Performance Improvement of a Movie Recommendation System based on Personal Propensity and Secure Collaborative Filtering

Jeong, Woon Hae; Kim, Se Jun; Park, Doo Soon; Kwak, Jin

doi:10.3745/jips.2013.9.1.157

Cited by 30 publications

(10 citation statements)

References 9 publications

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“…With the expansion of the Internet and the advent of smartphones, people now are able to get easy access to and extend their activities on the web [15]. Therefore, if information about roads having flood risk and safe paths is provided to people in advance through smartphone applications connected to an intelligent transportation system (ITS) or a navigation system, the damage caused by traffic congestion and drivers' isolation could be decreased.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Real-Time Flood Risk on Roads Based on Rainfall Calculated by the Revised Method of Missing Rainfall

Kim

Rhee

et al. 2014

Sustainability

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Recently, flood damage by frequent localized downpours in cities is on the increase on account of abnormal climate phenomena and the growth of impermeable areas due to urbanization. This study suggests a method to estimate real-time flood risk on roads for drivers based on the accumulated rainfall. The amount of rainfall of a road link, which is an intensive type, is calculated by using the revised method of missing rainfall in meteorology, because the rainfall is not measured on roads directly. To process in real time with a computer, we use the inverse distance weighting (IDW) method, which is a suitable method in the computing system and is commonly used in relation to precipitation due to its simplicity. With real-time accumulated rainfall, the flooding history, rainfall range causing flooding from previous rainfall information and frequency probability of precipitation are used to determine the flood risk on roads. The result of simulation using the suggested algorithms shows the high concordance rate between actual flooded areas in the past and flooded areas derived from the simulation for the research region in Busan, Korea.

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Section: Discussionmentioning

confidence: 99%

Estimation of Real-Time Flood Risk on Roads Based on Rainfall Calculated by the Revised Method of Missing Rainfall

Kim

Rhee

et al. 2014

Sustainability

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“…These methods filter or evaluate items through the opinions of other users [20] [14]. They are usually based on the assumption that the given user will prefer the items which other users with similar preferences liked in the past [1,13,24].…”

Section: Recommendation Systemmentioning

confidence: 99%

Recommendation system for sharing economy based on multidimensional trust model

Kim

Yoon

2014

Multimed Tools Appl

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The recommendation system are widely adopted in today's mainstream online sharing services, providing useful prediction of user's rating or user's preferences of sharing items (such as products, movies, books, and news articles). A key challenge of recommendation systems in sharing economy is to employ prediction algorithms to estimate the matching items with considering their interests and needs. The environment-context has been recognized as an important factor to consider in personalized recommender systems. Since dynamic information in environment-context describes the situation of items and users, the information affects the user's decision process essentially to apply in recommender systems. However, most model-based collaborative filtering approaches such as Matrix Factorization do not provide an easy way of integrating context information into the model. In this paper, we introduce a Multidimensional Trust model based on Tensor Factorization. The generalization of Matrix Factorization allows for a flexible and generic integration of contextual information. According to the different types of context, the Multidimensional Trust model considers the additional dimensions for the representation of the data as a tensor. This is achieved by going through the collecting user's behavior based on rating analysis and identification of users' historical activity and viewing patterns. The benefits behavior solutions, which use the handle intelligently to meet the users' needs, are the focus of this paper.

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“…As a result, systems that recommend content and products that are likely to be preferred by users are widely employed, and various personalized services utilizing such recommendation systems have been provided [18,19,20]. Therefore, studies on recommendation schemes considering users' preferences have been conducted actively to take various user requirements into account quickly [9,10,18,21,22].…”

Section: Introductionmentioning

confidence: 99%

Social group recommendation based on dynamic profiles and collaborative filtering

Bok

Lim

Yang³

et al. 2016

Neurocomputing

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Performance Improvement of a Movie Recommendation System based on Personal Propensity and Secure Collaborative Filtering

Cited by 30 publications

References 9 publications

Estimation of Real-Time Flood Risk on Roads Based on Rainfall Calculated by the Revised Method of Missing Rainfall

Estimation of Real-Time Flood Risk on Roads Based on Rainfall Calculated by the Revised Method of Missing Rainfall

Recommendation system for sharing economy based on multidimensional trust model

Social group recommendation based on dynamic profiles and collaborative filtering

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