recommendation clusters and a publish request
Clusters are logical grouping of the entities. The following code examples explains how to build the clusters based on your preference and how to create a publishing request for all the clusters.
// cluster for popular movies
val recommendationCluster1 = RecommendationCluster
.Builder()
.addEntity(movie)
.addEntity(tvShow)
.setTitle("Popular Movies")
.build()
// cluster for top searches
val recommendationCluster2 = RecommendationCluster
.Builder()
.addEntity(movie)
.addEntity(tvShow)
.setTitle("Top Searches")
.build()
// creating a publishing request
val recommendationRequest = PublishRecommendationClustersRequest
.Builder()
.setSyncAcrossDevices(true)
.setAccountProfile(accountProfile)
.addRecommendationCluster(recommendationCluster1)
.addRecommendationCluster(recommendationCluster2)
.build()
Create an account profile
To allow a personalized experience on Google TV, provide account and profile
information. Use the AccountProfile
to provide:
- Account ID: A unique identifier that represents the user's account within your application. This can be the actual account ID or an appropriately obfuscated version.
- Profile ID (optional): If your application supports multiple profiles within a single account, provide a unique identifier for the specific user profile.
- Locale(optional): You can optionally provide the user's preferred language.
This field is useful if you send
MediaActionFeedEntity
in theRecommendationRequest
.
// If app only supports account
val accountProfile = AccountProfile.Builder()
.setAccountId("account_id")
.build();
// If app supports both account and profile
val accountProfile = AccountProfile.Builder()
.setAccountId("account_id")
.setProfileId("profile_id")
.build();
// set Locale
val accountProfile = AccountProfile.Builder()
.setAccountId("account_id")
.setProfileId("profile_id")
.setLocale("en-US")
.build();
When the service receives the request, the following actions occur within one transaction:
- Existing
RecommendationsCluster
data from the developer partner is removed. - Data from the request is parsed and stored in the updated
RecommendationsCluster
. In case of an error, the entire request is rejected and the existing state is maintained.
Cross-device sync
SyncAcrossDevices
flag controls whether a user's recommendations cluster data
is shared with Google TV and available across their devices such as TV, phone,
tablets. In order for the recommendation to work, it must be set to true.
Obtain consent
The media application must provide a clear setting to enable or disable
cross-device syncing. Explain the benefits to the user and store the user's
preference once and apply it in publishRecommendations
Request accordingly. To
get the most out of cross-device feature, verify app obtains user
consent and enables SyncAcrossDevices
to true
.
Delete the video discovery data
To manually delete a user's data from the Google TV server before the standard
60-day retention period, use the client.deleteClusters()
method. Upon
receiving the request, the service deletes all existing video discovery
data for the account profile, or for the entire account.
The DeleteReason
enum defines the reason for data deletion.
The following code removes recommendations on logout.
// If the user logs out from your media app, you must make the following call
// to remove recommendations data from the current google TV device,
// otherwise, the recommendations data persists on the current
// google TV device until 60 days later.
client.deleteClusters(
new DeleteClustersRequest.Builder()
.setAccountProfile(AccountProfile())
.setReason(DeleteReason.DELETE_REASON_USER_LOG_OUT)
.build()
)
// If the user revokes the consent to share data with Google TV,
// you must make the following call to remove recommendations data from
// all current google TV devices. Otherwise, the recommendations data persists
// until 60 days later.
client.deleteClusters(
new DeleteClustersRequest.Builder()
.setAccountProfile(AccountProfile())
.setReason(DeleteReason.DELETE_REASON_LOSS_OF_CONSENT)
.build()
)
Create entities
The SDK has defined different entities to represent each item type. Following entities are supported for the Recommendation cluster:
MediaActionFeedEntity
MovieEntity
TvShowEntity
Description
Provide a short description for each entity; this description will be displayed when users hover over the entity, providing them with additional details.
Platform specific playBack URIs
Create playback URIs for each supported platform: Android TV, Android, or iOS. This allows the system to select the appropriate URI for video playback on the respective platform.
In the rare case when the playback URIs are identical for all platforms, repeat it for every platform.
// Required. Set this when you want recommended entities to show up on
// Google TV
val playbackUriTv = PlatformSpecificUri
.Builder()
.setPlatformType(PlatformType.TYPE_ANDROID_TV)
.setActionUri(Uri.parse("https://www.example.com/entity_uri_for_tv"))
.build()
// Optional. Set this when you want recommended entities to show up on
// Google TV Android app
val playbackUriAndroid = PlatformSpecificUri
.Builder()
.setPlatformType(PlatformType.TYPE_ANDROID_MOBILE)
.setActionUri(Uri.parse("https://www.example.com/entity_uri_for_android"))
.build()
// Optional. Set this when you want recommended entities to show up on
// Google TV iOS app
val playbackUriIos = PlatformSpecificUri
.Builder()
.setPlatformType(PlatformType.TYPE_IOS)
.setActionUri(Uri.parse("https://www.example.com/entity_uri_for_ios"))
.build()
val platformSpecificPlaybackUris =
Arrays.asList(playbackUriTv, playbackUriAndroid, playbackUriIos)
// Provide appropriate rating for the system.
val contentRating = new RatingSystem
.Builder()
.setAgencyName("MPAA")
.setRating("PG-13")
.build()
Poster images
Poster images require a URI and pixel dimensions (height and width). Target different form factors by providing multiple poster images, but verify all images maintain a 16:9 aspect ratio and a minimum height of 200 pixels for correct display of the "Recommendations" entity, especially within Google's Entertainment Space. Images with a height less than 200 pixels may not be shown.
Image image1 = new Image.Builder()
.setImageUri(Uri.parse("http://www.example.com/entity_image1.png");)
.setImageHeightInPixel(300)
.setImageWidthInPixel(169)
.build()
Image image2 = new Image.Builder()
.setImageUri(Uri.parse("http://www.example.com/entity_image2.png");)
.setImageHeightInPixel(640)
.setImageWidthInPixel(360)
.build()
// And other images for different form factors.
val images = Arrays.asList(image1, image2)
Recommendation reason
Optionally provide a recommendation reason which can be used by Google TV to construct reasons as to why to suggest a specific Movie or TV Show to the user.
//Allows us to construct reason: "Because it is top 10 on your Channel"
val topOnPartner = RecommendationReasonTopOnPartner
.Builder()
.setNum(10) //any valid integer value
.build()
//Allows us to construct reason: "Because it is popular on your Channel"
val popularOnPartner = RecommendationReasonPopularOnPartner
.Builder()
.build()
//Allows us to construct reason: "New to your channel, or Just added"
val newOnPartner = RecommendationReasonNewOnPartner
.Builder()
.build()
//Allows us to construct reason: "Because you watched Star Wars"
val watchedSimilarTitles = RecommendationReasonWatchedSimilarTitles
.addSimilarWatchedTitleName("Movie or TV Show Title")
.addSimilarWatchedTitleName("Movie or TV Show Title")
.Builder()
.build()
//Allows us to construct reason: "Recommended for you by ChannelName"
val recommendedForUser = RecommendationReasonRecommendedForUser
.Builder()
.build()
val watchAgain = RecommendationReasonWatchAgain
.Builder()
.build()
val fromUserWatchList = RecommendationReasonFromUserWatchlist
.Builder()
.build()
val userLikedOnPartner = RecommendationReasonUserLikedOnPartner
.Builder()
.setTitleName("Movie or TV Show Title")
.build()
val generic = RecommendationReasonGeneric.Builder().build()
Display time window
If an entity should only be available for a limited time, set a custom expiration time. Without an explicit expiration time, entities will automatically expire and be erased after 60 days. So set an expiration time only when the entities need to be expired sooner. Specify multiple such availability windows.
val window1 = DisplayTimeWindow
.Builder()
.setStartTimeStampMillis(now()+ 1.days.toMillis())
.setEndTimeStampMillis(now()+ 30.days.toMillis())
val window2 = DisplayTimeWindow
.Builder()
.setEndTimeStampMillis(now()+ 30.days.toMillis())
val availabilityTimeWindows: List<DisplayTimeWindow> = listof(window1,window2)
DataFeedElementId
If you have integrated your Media catalogue or Media action feed with Google TV, you need not create separate entities for Movie or TV Show and instead you can create a MediaActionFeed Entity which includes the required field DataFeedElementId. This Id must be unique and must match with the ID in Media Action Feed as it helps to identify ingested feed content and perform media content lookups.
val id = "dataFeedEleemntId"
MovieEntity
Here's an example of creating an MovieEntity
with all the required fields:
val movieEntity = MovieEntity.Builder()
.setName("Movie name")
.setDescription("A sentence describing movie.")
.addPlatformSpecificPlaybackUri(platformSpecificPlaybackUris)
.addPosterImages(images)
// Suppose the duration is 2 hours, it is 72000000 in milliseconds
.setDurationMills(72000000)
.build()
You can provide additional data such as genres, content ratings, release date, recommendation reason and availability time windows, which may be used by Google TV for enhanced displays or filtering purposes.
val genres = Arrays.asList("Action", "Science fiction");
val rating1 = RatingSystem.Builder().setAgencyName("MPAA").setRating("pg-13").build();
val contentRatings = Arrays.asList(rating1);
//Suppose release date is 11-02-2025
val releaseDate = 1739233800000L
val movieEntity = MovieEntity.Builder()
...
.addGenres(genres)
.setReleaseDateEpochMillis(releaseDate)
.addContentRatings(contentRatings)
.setRecommendationReason(topOnPartner or watchedSimilarTitles)
.addAllAvailabilityTimeWindows(availabilityTimeWindows)
.build()
TvShowEntity
Here's an example of creating an TvShowEntity
with all the required fields:
val tvShowEntity = TvShowEntity.Builder()
.setName("Show title")
.setDescription("A sentence describing TV Show.")
.addPlatformSpecificPlaybackUri(platformSpecificPlaybackUris)
.addPosterImages(images)
.build();
Optionally provide additional data such as genres, content ratings, recommendation reason, offer price, season count or availability time window, which may be used by Google TV for enhanced displays or filtering purposes.
val genres = Arrays.asList("Action", "Science fiction");
val rating1 = RatingSystem.Builder()
.setAgencyName("MPAA")
.setRating("pg-13")
.build();
val price = Price.Builder()
.setCurrentPrice("$14.99")
.setStrikethroughPrice("$16.99")
.build();
val contentRatings = Arrays.asList(rating1);
val seasonCount = 5;
val tvShowEntity = TvShowEntity.Builder()
...
.addGenres(genres)
.addContentRatings(contentRatings)
.setRecommendationReason(topOnPartner or watchedSimilarTitles)
.addAllAvailabilityTimeWindows(availabilityTimeWindows)
.setSeasonCount(seasonCount)
.setPrice(price)
.build()
MediaActionFeedEntity
Here's an example of creating an MediaActionFeedEntity
with all the required
fields:
val mediaActionFeedEntity = MediaActionFeedEntity.Builder()
.setDataFeedElementId(id)
.build()
Optionally provide additional data such as description, recommendation reason and display time window, which may be used by Google TV for enhanced displays or filtering purposes.
val mediaActionFeedEntity = MediaActionFeedEntity.Builder()
.setName("Movie name or TV Show name")
.setDescription("A sentence describing an entity")
.setRecommendationReason(topOnPartner or watchedSimilarTitles)
.addPosterImages(images)
.build()
By implementing these steps, developers can successfully integrate video content recommendations into Google TV, boosting user discovery and engagement, providing a consistent and personalized viewing experience for users across all their devices.