Tìm kiếm
Đang tải khung tìm kiếm
Kết quả 1 đến 1 của 1

    THẠC SĨ Adaptive neuro-Fuzzy network for recommendation 

    D
    dream dream Đang Ngoại tuyến (18495 tài liệu)
    .:: Cộng Tác Viên ::.
  1. Gửi tài liệu
  2. Bình luận
  3. Chia sẻ
  4. Thông tin
  5. Công cụ
  6. 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
    ix

    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.

    Xem Thêm: Adaptive neuro-Fuzzy network for recommendation 
    Nội dung trên chỉ thể hiện một phần hoặc nhiều phần trích dẫn. Để có thể xem đầy đủ, chi tiết và đúng định dạng tài liệu, bạn vui lòng tải tài liệu. Hy vọng tài liệu Adaptive neuro-Fuzzy network for recommendation  sẽ giúp ích cho bạn.
    #1
  7. Đang tải dữ liệu...

    Chia sẻ link hay nhận ngay tiền thưởng
    Vui lòng Tải xuống để xem tài liệu đầy đủ.

    Gửi bình luận

    ♥ Tải tài liệu

social Thư Viện Tài Liệu

Từ khóa được tìm kiếm

Nobody landed on this page from a search engine, yet!

Quyền viết bài

  • Bạn Không thể gửi Chủ đề mới
  • Bạn Không thể Gửi trả lời
  • Bạn Không thể Gửi file đính kèm
  • Bạn Không thể Sửa bài viết của mình
  •  
DMCA.com Protection Status