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Fix: suggestions logic/re-implement logic into worker and address performance issues (#834)

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## Description
Addresses suggestion logic oversight in recently merged suggestions fix,
and improve performance (especially app homepage startup time)

- Re-implements the logic that sorts/categorizes tailored suggestions
based on the seed items that are queried. This was implemented in the
original PR at first, but I overlooked moving that logic
- Removes `OneTimeWorkRequestBuilder` and replaces with trigger in
`PeriodicWorkRequest` to fire with a 30 seconds delay if cache is empty.

 
### Related issues
Fixes #833

### Testing
Will be testing in:
Android TV emulator in Android Studio
NVIDIA Shield TV Pro 2019

## AI or LLM usage
I will use AI to help with writing the tests/testing services.
This commit is contained in:
Justin Caveda 2026-02-07 19:38:26 -06:00 • committed by GitHub
parent abf5ec8004
commit 8e9a6799f5
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GPG key ID: B5690EEEBB952194
4 changed files with 186 additions and 26 deletions
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33
app/src/main/java/com/github/damontecres/wholphin/services/SuggestionsSchedulerService.kt
View file

@ -7,7 +7,6 @@ import androidx.work.BackoffPolicy
import androidx.work.Constraints
import androidx.work.ExistingPeriodicWorkPolicy
import androidx.work.NetworkType
import androidx.work.OneTimeWorkRequestBuilder
import androidx.work.PeriodicWorkRequestBuilder
import androidx.work.WorkManager
import androidx.work.workDataOf
@ -23,6 +22,7 @@ import java.util.UUID
import javax.inject.Inject
import kotlin.time.Duration.Companion.hours
import kotlin.time.Duration.Companion.minutes
import kotlin.time.Duration.Companion.seconds
import kotlin.time.toJavaDuration
@ActivityScoped
@ -72,29 +72,26 @@ class SuggestionsSchedulerService
SuggestionsWorker.PARAM_SERVER_ID to serverId.toString(),
)
val periodicWorkRequestBuilder =
PeriodicWorkRequestBuilder<SuggestionsWorker>(
repeatInterval = 12.hours.toJavaDuration(),
).setConstraints(constraints)
.setBackoffCriteria(
BackoffPolicy.EXPONENTIAL,
15.minutes.toJavaDuration(),
).setInputData(inputData)
if (cache.isEmpty()) {
Timber.i("Suggestions cache empty, scheduling immediate fetch")
workManager.enqueue(
OneTimeWorkRequestBuilder<SuggestionsWorker>()
.setConstraints(constraints)
.setInputData(inputData)
.build(),
)
Timber.i("Suggestions cache empty, scheduling periodic fetch with 30s delay")
periodicWorkRequestBuilder.setInitialDelay(30.seconds.toJavaDuration())
} else {
Timber.i("Scheduling periodic SuggestionsWorker")
}
Timber.i("Scheduling periodic SuggestionsWorker")
workManager.enqueueUniquePeriodicWork(
uniqueWorkName = SuggestionsWorker.WORK_NAME,
existingPeriodicWorkPolicy = ExistingPeriodicWorkPolicy.UPDATE,
request =
PeriodicWorkRequestBuilder<SuggestionsWorker>(
repeatInterval = 12.hours.toJavaDuration(),
).setConstraints(constraints)
.setBackoffCriteria(
BackoffPolicy.EXPONENTIAL,
15.minutes.toJavaDuration(),
).setInputData(inputData)
.build(),
request = periodicWorkRequestBuilder.build(),
)
}
}

46
app/src/main/java/com/github/damontecres/wholphin/services/SuggestionsWorker.kt
View file

@ -136,6 +136,10 @@ class SuggestionsWorker
val isSeries = itemKind == BaseItemKind.SERIES
val historyItemType = if (isSeries) BaseItemKind.EPISODE else itemKind
val contextualLimit = (itemsPerRow * 0.4).toInt().coerceAtLeast(1)
val randomLimit = (itemsPerRow * 0.3).toInt().coerceAtLeast(1)
val freshLimit = (itemsPerRow * 0.3).toInt().coerceAtLeast(1)
val historyDeferred =
async(Dispatchers.IO) {
fetchItems(
@ -144,11 +148,38 @@ class SuggestionsWorker
itemKind = historyItemType,
sortBy = ItemSortBy.DATE_PLAYED,
isPlayed = true,
limit = 10,
limit = 20,
extraFields = listOf(ItemFields.GENRES),
).distinctBy { it.relevantId }.take(3)
}
val seedItems = historyDeferred.await()
val allGenreIds =
seedItems
.flatMap { it.genreItems?.mapNotNull { g -> g.id } ?: emptyList() }
.distinct()
val excludeIds = seedItems.mapTo(HashSet()) { it.relevantId }
val contextualDeferred =
async(Dispatchers.IO) {
if (allGenreIds.isEmpty()) {
emptyList()
} else {
fetchItems(
parentId = parentId,
userId = userId,
itemKind = itemKind,
sortBy = ItemSortBy.RANDOM,
isPlayed = false,
limit = contextualLimit,
genreIds = allGenreIds,
excludeItemIds = excludeIds.toList(),
)
}
}
val randomDeferred =
async(Dispatchers.IO) {
fetchItems(
@ -157,7 +188,7 @@ class SuggestionsWorker
itemKind = itemKind,
sortBy = ItemSortBy.RANDOM,
isPlayed = false,
limit = itemsPerRow,
limit = randomLimit,
)
}
@ -170,17 +201,15 @@ class SuggestionsWorker
sortBy = ItemSortBy.DATE_CREATED,
sortOrder = SortOrder.DESCENDING,
isPlayed = false,
limit = (itemsPerRow * FRESH_CONTENT_RATIO).toInt().coerceAtLeast(1),
limit = freshLimit,
)
}
val seedItems = historyDeferred.await()
val contextual = contextualDeferred.await()
val random = randomDeferred.await()
val fresh = freshDeferred.await()
val excludeIds = seedItems.mapTo(HashSet()) { it.relevantId }
(fresh + random)
(contextual + fresh + random)
.asSequence()
.distinctBy { it.id }
.filterNot { excludeIds.contains(it.relevantId) }
@ -198,6 +227,7 @@ class SuggestionsWorker
limit: Int,
sortOrder: SortOrder? = null,
genreIds: List<UUID>? = null,
excludeItemIds: List<UUID>? = null,
extraFields: List<ItemFields> = emptyList(),
): List<BaseItemDto> {
val request =
@ -209,6 +239,7 @@ class SuggestionsWorker
genreIds = genreIds,
recursive = true,
isPlayed = isPlayed,
excludeItemIds = excludeItemIds,
sortBy = listOf(sortBy),
sortOrder = sortOrder?.let { listOf(it) },
limit = limit,
@ -225,6 +256,5 @@ class SuggestionsWorker
const val WORK_NAME = "com.github.damontecres.wholphin.services.SuggestionsWorker"
const val PARAM_USER_ID = "userId"
const val PARAM_SERVER_ID = "serverId"
private const val FRESH_CONTENT_RATIO = 0.4
}
}