Building a mobile movie recommendation service by user rating and APP usage with linked data on Hadoop

Hsieh, Meng-Yen; Chou, Wen-Kuang; Li, Kuan‐Ching

doi:10.1007/s11042-016-3833-0

Cited by 29 publications

(22 citation statements)

References 23 publications

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“…In the proposed process, the similarity calculation was based on the log-likelihood ratio, which relies on the statistical similarity between two items or users and yielded a sufficient number of items for the recommendation. The log-likelihood ratio utilizes occurrences related to users or items such as users or items that overlap and the events for which both users and items do not have preferences [34,35]. Prediction algorithms estimate the rating that a user would provide for a target item [36].…”

Section: Recommendation System Developmentmentioning

confidence: 99%

The Prefiltering Techniques in Emotion Based Place Recommendation Derived by User Reviews

Ishanka¹,

Yukawa²

2017

Applied Computational Intelligence and Soft Computing

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Context-aware recommendation systems attempt to address the challenge of identifying products or items that have the greatest chance of meeting user requirements by adapting to current contextual information. Many such systems have been developed in domains such as movies, books, and music, and emotion is a contextual parameter that has already been used in those fields. This paper focuses on the use of emotion as a contextual parameter in a tourist destination recommendation system. We developed a new corpus that incorporates the emotion parameter by employing semantic analysis techniques for destination recommendation. We review the effectiveness of incorporating emotion in a recommendation process using prefiltering techniques and show that the use of emotion as a contextual parameter for location recommendation in conjunction with collaborative filtering increases user satisfaction.

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Section: Recommendation System Developmentmentioning

confidence: 99%

The Prefiltering Techniques in Emotion Based Place Recommendation Derived by User Reviews

Ishanka¹,

Yukawa²

2017

Applied Computational Intelligence and Soft Computing

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“…Due to the increasingly popularity of mobile apps, they are the subject of ongoing research efforts, including mobile recommendation systems via apps examined by Hsieh et al [8] in this special issue. The authors presented a collaborative filtering-based mobile app recommender system, which is designed to suggest movies to the users.…”

Section: Mobile Appsmentioning

confidence: 99%

Guest Editorial: Multimedia Social Network Security and Applications

Zhang

Choo

2016

Multimed Tools Appl

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In the increasingly inter-connected society, multimedia social networks (MSN) have become a 'mainstream' tool used by online users to connect and share contents with other users 24/7 in real-time. It is, therefore, unsurprising that MSNs have become a salient area of inquiry by computer scientists and computer security researchers. For example, researchers need to design intelligent computing and soft computing technologies to improve multimedia system functions, efficiency and performance, as well as improving user's sharing experiences (e.g. using recommendation systems and more effective algorithms). Ensuring the security of users and data are also an ongoing topic of interest and importance due to the ease in producing and sharing user and multimedia content using MSNs. In recent years, we have seen advances in multimedia system security and soft computing for MSN applications, such as Bhard^security mechanisms (e.g. protocols and methodologies) and Bsoft^computing methods (e.g. machine learning and rough set), as well as research efforts in trust assessment, risk management and social factors to understand the trade-off between the effectiveness and security in MSNs.This special issue is dedicated to the reporting of state-of-the-art and recent advancements in this emerging area of enquiry. We received 26 submissions for this special issue, of which 12 were accepted for publication (i.e. 46 % acceptance rate). Each paper went through a rigorous peer review process, in addition to multiple follow-up rounds with the authors. A summary of the papers is categorized and presented below.

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“…A generic recommender architecture is presented in Figure 2. Hsieh et al [5] has discussed that different algorithms and techniques are used in recommender system for considering the attribute of the users' reviews and ratings. R. Burke [6] has explain that such algorithms are divided into collaborative filtering (CF) and content-based filtering (CBF).…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers have introduced another approach known cluster based approach [5,10,14]. Collaborative filtering based on Clustering reduces computation time and focuses only on time efficiency improvement as the clustering phase is performed off-line.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

Ramzan

Bajwa

Jamil

et al. 2019

Scientific Programming

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This paper presents an intelligent approach to handle heterogeneous and large-sized data using machine learning to generate true recommendations for the future customers. The Collaborative Filtering (CF) approach is one of the most popular techniques of the RS to generate recommendations. We have proposed a novel CF recommendation approach in which opinion based sentiment analysis is used to achieve hotel feature matrix by polarity identification. Our approach combines lexical analysis, syntax analysis and semantic analysis to understand sentiment towards hotel features and the profiling of guest type (solo, family, couple etc). The proposed system recommends hotels based on the hotel features and guest type as additional information for personalized recommendation. The developed system not only has the ability to handle heterogeneous data using big data Hadoop platform but it also recommend hotel class based on guest type using fuzzy rules. Different experiments are performed over the real world dataset obtained from two hotel websites. Moreover, the values of precision and recall and F-measure have been calculated and results are discussed in terms of improved accuracy and response time, significantly better than the traditional approaches.

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Building a mobile movie recommendation service by user rating and APP usage with linked data on Hadoop

Cited by 29 publications

References 23 publications

The Prefiltering Techniques in Emotion Based Place Recommendation Derived by User Reviews

The Prefiltering Techniques in Emotion Based Place Recommendation Derived by User Reviews

Guest Editorial: Multimedia Social Network Security and Applications

An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

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