public async Task <IEnumerable <EstateUnit> > Recommend(long user, int items)
{
IEnumerable <EstateUnit> result;
try
{
var userFavourites = await _favouritesRepository.ListAllAsync();
var userItems = userFavourites.Select(uf => new UserItem
{
UserId = uf.UserIdNumeric,
ItemId = uf.EstateUnitId
}).ToList();
var currentUserPreferences = userItems.Where(ui => ui.UserId == user).Select(ui => ui.ItemId).ToArray();
var baseModel = RecommendationDataModelBuilder.BuildModel(userItems, isReviewBased: false);
var modelForPreferences = RecommendationDataModelBuilder
.BuildModelForUserPreferences(baseModel, user, currentUserPreferences);
var similarity = new LogLikelihoodSimilarity(modelForPreferences);
var recommender = new GenericBooleanPrefItemBasedRecommender(modelForPreferences, similarity);
var recommendedItems = recommender.Recommend(user, items, null);
result = await GetRecommendedProperties(recommendedItems);
}
catch
{
result = new List <EstateUnit>();
}
return(result);
}
public ActionResult GetRecommendedFilms(string filmIdsJson)
{
var filmIds = JsonConvert.DeserializeObject <long[]>(filmIdsJson);
var dataModel = GetDataModel();
// recommendation is performed for the user that is missed in the preferences data
var plusAnonymModel = new PlusAnonymousUserDataModel(dataModel);
var prefArr = new GenericUserPreferenceArray(filmIds.Length);
prefArr.SetUserID(0, PlusAnonymousUserDataModel.TEMP_USER_ID);
for (int i = 0; i < filmIds.Length; i++)
{
prefArr.SetItemID(i, filmIds[i]);
// in this example we have no ratings of movies preferred by the user
prefArr.SetValue(i, 5); // lets assume max rating
}
plusAnonymModel.SetTempPrefs(prefArr);
var similarity = new LogLikelihoodSimilarity(plusAnonymModel);
var neighborhood = new NearestNUserNeighborhood(15, similarity, plusAnonymModel);
var recommender = new GenericUserBasedRecommender(plusAnonymModel, neighborhood, similarity);
var recommendedItems = recommender.Recommend(PlusAnonymousUserDataModel.TEMP_USER_ID, 5, null);
return(Json(recommendedItems.Select(ri => new Dictionary <string, object>()
{
{ "film_id", ri.GetItemID() },
{ "rating", ri.GetValue() },
}).ToArray()));
}
static void KnnItemBasedRecommenderTest()
{
Console.WriteLine("KnnItemBasedRecommenderTest");
System.Diagnostics.Stopwatch watch = new System.Diagnostics.Stopwatch();
watch.Start();
var model = new FileDataModel(filePath);
ItemSimilarity similarity = new LogLikelihoodSimilarity(model);
Optimizer optimizer = new ConjugateGradientOptimizer();
var recommender = new KnnItemBasedRecommender(model, similarity, optimizer, 10);
var iter = model.getUserIDs();
while (iter.MoveNext())
{
var userId = iter.Current;
var recommendedItems = recommender.recommend(userId, 5);
Console.Write("uid:" + userId);
foreach (var ritem in recommendedItems)
{
Console.Write("(" + ritem.getItemID() + "," + ritem.getValue() + ")");
}
Console.WriteLine();
}
watch.Stop();
Console.WriteLine(watch.ElapsedMilliseconds);
}
private GenericUserBasedRecommender CreateRecommender(string pathToDataFile)
{
dataModel = new FileDataModel(pathToDataFile, false, FileDataModel.DEFAULT_MIN_RELOAD_INTERVAL_MS, false);
var similarity = new LogLikelihoodSimilarity(dataModel);
var neighborhood = new NearestNUserNeighborhood(3, similarity, dataModel);
var recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
return(recommender);
}
public IRecommender BuildRecommender(IDataModel model)
{
IUserSimilarity similarity = new LogLikelihoodSimilarity(model);
IUserNeighborhood neighborhood =
new NearestNUserNeighborhood(10, similarity, model);
return
(new GenericUserBasedRecommender(model, neighborhood, similarity));
}
public List <MovieRecommendationDto> GetRecommendedMovies(int[] preferredMovieIds)
{
var dataModel = GetDataModel();
// recommendation is performed for the user that is missed in the preferences data
var plusAnonymModel = new PlusAnonymousUserDataModel(dataModel);
var prefArr = new GenericUserPreferenceArray(preferredMovieIds.Length);
prefArr.SetUserID(0, PlusAnonymousUserDataModel.TEMP_USER_ID);
for (int i = 0; i < preferredMovieIds.Length; i++)
{
prefArr.SetItemID(i, preferredMovieIds[i]);
// in this example we have no ratings of movies preferred by the user
prefArr.SetValue(i, 5); // lets assume max rating
}
plusAnonymModel.SetTempPrefs(prefArr);
var similarity = new LogLikelihoodSimilarity(plusAnonymModel);
var neighborhood = new NearestNUserNeighborhood(15, similarity, plusAnonymModel);
var recommender = new GenericUserBasedRecommender(plusAnonymModel, neighborhood, similarity);
var recommendedItems = recommender.Recommend(PlusAnonymousUserDataModel.TEMP_USER_ID, 5, null);
var movieIds = recommendedItems.Select(ri => (int)ri.GetItemID()).ToArray();
var movieIdToTitleDictionary = _unitOfWork.MovieRepository.GetMovieIdToTileDictionary(movieIds);
var recommendedMovies = new List <MovieRecommendationDto>();
foreach (var item in recommendedItems)
{
var movieId = (int)item.GetItemID();
var movieTitle = movieIdToTitleDictionary[movieId];
recommendedMovies.Add(
new MovieRecommendationDto
{
MovieId = movieId,
MovieTitle = movieTitle,
Rating = item.GetValue()
});
}
return(recommendedMovies);
}
public ActionResult Recommend(string filmIdsJson)
{
var filmIds = (new JavaScriptSerializer()).Deserialize <long[]>(filmIdsJson);
var pathToDataFile =
Path.Combine(System.Web.HttpRuntime.AppDomainAppPath, "data/albums.dat");
if (dataModel == null)
{
try
{
dataModel = new FileDataModel(pathToDataFile, false, FileDataModel.DEFAULT_MIN_RELOAD_INTERVAL_MS, false);
}
catch (Exception e)
{
var exe = e.ToString();
}
}
var plusAnonymModel = new PlusAnonymousUserDataModel(dataModel);
var prefArr = new GenericUserPreferenceArray(filmIds.Length);
prefArr.SetUserID(0, PlusAnonymousUserDataModel.TEMP_USER_ID);
for (int i = 0; i < filmIds.Length; i++)
{
prefArr.SetItemID(i, filmIds[i]);
prefArr.SetValue(i, 5); // lets assume max rating
}
plusAnonymModel.SetTempPrefs(prefArr);
var similarity = new LogLikelihoodSimilarity(plusAnonymModel);
var neighborhood = new NearestNUserNeighborhood(15, similarity, plusAnonymModel);
var recommender = new GenericBooleanPrefUserBasedRecommender(plusAnonymModel, neighborhood, similarity);
var recommendedItems = recommender.Recommend(PlusAnonymousUserDataModel.TEMP_USER_ID, 5, null);
return(Json(recommendedItems.Select(ri => new Dictionary <string, object>()
{
{ "id", ri.GetItemID() },
{ "rating", ri.GetValue() },
}).ToArray()));
}
/// <summary>
/// 推荐
/// </summary>
/// <param name="pageIndex">当前页</param>
/// <param name="pageSize">页容量</param>
/// <param name="showCount">显示数量</param>
/// <returns></returns>
public List <Books> RecommendBooks(int pageIndex, int pageSize, int showCount)
{
#region 推荐
List <Books> books = null;
if (Session["user"] != null)
{
Users user = Session["user"] as Users;
#region 构建用户行为数组
var loglist = logbll.LoadEntities(c => c.userID == user.Id).ToList();
StringBuilder sb = new StringBuilder();
if (loglist.Count > 0)
{
sb.Append("[");
int j = 0;
foreach (var item in loglist)
{
j++;
sb.Append(item.itemID.ToString());
if (j != loglist.Count)
{
sb.Append(",");
}
}
sb.Append("]");
}
#endregion
if (string.IsNullOrEmpty(sb.ToString()))
{
//冷启动
books = booksbll.LoadEntities(c => true).OrderByDescending(c => c.rating).Skip((pageIndex - 1) * pageSize).Take(pageSize).ToList();
}
else
{
var filmIds = (new JavaScriptSerializer()).Deserialize <long[]>(sb.ToString());
var logmodel = settingbll.LoadEntities(c => c.id == 16).FirstOrDefault();
string path = "";
if (logmodel != null && logmodel.value == "true")
{
path = "data/ratings1.dat";
}
else
{
path = "data/ratings.dat";
}
var pathToDataFile =
Path.Combine(System.Web.HttpRuntime.AppDomainAppPath, path);
if (dataModel == null)
{
dataModel = new FileDataModel(pathToDataFile, false, FileDataModel.DEFAULT_MIN_RELOAD_INTERVAL_MS, false);
}
var plusAnonymModel = new PlusAnonymousUserDataModel(dataModel);
var prefArr = new GenericUserPreferenceArray(filmIds.Length);
prefArr.SetUserID(0, PlusAnonymousUserDataModel.TEMP_USER_ID);
for (int i = 0; i < filmIds.Length; i++)
{
prefArr.SetItemID(i, filmIds[i]);
prefArr.SetValue(i, 5); // lets assume max rating
}
plusAnonymModel.SetTempPrefs(prefArr);
var similarity = new LogLikelihoodSimilarity(plusAnonymModel);
var neighborhood = new NearestNUserNeighborhood(15, similarity, plusAnonymModel);
var recommender = new GenericUserBasedRecommender(plusAnonymModel, neighborhood, similarity);
var recommendedItems = recommender.Recommend(PlusAnonymousUserDataModel.TEMP_USER_ID, showCount, null);
List <Books> newbooks = new List <Books>();
foreach (var item in recommendedItems)
{
int bid = Convert.ToInt32(item.GetItemID());
newbooks.Add(booksbll.LoadEntities(c => c.Id == bid).FirstOrDefault());
}
books = newbooks.Skip((pageIndex - 1) * pageSize).Take(pageSize).ToList();
}
}
else //不推荐
{
books = booksbll.LoadEntities(c => true).OrderByDescending(c => c.rating).Skip((pageIndex - 1) * pageSize).Take(pageSize).ToList();
}
#endregion
return(books.Count() <= 0 ? booksbll.LoadEntities(c => true).OrderByDescending(c => c.rating).Skip((pageIndex - 1) * pageSize).Take(pageSize).ToList() : books);
}
public ActionResult GetRecommendedbooks()
{
var csv = new CsvReader(new StreamReader(System.Web.HttpContext.Current.Server.MapPath("~/App_Data/books.csv")));
var records = csv.GetRecords <BookRecord>().ToList();
int favouriteGenre = db.Users.Where(u => u.UserName == User.Identity.Name).Select(u => u.genreID).SingleOrDefault();
var userRentals = db.Rentals.Where(m => m.rentalUser == User.Identity.Name).Join(db.Books,
r => r.rentalBook,
m => m.bookName,
(r, m) => new
{
genreId = m.genreID,
bookName = m.bookName,
rentalUer = r.rentalUser
});
if (userRentals.Count() == 0)
{
return(Json(new Dictionary <string, object>()
{
{ "book_id", 0 },
{ "rating", 0 },
}));
}
else if (userRentals.Where(m => m.genreId == favouriteGenre).Count() != 0)
{
userRentals = userRentals.Where(m => m.genreId == favouriteGenre);
}
long[] bookIdsTemp = new long[userRentals.Count()];
int bookCounter = 0;
foreach (var item in userRentals)
{
foreach (var record in records)
{
if (item.bookName == record.title)
{
if (!bookIdsTemp.Contains(record.bookId))
{
bookIdsTemp[bookCounter] = record.bookId;
bookCounter++;
}
}
}
}
long[] bookIds = new long[bookCounter];
for (int i = 0; i < bookCounter; i++)
{
bookIds[i] = bookIdsTemp[i];
}
var dataModel = GetDataModel();
// recommendation is performed for the user that is missed in the preferences data
var plusAnonymModel = new PlusAnonymousUserDataModel(dataModel);
var prefArr = new GenericUserPreferenceArray(userRentals.Count());
prefArr.SetUserID(0, PlusAnonymousUserDataModel.TEMP_USER_ID);
for (int i = 0; i < bookIds.Length; i++)
{
prefArr.SetItemID(i, bookIds[i]);
// in this example we have no ratings of books preferred by the user
prefArr.SetValue(i, 5); // lets assume max rating
}
plusAnonymModel.SetTempPrefs(prefArr);
var similarity = new LogLikelihoodSimilarity(plusAnonymModel);
var neighborhood = new NearestNUserNeighborhood(15, similarity, plusAnonymModel);
var recommender = new GenericUserBasedRecommender(plusAnonymModel, neighborhood, similarity);
var recommendedItems = recommender.Recommend(PlusAnonymousUserDataModel.TEMP_USER_ID, 1, null);
if (recommendedItems.Count() == 0)
{
return(Json(new Dictionary <string, object>()
{
{ "book_id", 0 },
{ "rating", 0 },
}));
}
return(Json(recommendedItems.Select(ri => new Dictionary <string, object>()
{
{ "book_id", ri.GetItemID() },
{ "rating", ri.GetValue() },
}).ToArray()[0]));
}
