// This method gets called by the runtime. Use this method to configure the HTTP request pipeline.
public void Configure(
IApplicationBuilder app,
IHostingEnvironment env,
ILoggerFactory loggerFactory,
BookRecommenderContext db,
SpreadingRecommenderCache spreadingRecommederCache)
{
// add values to the appsetting singleton from appsettings.json
AppSettingsSingleton.Database.Connection = Configuration["Database:Connection"];
AppSettingsSingleton.Mining.WikiPagesStorage = Configuration["Mining:WikiPagesStorage"];
AppSettingsSingleton.Mining.Password = Configuration["Mining:Password"];
loggerFactory.AddConsole(Configuration.GetSection("Logging"));
loggerFactory.AddDebug();
if (env.IsProduction())
{
app.UseForwardedHeaders(new ForwardedHeadersOptions
{
ForwardedHeaders = ForwardedHeaders.XForwardedFor | ForwardedHeaders.XForwardedProto
});
}
if (env.IsDevelopment())
{
app.UseDeveloperExceptionPage();
}
else
{
app.UseExceptionHandler("/Home/Error");
}
// var options = new RewriteOptions()
// .AddRedirectToHttps();
app.UseStaticFiles();
app.UseAuthentication();
app.UseMvc(routes =>
{
routes.MapRoute(
name: "default",
template: "{controller=Home}/{action=Index}/{id?}");
});
app.UseResponseCompression();
System.Console.WriteLine("Spreading activation cache init started.");
var sw = Stopwatch.StartNew();
spreadingRecommederCache.Initialize(db);
sw.Stop();
System.Console.WriteLine($"Spreading activation cache initialized, it took: {sw.ElapsedMilliseconds}ms");
}
private static void TestRecSpreadingActivation(List <int> bookIds, int maxLevel,
int numberOfNSimilarNeighbors, int numberOfNeighborsForNextLevel)
{
List <Recommendation> recommendationsRSA = new List <Recommendation>();
String commentRDEBS = "userId=null, maxLevel=" + maxLevel + ", numberOfSimilarNeighbors=" + numberOfNSimilarNeighbors + ", numberOfNeighborsForNextLevel=" + numberOfNeighborsForNextLevel + ", backSpreading=yes";
BookRecommenderContext db = new BookRecommenderContext();
SpreadingRecommenderCache model = new SpreadingRecommenderCache();
model.Initialize(db);
foreach (int bookIdI in bookIds)
{
RecommenderSpreadingActivation recommender = new RecommenderSpreadingActivation(
maxLevel, numberOfNSimilarNeighbors, numberOfNeighborsForNextLevel, new SimilarityCacheModels(model));
List <int> recommendationListI = recommender.Recommend(bookIdI, null, HOW_MANY_REC);
Recommendation recommendationI = new Recommendation(bookIdI, recommendationListI);
recommendationsRSA.Add(recommendationI);
System.Console.WriteLine(recommendationI.exportAsString());
}
String fileName = "RecommenderSpreadingActivationLevel" + maxLevel + "SimilarNeighbors" + numberOfNSimilarNeighbors + "NeighborsForNextLevel" + numberOfNeighborsForNextLevel + ".rec";
ExportsRecommendationsToFile(recommendationsRSA, commentRDEBS, fileName);
}
private void RunOneLevelBFS(int levelI, List <int> bookIDsPreviousLevel,
GraphOfBooks graph)
{
// end of recursion
if (levelI == 0)
{
return;
}
SpreadingRecommenderCache model = simCacheModel.spreadingRecommenderCache;
// get next level of similar verteces
List <int> bookIDsForNextLevel = new List <int>();
foreach (int bookIdFromI in bookIDsPreviousLevel)
{
List <Tuple <int, int> > simBooksWithQuantitiesUnsortedI =
model.GetSimilaritiesBooksWithQuantitiesByAll(bookIdFromI);
List <Tuple <int, int> > simBooksWithQuantitiesSortedI =
simBooksWithQuantitiesUnsortedI.OrderBy(v => v.Item2).Reverse().ToList();
List <Tuple <int, int> > simBooksWithQuantitiesI =
simBooksWithQuantitiesSortedI.Take(numberOfSimilarNeighbors).ToList();
// add neighbors for next level
bookIDsForNextLevel.AddRange(
simBooksWithQuantitiesI.Select(b => b.Item1));
// vertex was inserted to graph in the last iteration
double?activationValueObjI = graph.GetActivationValueOfVertex(bookIdFromI);
int numberOfneighborsI = simBooksWithQuantitiesI.Select(b => b.Item2).Sum();
if (numberOfneighborsI == 0)
{
continue;
}
// update current vertex
graph.SetVertex(bookIdFromI, activationValueObjI.Value * MULTIPLICATOR_OF_ACTIVATION_PRESERVING);
// computes difference of activation value for distribution to neighbors
double activationValueForDistributionI =
activationValueObjI.Value * (1 - MULTIPLICATOR_OF_ACTIVATION_PRESERVING) / numberOfneighborsI;
// re-compute weights of neighbors
foreach (Tuple <int, int> neighborBookIdsWithQuantityI in simBooksWithQuantitiesI)
{
int neighborBookIdI = neighborBookIdsWithQuantityI.Item1;
double neighborQuantityI = neighborBookIdsWithQuantityI.Item2;
double activationValueDiff =
neighborQuantityI * activationValueForDistributionI;
graph.IncreaseActivationValueOfVertex(neighborBookIdI, activationValueDiff);
}
}
List <Tuple <int, int> > bookIDsForNextLevelAndTheirQuantities = bookIDsForNextLevel.GroupBy(b => b)
.Select(group => new Tuple <int, int>(group.Key, group.Count())).ToList();
List <Tuple <int, int> > sortedBookIDsForNextLevelAndTheirQuantities =
bookIDsForNextLevelAndTheirQuantities.OrderBy(v => v.Item2).Reverse().ToList();
List <int> bookIDsForNextLevelNoDuplicate = sortedBookIDsForNextLevelAndTheirQuantities
.Select(b => b.Item1).Take(numberOfNeighborsForNextLevel).ToList();
if (DEBUG)
{
System.Console.WriteLine($"Level {levelI} finished, next leven itemCount: {bookIDsPreviousLevel.Count}");
graph.printTopKOfGraph(10);
}
// recursion for next level
RunOneLevelBFS(levelI - 1, bookIDsForNextLevelNoDuplicate, graph);
}
