private bool isModelInTitle(string productModel, string listingTitle)
{
bool res = true;
productModel = Preprocessor.pre_process(productModel);
listingTitle = Preprocessor.pre_process(listingTitle);
ArrayList productModelParts = LCS.convert(productModel);
for (int i = 0; i < productModelParts.Count; i++)
{
if (listingTitle.IndexOf(Convert.ToString(productModelParts[i])) == -1)
{
res = false;
break;
}
}
return(res);
}
private List <Match> matchProducts_with_Listings(List <Product> products, List <Listing> listings)
{
rtbConsole.Clear();
List <Match> matches = new List <Match>();
int i = 1;
int f = 1000;
foreach (Product product in products)
{
if (f-- > 0)
{
Match match = new Match();
match.ProductName = product.Name;
foreach (Listing listing in listings)
{
if (!listing.Assigned)
{
if (Preprocessor.pre_process(product.Manufacturer) == Preprocessor.pre_process(listing.Manufacturer))
{
if (isModelInTitle(product.Model, listing.Title))
{
double nlcs_score = LCS.normalized_lcs(Preprocessor.pre_process(product.Name),
Preprocessor.pre_process(listing.Title));
listing.Score = nlcs_score;
if (nlcs_score >= Convert.ToDouble(nudMinSimScore.Value))// can be found using machine learning techniques
{
match.Listings.Add(listing);
listing.Assigned = true;
}
}
}
}
}
matches.Add(match);
rtbConsole.AppendText("product " + i++.ToString() + " of " + products.Count + "\n");
rtbConsole.Focus();
Application.DoEvents();
}
}
return(matches);
}
