ADAPTIVE NEURO-FUZZY NETWORK FOR RECOMMENDATION In Partial Fulfillment of the Requirements of the Degree of MASTER OF INFORMATION TECHNOLOGY MANAGEMENT In Computer Science and Engineer By Mr. Nguyen Duc Anh ID: MITM05001 International University - Vietnam National University HCMC March 2015 ADAPTIVE NEURO-FUZZY NETWORK FOR RECOMMENDATION In Partial Fulfillment of the Requirements of the Degree of MASTER OF INFORMATION TECHNOLOGY MANAGEMENT In Computer Science and Engineer By Mr. Nguyen Duc Anh ID: MITM05001 International University - Vietnam National University HCMC March 2015 Under the guidance and approval of the committee, and approved by all its members, this thesis has been accepted in partial fulfillment of the requirements for the degree. Approved: --------------------------------------------Chairperson ------------------------------------------Committee member --------------------------------------------Committee member ------------------------------------------Committee member --------------------------------------------Committee member ------------------------------------------Committee member ii Acknowledgments Throughout my thesis and development process, it is impossible for me to well complete all my tasks and missions without the support and encouragement from the other ones. At first, I would like to thank Dr. Duong Trong Hai. He is always by my side to support me identify the main ideas of this research this is the most important support for me. He instructs me to be familiar with data-mining, machine-learning, etc. Moreover, He is willing to give me helpful advices whenever I have difficulties or troubles with my thesis. I am grateful to my family, who encourages and motivates me to keep moving forward. There are also my colleagues, schoolmates who also support and help me directly and indirectly; I want to say thank all of them. iii Plagiarism Statements I would like to declare that, apart from the acknowledged references, this thesis either does not use language, ideas, or other original material from anyone; or has not been previously submitted to any other educational and research programs or institutions. I fully understand that any writings in this thesis contradicted to the above statement will automatically lead to the rejection from the MITM program at the International University – Vietnam National University Hochiminh City. iv Copyright Statement This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognize that its copyright rests with its author and that no quotation from the thesis and no information derived from it may be published without the author’s prior consent. © Nguyen Duc Anh/MITM05001/2012-2015 v Table of Contents Plagiarism Statements ............................................................................................................................ iv Copyright Statement ............................................................................................................................... v This Thesis based on Publications .......................................................................................................... x Abstract .................................................................................................................................................. xi Chapter 1: Introduction ........................................................................................................................... 1 1.1 Motivation ..................................................................................................................................... 1 1.2 Goals of the Dissertation ............................................................................................................... 1 1.3 Overal Approach ........................................................................................................................... 1 1.4 Related Work ................................................................................................................................ 2 1.5 Thesis Outline ............................................................................................................................... 5 Chapter 2: User Behaviors-based CF Using Neuro-Fuzzy Network ...................................................... 7 2.1 Profile Modeling ........................................................................................................................... 7 2.2 Content-based Filtering Using Neuro-Fuzzy Network ................................................................. 8 Chapter 3. Experiments ......................................................................................................................... 12 3.1 Dataset Introduction .................................................................................................................... 12 3.1.1 Overview .............................................................................................................................. 12 3.1.2 Dataset analysis ........................................................................................................................ 13 3.2 Applied ANFIS to netflix dataset................................................................................................ 14 3.2.1 ANFIS Model....................................................................................................................... 14 3.2.2 Run the testing dataset ......................................................................................................... 25 3.3 Evaluation Methods .................................................................................................................... 27 3.4 Practice and Result ...................................................................................................................... 27 3.4.1 Movie 329 ............................................................................................................................ 28 3.4.2 Movie 30 .............................................................................................................................. 30 3.4.3Movie 2464 ........................................................................................................................... 31 vi 3.4.4 Movie 2848 .......................................................................................................................... 33 3.4.5Movie 2548 ........................................................................................................................... 34 3.5 Evaluation results ........................................................................................................................ 36 Chapter 4: Conclusion........................................................................................................................... 38 References ............................................................................................................................................. 39 vii List of Figures Fg 2.1.1 1 Profile generation process .................................................................................... 8 Fg3.1.1 1 Netflix Dataset structure ...................................................................................... 12 Fg3.1.2 1 Rating-scores statistic .......................................................................................... 13 Fg3.1.2 2 The rating-scores comparison for top 10 movies have highest number of rating14 Fg3.2.1 1 The ANFIS’s structure ......................................................................................... 14 Fg3.2.1 2 The main workflow of ANFIS............................................................................... 15 Fg3.2.1 3Sample of user profile Level 1 .............................................................................. 16 Fg3.2.1 4 Sample of user profile Level 2 ............................................................................. 18 Fg3.2.1 5HyperBox dataset and PureBox Dataset where before and after clustered by NCP 18 Fg3.2.1 6 Samples of purebox clusters ................................................................................ 20 Fg3.2.1 7 The Max-Min PureBox......................................................................................... 21 Fg3.2.1 8 The final result of the user profile building steps ................................................ 22 Fg3.2.1 9 Samples of data use to training by Perceptron .................................................... 23 Fg3.3. 1 Distribution of Tranning set and Testing set in dataset ........................................ 28 Fg3.4.2. 1 Comparison between training data set and real dataset of movie 30 ................ 30 Fg3.4.2. 2 Result of 100 samples used to test for movie 30 ................................................. 31 Fg3.4.3. 1 Comparison between training data set and real dataset of movie 2464 ............ 32 Fg3.4.3. 2 Result of 100 samples used to test for movie 2464 ............................................. 32 Fg3.4.4. 1 Comparison between training data set and real dataset of movie 2848 ............ 33 Fg3.4.4. 2 Result of 100 samples used to test for movie 2848 ............................................. 34 Fg3.4.5. 1 Comparison between training data set and real dataset of movie 2548 ............ 35 Fg3.4.5. 2 Result of 100 samples used to test for movie 2548 ............................................. 35 Fg3.5. 1 MAE and RMSE of movies 2464,2548,30,2848,329 .............................................. 36 viii List of Table Table 3.2.1 1 Samples of W had computed by Perception for Movie 329 ........................... 23 Table 3.2.2.1Predict Rating-scores for 5 userssamples, movie 329 .................................... 26 Table 3.4.1.1 Comparison between training data set and real dataset of movie 329 .......... 28 Table3.4.2. 1 Comparison between training data set and real dataset of movie 30 ............ 30 Table3.4.3. 1 Comparison between training data set and real dataset of movie 2464 ........ 31 Table3.4.4. 1 Comparison between training data set and real dataset of movie 2848 ........ 33 Table3.4.5. 1 Comparison between training data set and real dataset of movie 2548 ........ 34 ix This Thesis based on Publications International Conference Publications (Accepted) Duc Anh Nguyen and Trong Hai Duong, “Video Recommendation Using NeuroFuzzy on Social TV Environment”, International conference on Computer Science, Applied Mathematics and Applications (ICCSAMA 2015) published in a volume of series Advances in Intelligent Systems and Computing of Springer Verlag, indexed by ISI Proceedings, DBLP, Ulrich's, EI-Compendex, SCOPUS, Zentralblatt Math, MetaPress, Springerlink. Issues in ISI-SCI journals. International Journal Publications (Submitted) Trong Hai Duong and Duc Anh Nguyen, “User Behaviors-based Collaborative Filtering for Video Recommendation Using Ontology-based Neuro-Fuzzy on Social TV”, ELSEVIER, 03-2015. x Abstract Recommendation systems are systems that seek for prediction and give users recommendation about products or items that they might be interested in. There are two common approaches, which have been proposed to perform recommendation system; they are content-based filtering (CBF) and collaborative filtering (CF). CBF methods are based on the description of previously preferred items to predict a target user’s rating. On the other hand, CF methods are based on neighbors’ ratings to predict a target user’s rating. In this work, we consider recommendation on the context of Social TV (STV). The watchers/users may either share, comment, rate, or tag videos in which they are interested in. Each video must be watched and rated by many users. For these assumptions, we proposed a novel model-based collaborative filtering using a fuzzy neural network to learn user’s social web behaviors to make video recommendation on STV. We use Netflix data-set to evaluate the proposed method. The result shown that the proposed approach is a significant effective method. Keywords: ANFIS, Ontology, Smart TV, Video, Recommendation system, and Neural network. xi Chapter 1: Introduction 1.1 Motivation Recommendation is a subclass of information filtering, which uses data on past user preferences to predict possible future likes and interests. There are few approaches which applied in recommendation system such as Collaborativebased, Demographic-based, Content-based, Knowledge –based, Hybrid-based Recommendation. Prior collaborative filtering (CF) methods based on neighbors’ ratings to predict a target user’s rating. A situation that there are no any neighbors, the traditional CF’s result is gone downhill. To solve the aforementioned problem, we proposed a novel model-based collaborative filtering using a fuzzy neural network to learn user’s social web behaviors for video recommendation on STV. 1.2 Goals of the Dissertation Our goals in this thesis focused on solve the problem of lack of neighbors in the traditional CF. In that, we predict unknown rating from a target user to a target video by adjusting users profile and rating-scale values using ANFIS. 1.3 Overal Approach The idea of the proposed method is to adjust users’ social web behavior to their owning ratings dual with a target video. In particular, a user profile is learned by the user’s social web behavior. This user profile is presented by a vector. For each target video, we collect all users’ profiles who rated on the target video. Each user’s profile are considered as an input vector and his/her corresponding rating-score is as output value of the fuzzy neural network. The 1 trained neural network is used to predict the rating of a user to the target video. We use netflix data set to evaluate the proposed method. 1.4 Related Work The trend for using online social networks to talk about TV programs and to share their opinions with others, is increasing. This reflected with the dissemination of platforms designed for Social TV [1]. The NoTube [1] brings the social web and TV closer to the consumers. The social TV is able to provide users’ social context that personalize users’ TV program and video with both of content-based and collaborative-based filtering manners. Content-based filtering (CBF)[4] relies on the description of previously preferred items of a target user and generates recommended items with content are similar to those the target user has preferred in the past without directly relying on the preferences of other users. Collaborative filtering (CF) [5] relies on the basis of previously preferred items of a large group of users’ rating information and make recommended items to a target user based on the items that similar users have preferred in the past, without relying on any information about the items themselves other than their ratings. According to algorithms of CF, CF can be grouped into two types: (a) Memory-based collaborative filtering methods recommend items are those that were previously preferred by users who share similar preferences as the target user [6]. These algorithms require all ratings, items, and users to be stored in memory. (b) Model-based collaborative filtering methods recommend items based on models that are trained by using the collection of ratings to identify patterns in the input data [7]. The memory-based collaborative filtering store the training data in memory that is delayed until a recommendation is made to the system, as 2 opposed to model-based collaborative filtering, where the system tries to generalize a model using the training data before recommendation making. The advantage of memory-based methods is deal with less parameters to be tuned, while the disadvantage is that the approach cannot deal with data scarcity in a principled manner [9]. In Social TV, recommendation systems have been developed to help users access TV programs that are appropriate to their preferences by learning from viewing history data, mapping social users’ preferences and TV program attributes [15, 16, 9]. Authors [9] proposed hybrid approach combining contentbased methods with those based on collaborative filtering for TV program recommendation. To eliminate the overload computation of collaborative filtering, singular value decomposition technique [17] is applied in order to reduce the dimension of the user-item representation, and afterwards, how this low-rank representation can be employed in order to generate item-based prediction, which has shown a good behavior in the TV domain. Authors [10] proposed a framework for adaptive news recommendation in social media by utilizing user’s comments. User’s comments are collected to build a topic profile using a weighted graph. To generate the weighted importance of topics, the standard TF/IDF model [11] and variant of the PageRank algorithms [12] are applied. With the topic profile constructed, it can be used to select relevant news from a collection of news articles in the database by constructing a retrieval module using combination of the strengths of two state-of-the-art news retrieve time factor [13] and language model [14]. 3 In fact, there are many researches on recommendation systems. One of them is the research named: “Neural Network Modeling for an Intelligent Recommendation System Supporting SRM for Universities in Thailand”, [21] proposed by Kanokwan Kongsakun and Chun Che Fung. This is a recommendation system proposal, used to predict and recommend the appropriate courses for students thereby increase their chance of success. Their proposal is based on students' historic records and final results. The authors used Neural Network techniques to find the structures and relationships within data and final GPA of freshmen in subjects of interest. The authors [21] had come to the conclusion that recommendation system is a useful service. According to another research named: “A Hybrid Latent Variable Neural Network Model for Item Recommendation” [22]. The authors [22] proposed neural network model with latent input variables named Latent Neural Network (LNN), as a hybrid collaborative filtering of both approaches CF and CBF. The strong point of LNN is that it addressed the cold-start problem, but the complexity of LNN requires more time to train than others. In additional, LNN is capable of modeling higher-order dependencies and nonlinearities in the data; but in fact the data in MovieLens data-set, Netflix data-set and the similar datasets are inherently sparse and nonlinear models. Thus, their proposal is not suitable as well for that kind of data. Another method proposed by Christina Jianfeng Gao, Patrick Pantel, Michael Gamon, Xiaodong He, Li Deng [23] named “Modeling Interestingness with Deep Neural Networks”, this is a recommendation system to recommend users a target document they may interested in, based on analyzing the 4 documents which they have read. According to this research, the authors [23] used two interestingness tasks: automatic highlighting and contextual entity search within their proposal. Another interesting proposal named: “A Hybrid Movie Recommender System Based on Neural Networks” [24], in which the authors [24] proposed a hybrid filtering approach to combine CF and CBF. Their model had archived overall 82% of successful recommendations, although the authors said it seems strange that the precision falls as the user has evaluated many movies. They came up with the final conclusion saying that the reason is as the watcher/user keeps evaluating movies, it is possible that user has covered a wide range of movies that share a common characteristic features (Kinds, Stars, Synopsis), while being totally different and, subsequently, differently evaluated [24]. 1.5 Thesis Outline In this thesis, about which, the introduction in chapter 1 aims to reveal the problems I have been conducting a research and the parameters included in my thesis research paper. The second part is chapter 2 named “User Behaviors-based CF Using NeuroFuzzy Network”. The main purpose of this part is to analyze in detail the relevant theories such as User modeling, ANFIS, TF/IDF, Perceptron, etc. which will apply in my thesis research paper. Chapter 3 is Experiment. This chapter introduces about applying the proposed novel ANFIS for Video recommendation system and introduces the 5 Evaluation methods which I used to evaluate the results, In this thesis, I used Netflix as a sample dataset. Finally, chapter 4 is the last one of my thesis report, it presents the conclusion. 6 Chapter 2: User Behaviors-based CF Using Neuro-Fuzzy Network 2.1 Profile Modeling User profile can be static and dynamic information. In static, user permanent information such as name, age, sex, educational background etc. is included, whereas the dynamic user profile, the less permanent characteristics like user’s current motions, locations are mentioned; however, the user interest which often changes is mainly included. Here, we consider the profile with only user interest, which is user’s social web behavior such as user’s posts, comments, share, ratings, preferences, and tags. The user profile is represented by using a weighted vector defined as follows: Definition 1 (Profile Feature).Let feature pi is defined as follows: ={( be a profile of an user ), ( ),…, ( . The profile )} is a set of pairs of concept and its weight. The process used to generate a user’s profile, which is presented in Fig: 2.1.1.1. The tf /idf weight (term frequency inverse document frequency) is a weight often used in information retrieval and text mining. This weight is a statistical measure used to evaluate how important a word is to a document in a collection or corpus. Here we use traditional vector space model (tf /idf) to define the feature of the documents [18]. 7 Fg 2.1.1 1 Profile generation process 2.2 Content-based Filtering Using Neuro-Fuzzy Network We assume that user ui interests in a set ={ , , …, } of movies (he/she watched and made a rating to them). The user’s profile can be considered as a feature vector: from movies in = {( and that each movie { , ,…, ), ( ), ..., ( )}, where is a genre generated by using vector space tf/idf. We assume ; j = 1..k also can be interested by n users . The rating-score of the movie can be denoted as is denoted by with respect to the user , so the rating-score set of a movie ={ , ,…, = with respect to }. For each movie , we consider a black-box-typed model expressing a mathematical relationship between a input of feature vectors of users in ={ ,…, , } and a output of the rating-score space, given data set of corresponding users , is the feature vector of from user to movie , denoted by { ={ , as follows: ( user of the data set ,…, , and , , …, }, based on a ), i = 1…k where is rating-score , as an output. This work can be seen as system- identifying process, in which the model works as a mathematical function f expressed by a mapping as follows: 8 f: (1) | =f( ) In this paper, the above mathematical model is expressed by a fuzzy-neuron structure (FNS), which is a combination of a fuzzy inference system (FIS) and a neural network structure (NNS). Relating to the FIS, it can be summarized as follows. The FIS is built based on the algorithm establishing an adaptive neuro-fuzzy system, ENFS [3]. By using data driven method, the same features or characteristics of the object are expressed by hyperbox-typed data clusters, which can be considered as a structure upon which fuzzy sets and membership functions are established to build the FIS. In the FIS, the fuzzy deducing rules are built based on constituting clauses depicting the fuzzy relationships typing MISO as following: = IF where and = and … and = THEN = (2) is language variables expressing the result of clustering process; = 1…M is maximum membership value of the sample in -labeled data clusters, which is used to establish the corresponding hyperbox value sample; is constituting rule; and sample. We consider a set of the patterns covered by the h min-max hyperbox .The is determined using two vertexes, the max vertex , …, ] and the min vertex |( ) of this is the constituting value, which is used to calculate the predicting value of the max( ;k =[ and , , …, . If ], where , = consists of the patterns associated with the cluster labeling m only, then the 9 =[ will be considered as a pure hyperbox labeling m, and denoted .An HB can be considered as a crisp frame on which different types of membership functions (MFs) can be adapted. Here, the original Simpson’s MF is adopted, in which the slope outside the HB is established by the value of the fuzziness parameter . = f(x, ) = Where t = ; (3) is the number of pure hyperboxes labeling m. Several pHB can be associated with the same cluster labeling m, thus the overall input MF, ( is calculated as follows: ( , …, = max{ } (4) The process of the ANFIS can be summarized as follows: Choose the number of neurons of the hidden layer Step 1. Separate the data set {( , . ), i = 1..k} (1) to build data clusters , i = 1…m Using the algorithm for parting data space, PDS [2], the given data set (1) is separated into hyper box-typed data clusters in the input space and hyper planes, , i = 1..m, in the output data space. Where, M is optimal number of data clusters established by the clustering process. Step 2. Build a new data set, named NN-set, for training the NN The NN-set has k samples with input-output samples depicted by (1). Step 3. Train the NN 10 The NN-set is used for train the NN based on the algorithm Le-venbergMarquardt. - Calculate values of MFs based on equations (3) and (4); - The output of the neuro-fuzzy network is calculated as following equations: = (5) , k = 1…M = (6) Step 4.Check for stopping condition Calculate error between output of the NN-set and corresponding depicting output of the NN = - If EN [E] : the structure FNS based on the NN is chosen; - If EN > [E] : N=N+1 then return to Step 3. 11 Chapter 3. Experiments 3.1 Dataset Introduction 3.1.1 Overview The sample dataset used is netflix data set which contains 14,707,483ratings which performed by 459,340 anonymous NetFlix’s customers over 17,770movies, from 1999-11-11 to 2005-12-31. The rating scale has 5 values: 5 is excellent, 4 is very good, 3 is good, 2 is fair, and 1 is poor. Fg3.1.1 1 Netflix Dataset structure There are 2 primary tables named “rating” and “movie_info”. The first one is rating, it has 9 columns: movie_id, genre,rating, director, writers, star, image_link, host, content_rating. The table “rating” has 4 columns: User_id, movie_id, rating, date. 12 3.1.2 Dataset analysis The Customer’s rating-score was stored into a table named “rating”. It records 14,7 million of user's rating, each record represents a single rating of one movie_id by one user_id, and some additional information as user's rating score, date. All of them are anonymous rates, the lowest rating score is the label of 1 rating-score with 632 thousand rates, after that is the label of 2 rating-scores with 1,4 million rates, the label of 5 rating-scores with 3 million rates and the highest is the label of 4 rating-scores with 4,7 million rates, and the label of 3 ratingscores has 4 million rates. As shown in Fg3.1.2.1. Fg3.1.2 1 Rating-scores statistic Fg3.1.2.2 shown the statistic of top 10 movies which have number of rating is highest 13 Fg3.1.2 2 The rating-scores comparison for top 10 movies have highest number of rating 3.2 Applied ANFIS to netflix dataset 3.2.1 ANFIS Model Fg3.2.1 1 The ANFIS’s structure 14 As shown in Fg3.2.1.1 (the ANFIS structure) and Fg3.2.1.2 (the ANFIS’s workflow),The dataset has 4 main steps: Step 1: Build User profiles level 1 & 2 Step 2:Cluster user profile dataset into PureBoxs Step 3: Build User profiles by computing the distance between users and 5 groups of Purebox. Step 4:Do Perceptron Fg3.2.1 2 The main workflow of ANFIS 15 Step 1: Build User profiles level 1 & 2 Step 1.1: Build User profiles level 1 There are more than 40 genres in the whole dataset, but some of them have the same meaning, such as "Sport" and "Sports", etc. So in total, the Netflix dataset has only 37 different genres, in following: Action, Adult, Adventure,Animation,Anime, Biography, Children, Classics, Comedy, Crime, Documentary, Drama, Faith, Family, Fantasy, FilmNoir, Fitness, Foreign, Game Show, Gay, History, Horror, Independent, Lesbian, Music, Mystery, Romance, Sci-Fi, Short, Special Interest, Spirituality, Sport, Talk Show, Television, Thriller, War, Western For each user, we create a corresponding profile represented by a feature vector including 37 genres as its components. The outcome of this step will look like the figure below: Fg3.2.1 3Sample of user profile Level 1 16 Step 1.2: Build User profiles level 2 Based on the outcome of previous step, We apply TF/IDF to be weight how important the genre is to each user in whole dataset of user profiles. - |D|: Total number of users within dataset. There is 459,340 users, it means |D| = 459,340. - : Total number of users where the genre had appreared - : Summary of values in 37 genres for a user : Value of the genre at position i For example:Computing the TF/IDF for user id “2181339” which appear in Fg3.2.1.4. - |D| = 459,340. - = 372,014 - =(46+0+40+3+0+35+2+5+187+64+7+199+0+8+26+0+0+0+0+0+ 0+3+0+0+ 25+17 +88+3+8+0+0+18+0+0+27+3+0) = 814 - =46 17 Fg3.2.1 4 Sample of user profile Level 2 Step 2:Clustering user profile dataset into PureBoxs We applied A new cutting procedure - NCP[2] to do clustering the training dataset (1000 samples) into pureboxes. Fg 3.2.1.5 shows result of pureboxes clustered by NCP. Fg3.2.1 5HyperBox dataset and PureBox Dataset where before and after clustered by NCP 18 For each HyperBox, consider the two classes of rating labels named labeltop1 and labeltop2 which are the most frequent ones with respect to the wholenumber of the patterns covered by this HyperBox.We count the number of users who attend in the top 2 rating labelsinto 2 variables: nh_1 and nh_2. Follow that, we do computing the average point between 2 centroids of labeltop1and labeltop2 into a new feature vector named HBAverage. For every genres, let n1 be the number of users in labeltop1which have the current genre’svalue is greater than the current genre’s value of HBAverage. Let n2 be the number of users in labeltop2 which have the current genre’s value less than the current genre’s value of HBAverage. Let Apply Penalty function in below for WReturn. declare @E1 decimal(20,10),@E2 decimal(20,10),@Delta decimal(20,10) select @E1=0.05,@E2=0.95,@Delta=0.335 While (@genre) begin if @WReturn=@E2 set @WReturn=@WReturn+@Delta else set @WReturn=1 if (@WMax< @genre) set @WMax=@genre end Let WReturn_Max is the greatest value of WReturn and WMax_genre is the genre’s position of this WReturn_Max. 19 Finally, cut the Hyperbox into two temporary HyperBox-es at WReturn_Max via WMax_genre. If there is at least one temporary HyperBox is contains only one label of rating-score, let’s move this HyperBox to the PureBox dataset. If not let’s move them to HyperBox dataset and restart the NCP with the new HyperBox. The NCP will end if the HyperBox dataset is empty. The results of this step are shown in Fg 3.2.1.6. Fg3.2.1 6 Samples of purebox clusters Step 3: Build User profiles by computing the distance between users and 5 groups of Pureboxs’ Label. Step 3.1:According to the result of the pevious step (step 2). We apply Max-Min vertexes [2] to get Max() and Min() for each PureBox in the PureBox Dataset. Let @MaxRow and @MinRow be the variable storing the results of this step. As shown in Fg3.2.1.7. 20 Fg3.2.1 7 The Max-Min PureBox Step 3.2: Compute the distance from users to all PureBoxs in PureBox Dataset. set @gama=0.5 set @f1=(@DataVal-@MaxVal)*@gama set@f2=(@MinVal-@DataVal)*@gama if @f1>1 set @f1=1 elseif @f11 set @f2=1 elseif @f2[...]... 3 In fact, there are many researches on recommendation systems One of them is the research named: “Neural Network Modeling for an Intelligent Recommendation System Supporting SRM for Universities in Thailand”, [21] proposed by Kanokwan Kongsakun and Chun Che Fung This is a recommendation system proposal, used to predict and recommend the appropriate courses for students thereby increase their chance... f: (1) | =f( ) In this paper, the above mathematical model is expressed by a fuzzy- neuron structure (FNS), which is a combination of a fuzzy inference system (FIS) and a neural network structure (NNS) Relating to the FIS, it can be summarized as follows The FIS is built based on the algorithm establishing an adaptive neuro- fuzzy system, ENFS [3] By using data driven method, the same features or characteristics... authors used Neural Network techniques to find the structures and relationships within data and final GPA of freshmen in subjects of interest The authors [21] had come to the conclusion that recommendation system is a useful service According to another research named: “A Hybrid Latent Variable Neural Network Model for Item Recommendation [22] The authors [22] proposed neural network model with latent... TV, Video, Recommendation system, and Neural network xi Chapter 1: Introduction 1.1 Motivation Recommendation is a subclass of information filtering, which uses data on past user preferences to predict possible future likes and interests There are few approaches which applied in recommendation system such as Collaborativebased, Demographic-based, Content-based, Knowledge –based, Hybrid-based Recommendation. .. data set, named NN-set, for training the NN The NN-set has k samples with input-output samples depicted by (1) Step 3 Train the NN 10 The NN-set is used for train the NN based on the algorithm Le-venbergMarquardt - Calculate values of MFs based on equations (3) and (4); - The output of the neuro- fuzzy network is calculated as following equations: = (5) , k = 1…M = (6) Step 4.Check for stopping condition... this work, we consider recommendation on the context of Social TV (STV) The watchers/users may either share, comment, rate, or tag videos in which they are interested in Each video must be watched and rated by many users For these assumptions, we proposed a novel model-based collaborative filtering using a fuzzy neural network to learn user’s social web behaviors to make video recommendation on STV...Abstract Recommendation systems are systems that seek for prediction and give users recommendation about products or items that they might be interested in There are two common approaches, which have been proposed to perform recommendation system; they are content-based filtering (CBF) and collaborative filtering (CF)... part is chapter 2 named “User Behaviors-based CF Using NeuroFuzzy Network The main purpose of this part is to analyze in detail the relevant theories such as User modeling, ANFIS, TF/IDF, Perceptron, etc which will apply in my thesis research paper Chapter 3 is Experiment This chapter introduces about applying the proposed novel ANFIS for Video recommendation system and introduces the 5 Evaluation... sample dataset Finally, chapter 4 is the last one of my thesis report, it presents the conclusion 6 Chapter 2: User Behaviors-based CF Using Neuro- Fuzzy Network 2.1 Profile Modeling User profile can be static and dynamic information In static, user permanent information such as name, age, sex, educational background etc is included, whereas the dynamic user profile, the less permanent characteristics... target user’s rating A situation that there are no any neighbors, the traditional CF’s result is gone downhill To solve the aforementioned problem, we proposed a novel model-based collaborative filtering using a fuzzy neural network to learn user’s social web behaviors for video recommendation on STV 1.2 Goals of the Dissertation Our goals in this thesis focused on solve the problem of lack of neighbors .. .ADAPTIVE NEURO-FUZZY NETWORK FOR RECOMMENDATION In Partial Fulfillment of the Requirements of the Degree of MASTER OF INFORMATION TECHNOLOGY MANAGEMENT In Computer... Filtering for Video Recommendation Using Ontology-based Neuro-Fuzzy on Social TV”, ELSEVIER, 03-2015 x Abstract Recommendation systems are systems that seek for prediction and give users recommendation. .. there are many researches on recommendation systems One of them is the research named: “Neural Network Modeling for an Intelligent Recommendation System Supporting SRM for Universities in Thailand”,