public HtmlString GetRecommendedVids(int userID)
{
RecommendedWatchML ml = new RecommendedWatchML();
ML_BL ml_bl = new ML_BL();
List <VideoRepository> recommendedWatch = new List <VideoRepository>();
var topics = ml_bl.GetUserTopics(userID);
//Use ML to determine best recommended video based on interests
var MLOutputs = ml.GetRecommendationsFromInterests(topics);
if (MLOutputs.Any())
{
recommendedWatch.AddRange(dal.GetRecommendedVids(userID, whereIn: MLOutputs.Select(z => (int)z.VideoId).ToList()));
}
if (MLOutputs.Count < 5)
{
recommendedWatch.AddRange(dal.GetRecommendedVids(userID, top: 5 - MLOutputs.Count, whereNotIn: MLOutputs.Select(z => (int)z.VideoId).ToList()));
}
var vidsStr = ConvertVidGridHTMLSting(recommendedWatch);
return(new HtmlString(vidsStr));
}
internal HtmlString GetNextRecommendedWatch(Guid videoId)
{
ML_BL ml_bl = new ML_BL();
// Get the ML Id from the table - each video is linked to two unique identifier, a GUID which is used by the applicable web-side and an int used by the ML
var MLVideoId = ml_bl.GetMLVideoId(videoId);
RecommendedWatchML ml = new RecommendedWatchML();
var MLOutputs = ml.GetRecommendationsFromPreviousWatch(MLVideoId); // Get Recommendation from trained model
if (MLOutputs.Any())
{
return(new HtmlString(ConvertVidGridHTMLSting(dal.GetVideoByMLId(MLOutputs.Select(z => (int)z.nextWatch).ToList()), withSVG: false)));
}
else
{
return(null);
}
}
