Thạc Sĩ Adaptive neuro-Fuzzy network for recommendation 

vi

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 vii

3.4.4 Movie 2848 33
3.4.5Movie 2548 . 34
3.5 Evaluation results 36
Chapter 4: Conclusion . 38
References . 39

viii

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
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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

x

This Thesis based on Publications
International Conference Publications (Accepted)
Duc Anh Nguyen and Trong Hai Duong, “Video Recommendation Using Neuro-
Fuzzy 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.

xi

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.
1

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 Collaborative-
based, 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 2

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 3

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 content-
based 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]. 4

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 5

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 Neuro-
Fuzzy 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 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.