public void TestFile()
{
UserCorrelation userCorrelation = new PearsonCorrelation(model);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, userCorrelation, model);
Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, userCorrelation);
Assert.AreEqual(2, recommender.Recommend("A123", 3).Count);
Assert.AreEqual(2, recommender.Recommend("B234", 3).Count);
Assert.AreEqual(1, recommender.Recommend("C345", 3).Count);
// Make sure this doesn't throw an exception
model.Refresh();
}
public void TestDatabase()
{
UserCorrelation userCorrelation = new PearsonCorrelation(model);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, userCorrelation, model);
Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, userCorrelation);
Assert.AreEqual(2, recommender.Recommend("A123", 3).Count);
Assert.AreEqual(2, recommender.Recommend("B234", 3).Count);
Assert.AreEqual(1, recommender.Recommend("C345", 3).Count);
// Make sure this doesn't throw an exception
model.Refresh();
}
public void testFile()
{
IUserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model);
IUserNeighborhood neighborhood = new NearestNUserNeighborhood(3, userSimilarity, model);
IRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, userSimilarity);
Assert.AreEqual(1, recommender.Recommend(123, 3).Count);
Assert.AreEqual(0, recommender.Recommend(234, 3).Count);
Assert.AreEqual(1, recommender.Recommend(345, 3).Count);
// Make sure this doesn't throw an exception
model.Refresh(null);
}
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()));
}
public void TestHowMany()
{
List<User> users = new List<User>(3);
users.Add(GetUser("test1", 0.1, 0.2));
users.Add(GetUser("test2", 0.2, 0.3, 0.3, 0.6));
users.Add(GetUser("test3", 0.4, 0.4, 0.5, 0.9));
users.Add(GetUser("test4", 0.1, 0.4, 0.5, 0.8, 0.9, 1.0));
users.Add(GetUser("test5", 0.2, 0.3, 0.6, 0.7, 0.1, 0.2));
DataModel dataModel = new GenericDataModel(users);
UserCorrelation correlation = new PearsonCorrelation(dataModel);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, correlation, dataModel);
Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, correlation);
IList<RecommendedItem> fewRecommended = recommender.Recommend("test1", 2);
IList<RecommendedItem> moreRecommended = recommender.Recommend("test1", 4);
for (int i = 0; i < fewRecommended.Count; i++)
{
Assert.AreEqual(fewRecommended[i].Item, moreRecommended[i].Item);
}
}
public void TestRescorer()
{
List<User> users = new List<User>(3);
users.Add(GetUser("test1", 0.1, 0.2));
users.Add(GetUser("test2", 0.2, 0.3, 0.3, 0.6));
users.Add(GetUser("test3", 0.4, 0.4, 0.5, 0.9));
DataModel dataModel = new GenericDataModel(users);
UserCorrelation correlation = new PearsonCorrelation(dataModel);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(1, correlation, dataModel);
Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, correlation);
IList<RecommendedItem> originalRecommended = recommender.Recommend("test1", 2);
IList<RecommendedItem> rescoredRecommended =
recommender.Recommend("test1", 2, new ReversingRescorer<Item>());
Assert.IsNotNull(originalRecommended);
Assert.IsNotNull(rescoredRecommended);
Assert.AreEqual(2, originalRecommended.Count);
Assert.AreEqual(2, rescoredRecommended.Count);
Assert.AreEqual(originalRecommended[0].Item, rescoredRecommended[1].Item);
Assert.AreEqual(originalRecommended[1].Item, rescoredRecommended[0].Item);
}
public void TestHowMany()
{
List <User> users = new List <User>(3);
users.Add(GetUser("test1", 0.1, 0.2));
users.Add(GetUser("test2", 0.2, 0.3, 0.3, 0.6));
users.Add(GetUser("test3", 0.4, 0.4, 0.5, 0.9));
users.Add(GetUser("test4", 0.1, 0.4, 0.5, 0.8, 0.9, 1.0));
users.Add(GetUser("test5", 0.2, 0.3, 0.6, 0.7, 0.1, 0.2));
DataModel dataModel = new GenericDataModel(users);
UserCorrelation correlation = new PearsonCorrelation(dataModel);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, correlation, dataModel);
Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, correlation);
IList <RecommendedItem> fewRecommended = recommender.Recommend("test1", 2);
IList <RecommendedItem> moreRecommended = recommender.Recommend("test1", 4);
for (int i = 0; i < fewRecommended.Count; i++)
{
Assert.AreEqual(fewRecommended[i].Item, moreRecommended[i].Item);
}
}
public void TestRescorer()
{
List <User> users = new List <User>(3);
users.Add(GetUser("test1", 0.1, 0.2));
users.Add(GetUser("test2", 0.2, 0.3, 0.3, 0.6));
users.Add(GetUser("test3", 0.4, 0.4, 0.5, 0.9));
DataModel dataModel = new GenericDataModel(users);
UserCorrelation correlation = new PearsonCorrelation(dataModel);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(1, correlation, dataModel);
Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, correlation);
IList <RecommendedItem> originalRecommended = recommender.Recommend("test1", 2);
IList <RecommendedItem> rescoredRecommended =
recommender.Recommend("test1", 2, new ReversingRescorer <Item>());
Assert.IsNotNull(originalRecommended);
Assert.IsNotNull(rescoredRecommended);
Assert.AreEqual(2, originalRecommended.Count);
Assert.AreEqual(2, rescoredRecommended.Count);
Assert.AreEqual(originalRecommended[0].Item, rescoredRecommended[1].Item);
Assert.AreEqual(originalRecommended[1].Item, rescoredRecommended[0].Item);
}
public void testHowMany()
{
IDataModel dataModel = getDataModel(
new long[] {1, 2, 3, 4, 5},
new Double?[][] {
new double?[]{0.1, 0.2},
new double?[]{0.2, 0.3, 0.3, 0.6},
new double?[]{0.4, 0.4, 0.5, 0.9},
new double?[]{0.1, 0.4, 0.5, 0.8, 0.9, 1.0},
new double?[]{0.2, 0.3, 0.6, 0.7, 0.1, 0.2},
});
IUserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
IUserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, dataModel);
IRecommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
IList<IRecommendedItem> fewRecommended = recommender.Recommend(1, 2);
IList<IRecommendedItem> moreRecommended = recommender.Recommend(1, 4);
for (int i = 0; i < fewRecommended.Count; i++) {
Assert.AreEqual(fewRecommended[i].GetItemID(), moreRecommended[i].GetItemID());
}
recommender.Refresh(null);
for (int i = 0; i < fewRecommended.Count; i++) {
Assert.AreEqual(fewRecommended[i].GetItemID(), moreRecommended[i].GetItemID());
}
}
public long[] GetNNearestNeighborsUsersRecommendations(int numNeighbours, int userId)
{
GenericDataModel model = GetUserBasedDataModel();
EuclideanDistanceSimilarity similarity = new EuclideanDistanceSimilarity(
model);
IUserNeighborhood neighborhood = new NearestNUserNeighborhood(
20, similarity, model);
long[] neighbors = neighborhood.GetUserNeighborhood(userId);
var recommender =
new GenericUserBasedRecommender(model, neighborhood, similarity);
var recommendedItems = recommender.Recommend(userId, 8);
return(neighbors);
}
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 List <int> GetRecommendations(int userId)
{
GenericDataModel model = GetUserBasedDataModel();
EuclideanDistanceSimilarity similarity = new EuclideanDistanceSimilarity(
model);
IUserNeighborhood neighborhood = new NearestNUserNeighborhood(
15, similarity, model);
long[] neighbors = neighborhood.GetUserNeighborhood(userId);
var recommender =
new GenericUserBasedRecommender(model, neighborhood, similarity);
var recommendedItems = recommender.Recommend(userId, 12);
var bookIds = recommendedItems.Select(ri => (int)ri.GetItemID()).ToList();
return(bookIds);
}
public ActionResult Test()
{
//构建用户行为数组
string filmIdsJson = "[6090,5461,5426]";
var filmIds = (new JavaScriptSerializer()).Deserialize <long[]>(filmIdsJson);
var pathToDataFile =
Path.Combine(System.Web.HttpRuntime.AppDomainAppPath, "data/ratings.dat");
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, 5, null);
return(Json(recommendedItems.Select(ri => new Dictionary <string, object>()
{
{ "film_id", ri.GetItemID() },
{ "rating", ri.GetValue() },
}).ToArray(), JsonRequestBehavior.AllowGet));
}
public void testFile()
{
IUserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model);
IUserNeighborhood neighborhood = new NearestNUserNeighborhood(3, userSimilarity, model);
IRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, userSimilarity);
Assert.AreEqual(1, recommender.Recommend(123, 3).Count);
Assert.AreEqual(0, recommender.Recommend(234, 3).Count);
Assert.AreEqual(1, recommender.Recommend(345, 3).Count);
// Make sure this doesn't throw an exception
model.Refresh(null);
}
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]));
}
public void testRescorer()
{
IDataModel dataModel = getDataModel(
new long[] {1, 2, 3},
new Double?[][] {
new double?[]{0.1, 0.2},
new double?[]{0.2, 0.3, 0.3, 0.6},
new double?[]{0.4, 0.5, 0.5, 0.9},
});
IUserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
IUserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, dataModel);
IRecommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
IList<IRecommendedItem> originalRecommended = recommender.Recommend(1, 2);
IList<IRecommendedItem> rescoredRecommended =
recommender.Recommend(1, 2, new ReversingRescorer<long>());
Assert.NotNull(originalRecommended);
Assert.NotNull(rescoredRecommended);
Assert.AreEqual(2, originalRecommended.Count);
Assert.AreEqual(2, rescoredRecommended.Count);
Assert.AreEqual(originalRecommended[0].GetItemID(), rescoredRecommended[1].GetItemID());
Assert.AreEqual(originalRecommended[1].GetItemID(), rescoredRecommended[0].GetItemID());
}
/// <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);
}
