public ResponseBase <string[]> Get(string tags, RecommendationType type, int topK = 5)
{
var response = new ResponseBase <string[]>();
var productRecommender = new ProductRecommender(type, tags, topK * _bestNMultiplier, topK);
this.RunSafely(() =>
{
productRecommender.CalculateOutputs();
var result = productRecommender.Outputs;
//log recommendations
var dt = DataProvider.GenerateProductRecommendationDataTable();
var recommendationDate = DateTime.Now;
for (int i = 0; i < result.Length; i++)
{
dt.Rows.Add(null, tags, i + 1, result[i], recommendationDate);
}
DataProvider.WriteProductRecommendationToDatabase(dt);
response.Success(result);
}, ex =>
{
response.Error(ex);
});
return(response);
}
public ResponseBase <ProductRecommendationResponse> Get(string tags, int topK = 5)
{
topK = 20;
var bestN = 2;
var response = new ResponseBase <ProductRecommendationResponse>();
var colorRecommender = new ProductRecommender(RecommendationType.ColorGroup, tags, bestN * _bestNMultiplier, bestN);
var productTypeRecommender = new ProductRecommender(RecommendationType.ProductType, tags, bestN * _bestNMultiplier, bestN);
var productRecommender = new ProductRecommender(RecommendationType.ProductId, tags, topK * _bestNMultiplier, 0);
this.RunSafely(() =>
{
var productRecommenders = new List <ProductRecommender>();
productRecommenders.Add(colorRecommender);
productRecommenders.Add(productTypeRecommender);
productRecommenders.Add(productRecommender);
Parallel.ForEach(productRecommenders, recommender =>
{
recommender.CalculateOutputs();
});
var colorGroupResult = colorRecommender.Outputs;
var productTypeResult = productTypeRecommender.Outputs;
var productIdResult = productRecommender.Outputs;
var products = DataProvider.GetProductDatasetFromCsv();
var productIdColumnName = "ProductId";
var genderColumnName = "Gender";
var colorGroupColumnName = "ColorGroup";
var productTypeColumnName = "ProductType";
var dtDatasetStatus = DataProvider.GetRecommendationDatasetStatus();
var gender = string.Empty;
if (dtDatasetStatus != null && dtDatasetStatus.Rows.Count == 1)
{
gender = dtDatasetStatus.Rows[0]["RecommendationDatasetTableName"].ToString();
}
var filteredProducts = products.AsEnumerable()
.Where(x => productIdResult.Contains(x[productIdColumnName].ToString()) &&
colorGroupResult.Contains(x[colorGroupColumnName].ToString()) &&
productTypeResult.Contains(x[productTypeColumnName].ToString()) &&
(string.IsNullOrEmpty(gender) || x[genderColumnName].ToString() == gender))
.Select(x => new ProductRecommendationModel
{
ProductId = x[productIdColumnName].ToString(),
Gender = x[genderColumnName].ToString(),
ColorGroup = x[colorGroupColumnName].ToString(),
ProductType = x[productTypeColumnName].ToString(),
GenderWeight = 1,
ColorGroupWeight = colorGroupResult.Length - Array.IndexOf(colorGroupResult, x[colorGroupColumnName].ToString()),
ProductTypeWeight = productTypeResult.Length - Array.IndexOf(productTypeResult, x[productTypeColumnName].ToString())
})
.ToList();
var productsArray = filteredProducts.OrderByDescending(x => x.GenderWeight * x.ColorGroupWeight * x.ProductTypeWeight)
.Take(topK)
.Select(x => x.ProductId)
.ToArray();
var result = new ProductRecommendationResponse();
foreach (var item in productsArray)
{
result.Products.Add(new ProductRecommendationAttribute {
Name = item, ConfidenceRank = (Array.IndexOf(productsArray, item) + 1)
});
}
foreach (var item in colorGroupResult)
{
result.Colors.Add(new ProductRecommendationAttribute {
Name = item, ConfidenceRank = (Array.IndexOf(colorGroupResult, item) + 1)
});
}
foreach (var item in productTypeResult)
{
result.ProductTypes.Add(new ProductRecommendationAttribute {
Name = item, ConfidenceRank = (Array.IndexOf(productTypeResult, item) + 1)
});
}
//log recommendations
Task.Run(() => LogResults(tags, result));
response.Success(result);
}, ex =>
{
response.Error(ex);
});
return(response);
}
