public JsonResult GetItemRecommendations(string itemId, List <string> recentViewedProductList, string recommedType, string modelId, int noOfItems, string userId)
{
var resp = new List <RecommendationResult>();
if (recommedType == RecommendationTypes.RecentView.ToString())
{
if (recentViewedProductList != null && !recentViewedProductList.Any())
{
return(JsonSuccess("", JsonRequestBehavior.AllowGet));
}
resp = (from o in recentViewedProductList select new RecommendationResult {
RecommendedItemId = o
}).ToList();
}
else
{
_recommendationSettings = _sessionContext.CurrentSiteConfig.RecommendationSettings;
_recommendationClient = new RecommendationsAPI(new Uri(_recommendationSettings.ApiEndPoint));
_recommendationClient.HttpClient.DefaultRequestHeaders.Add("x-api-key", _recommendationSettings.RecommederKey);
// string userId = string.Empty;
string visitorId = string.Empty;
if (string.IsNullOrEmpty(userId))
{
userId = string.Empty;
if (_sessionContext.CurrentUser != null)
{
userId = _sessionContext.CurrentUser.UserId.ToString();
}
else
{
visitorId = _sessionContext.DeviceId;
}
}
if (string.IsNullOrEmpty(modelId))
{
modelId = GetRecommendationModelId(recommedType);
}
var modelGuId = Guid.Empty;
Guid.TryParse(modelId, out modelGuId);
var usageEvent = new List <UsageEvent>();
if (recommedType == RecommendationTypes.Basket.ToString())
{
var basket = _basketApi.GetBasketData("")?.Result;
if (basket != null && basket.LineItems != null && basket.LineItems.Any())
{
itemId = string.Join(",", basket.LineItems.Select(x => x.ProductId));
}
}
if (!string.IsNullOrEmpty(itemId))
{
if (modelGuId == Guid.Empty)
{
resp = _recommendationClient.Models.GetItemRecommendationsFromDefaultModel(itemId, noOfItems)?.ToList();
}
else
{
resp = _recommendationClient.Models.GetItemRecommendations(modelGuId, itemId, noOfItems)?.ToList();
}
}
else
{
if (modelGuId == Guid.Empty)
{
resp = _recommendationClient.Models.GetPersonalizedRecommendationsFromDefaultModel(usageEvent, userId != string.Empty ? userId : visitorId, noOfItems)?.ToList();
}
else
{
resp = _recommendationClient.Models.GetPersonalizedRecommendations(modelGuId, usageEvent, userId != string.Empty ? userId : visitorId, noOfItems)?.ToList();
}
}
}
if (resp != null && resp.Count > 0)
{
SearchRequestModel criteria = new SearchRequestModel
{
Filters = new List <SearchFilter>()
};
foreach (var data in resp)
{
var searchFilter = new SearchFilter
{
Key = "recordId",
Value = data.RecommendedItemId
};
criteria.Filters.Add(searchFilter);
}
var response = _productApi.GetProducts(criteria);
if (response != null && response.Result != null)
{
return(JsonSuccess(response.Result.Results, JsonRequestBehavior.AllowGet));
}
}
return(JsonSuccess("", JsonRequestBehavior.AllowGet));
}
static void Main(string[] args)
{
// Modify the lines below based on the values you received when the site was configured.
// Note that the connection string is only needed by the sample because it uploads some sample
// data to create a recommendation model.
string recommendationsEndPointUri = "https://yoursite.azurewebsites.net";
string apiAdminKey = "your Admin key goes here";
string connectionString = @"DefaultEndpointsProtocol=https;AccountName=yoursite;AccountKey=theAccountKeyProvidedToYouAtConfigurationTime";
// Blob storage locations. You may modify these locations to point to your real data.
string blobContainerName = "sample-data";
string catalogFileRelativeLocation = "demoCatalog.csv";
string usageFolderRelativeLocation = "usageFiles";
string evaluationUsageFolderRelativeLocation = null;
// Copy some sample data for training to the blob storage location.
// If you are using your own data, and the data is already uploaded to blob storage,
// this step may not be necessary.
Console.WriteLine("Copying sample training data to blob storage...");
CopySampleFilesToBlobStorage(connectionString, blobContainerName, catalogFileRelativeLocation, usageFolderRelativeLocation);
// Create an instance that allows us to work with the models.
// Note that you can "train" recommendation models only with the modeling key,
// You can score models with either the modeling key or the scoring key.
var webApp = new RecommendationsAPI(new Uri(recommendationsEndPointUri));
// Add the api key header to all requests
webApp.HttpClient.DefaultRequestHeaders.Add("x-api-key", apiAdminKey);
// Create a models class.
var models = new Models(webApp);
// Set to a specific model Id if you would like to skip the training phase.
Guid?modelId = null;
if (!modelId.HasValue)
{
// Let's train the model.
// Set the training parameters
var modelParameters = new ModelParameters(
description: "Sample created model",
blobContainerName: blobContainerName,
catalogFileRelativeLocation: catalogFileRelativeLocation,
usageFolderRelativeLocation: usageFolderRelativeLocation,
evaluationUsageFolderRelativeLocation: evaluationUsageFolderRelativeLocation,
supportThreshold: 3,
cooccurrenceUnit: CooccurrenceUnit.User,
similarityFunction: SimilarityFunction.Jaccard,
enableColdItemPlacement: false,
enableColdToColdRecommendations: false,
enableUserAffinity: true,
allowSeedItemsInRecommendations: true,
enableBackfilling: true,
decayPeriodInDays: 30);
Console.WriteLine("Training a new model.");
Model model = (models.CreateModel(modelParameters)) as Model;
Console.WriteLine("Waiting for model " + model.Id + "to complete training. ");
modelId = model.Id;
do
{
Thread.Sleep(5000);
model = models.GetModel(modelId.Value);
Console.WriteLine("Model " + model.Id + " status: " + model.ModelStatus + ":" + model.ModelStatusMessage);
}while (model.ModelStatus != ModelStatus.Completed &&
model.ModelStatus != ModelStatus.Failed);
Console.WriteLine("Model " + model.Id + " completed with status " + model.ModelStatus);
// Get model status.
if (model.ModelStatus != ModelStatus.Completed)
{
Console.WriteLine("Model training aborted.");
return;
}
}
// Scoring Example 1: Let's get some recommendations for item with ID 6480764
string itemId = "DHF-00881";
Console.WriteLine("\n\nRecommendations for Item " + itemId + ":");
var results = models.GetItemRecommendations(modelId.Value, "6480764");
foreach (var result in results)
{
Console.WriteLine("\t Recommendation Id: " + result.RecommendedItemId + " Score:" + result.Score);
}
// Scoring Example 2: Let's get some recommendations from the default model.
Console.WriteLine("\n\nRecommendations for Item from default model " + itemId + ":");
// First, ensure this model is the default model.
models.SetDefaultModel(modelId.Value);
// Now that it is the default model, we can request recommendations using the default model
results = models.GetItemRecommendationsFromDefaultModel("6480764");
foreach (var result in results)
{
Console.WriteLine("\t Recommendation Id: " + result.RecommendedItemId + " Score:" + result.Score);
}
// Scoring Example 3: Let's get personalized recommendations.
// Assume a customer has done two recent transactions -- she purchased item DHF-01550
// on February 1st 2017, and purchased item DHF-01333 the previous day.
Console.WriteLine("\n\nPersonalized recommendations for user with 2 transactions:");
var events = new List <UsageEvent>
{
new UsageEvent
{
ItemId = "DHF-01550",
EventType = EventType.Purchase,
Timestamp = new DateTime(2017, 2, 1)
},
new UsageEvent
{
ItemId = "DHF-01333",
EventType = EventType.Purchase,
Timestamp = new DateTime(2017, 1, 31)
}
};
results = models.GetPersonalizedRecommendationsFromDefaultModel(events);
foreach (var result in results)
{
Console.WriteLine("\t Recommendation Id: " + result.RecommendedItemId + " Score:" + result.Score);
}
Console.WriteLine("\nPress any key to close application.");
Console.ReadKey();
}
static void Main()
{
// modify the lines below based on the values you received when the site was configured.
// note that the connection string is only needed by the sample because it uploads some sample
// data to create a recommendation model.
string recommendationsEndPointUri = "https://yoursite.azurewebsites.net";
string apiAdminKey = "your admin key goes here";
string connectionString = @"DefaultEndpointsProtocol=https;AccountName=yoursite;AccountKey=theAccountKeyProvidedToYouAtConfigurationTime";
// create recommendations client that allows us to work with the recommendations API
var recommendationsClient = new RecommendationsAPI(new Uri(recommendationsEndPointUri));
// add the api key header to all requests.
// note that you can "train" recommendation models only with the modeling key,
// you can score models with either the modeling key or the scoring key.
recommendationsClient.HttpClient.DefaultRequestHeaders.Add("x-api-key", apiAdminKey);
// comment out this line and use a pre-trained model Id if you would like to skip the training phase.
Guid modelId = TrainModelUsingSampleData(recommendationsClient, connectionString);
#region Scoring Example 1: Getting some recommendations for item with ID DHF-01159
string itemId = "DHF-01159";
Console.WriteLine($"Getting recommendations for item '{itemId}' using model '{modelId}':");
IList <RecommendationResult> results = recommendationsClient.Models.GetItemRecommendations(modelId, itemId);
PrintRecommendationResults(results);
#endregion
#region Scoring Example 2: Getting some recommendations from the default model
// First, ensure this model is the default model.
Console.WriteLine($"\t Setting model '{modelId}' as the default model");
recommendationsClient.Models.SetDefaultModel(modelId);
// Now that it is the default model, we can request recommendations using the default model
Console.WriteLine($"Getting recommendations for item '{itemId}' using the default model:");
results = recommendationsClient.Models.GetItemRecommendationsFromDefaultModel(itemId);
PrintRecommendationResults(results);
#endregion
#region Scoring Example 3: Getting personalized recommendations
// assuming some user had done two recent transactions -- she purchased item DAF-00448
// on February 1st 2017, and purchased item DHF-01333 the previous day.
var events = new List <UsageEvent>
{
new UsageEvent
{
ItemId = "DAF-00448",
EventType = EventType.Purchase,
Timestamp = new DateTime(2017, 2, 1)
},
new UsageEvent
{
ItemId = "DHF-01333",
EventType = EventType.Purchase,
Timestamp = new DateTime(2017, 1, 31)
}
};
Console.WriteLine("Getting personalized recommendations for user with 2 transactions:");
results = recommendationsClient.Models.GetPersonalizedRecommendationsFromDefaultModel(events);
PrintRecommendationResults(results);
#endregion
Console.WriteLine();
Console.WriteLine("Press any key to close application.");
Console.ReadLine();
}
