Program Recommendation System Research Articles

An adaptive program recommendation system for multi-user sharing environment

An adaptive program recommendation system for multi-user sharing environment

Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder

The individual differences among children with autism spectrum disorder (ASD) may make it challenging to achieve comparable benefits from a specific exercise intervention program. A new method for predicting the possible outcomes and maximizing the benefits of exercise intervention for children with ASD needs further exploration. Using the mini-basketball training program (MBTP) studies to improve the symptom performance of children with ASD as an example, we used the supervised machine learning method to predict the possible intervention outcomes based on the individual differences of children with ASD, investigated and validated the efficacy of this method. In a long-term study, we included 41 ASD children who received the MBTP. Before the intervention, we collected their clinical information, behavioral factors, and brain structural indicators as candidate factors. To perform the regression and classification tasks, the random forest algorithm from the supervised machine learning method was selected, and the cross validation method was used to determine the reliability of the prediction results. The regression task was used to predict the social communication impairment outcome following the MBTP in children with ASD, and explainable variance was used to evaluate the predictive performance. The classification task was used to distinguish the core symptom outcome groups of ASD children, and predictive performance was assessed based on accuracy. We discovered that random forest models could predict the outcome of social communication impairment (average explained variance was 30.58%) and core symptom (average accuracy was 66.12%) following the MBTP, confirming that the supervised machine learning method can predict exercise intervention outcomes for children with ASD. Our findings provide a novel and reliable method for identifying ASD children most likely to benefit from a specific exercise intervention program in advance and a solid foundation for establishing a personalized exercise intervention program recommendation system for ASD children.

Convolutional Neural Network-Based Personalized Program Recommendation System for Smart Television Users

The smart home culture is rapidly increasing across the globe and driving smart home users toward utilizing smart appliances. Smart television (TV) is one such appliance that is embedded with smart technology. The users of smart TV have their interests in the programs. However, automatic recommendation of programs for user-to-user is still under-researched. Several papers discussed recommendation systems, but those are related to different applications. Even though there are some works on recommending programs to smart TV users (single-user and multi-user), they did not discuss the smart TV camera module to capture and validate the user image for recommending personalized programs. Hence, this paper proposes a convolutional neural network (CNN)-based personalized program recommendation system for smart TV users. To implement this proposed approach, the CNN algorithm is trained on the datasets ‘CelebFaces Attribute Dataset’ and ‘Labeled Faces in the Wild-People’ for feature extraction and to detect a human face. The trained CNN model is applied to the user image captured by using the smart TV camera module. Further, the captured image is matched with the user image in the ‘synthetic dataset’. Based on this matching, the hybrid filtering technique is proposed and applied; thereby the recommendation of the respective program is done. The proposed CNN algorithm has achieved approximately 95% training performance. Besides, the performance of hybrid filtering is approximately 85% from the single-user perspective and approximately 81% from the multi-user perspective. From this, it is observed that hybrid filtering outperformed conventional content-based filtering and collaborative filtering techniques.

Causal Inference-based Study Abroad Program Recommendation System

Causal Inference-based Study Abroad Program Recommendation System

A Recommendation Method of National Fitness Items Based on Neural Network Algorithm

In order to improve the effectiveness of national fitness programs, this article analyzes the method of national fitness items recommendation based on a neural network algorithm. By using the time and space characteristics of fitness users’ sign-in, a novel POI recommendation model is proposed, and a novel fusion method is proposed to combine similarity and spatial similarity to achieve the final similarity calculation based on fitness users’ temporal and spatial preferences. In addition, in order to model the spatial similarity of fitness users, the Voronoi diagram is constructed by using the geographic locations of all POIs. Finally, this paper constructs a recommendation system for national fitness items based on a neural network algorithm. The experimental research results show that the national fitness program recommendation system proposed in this article basically meets the expected demand.

Embedding Crowd-Vote as Knowledge Source to Support Decision Making on University Program Selection

Crowd-vote has implemented as a part of a university program recommendation system, complementing the aspect of knowledge management. The recommendation system is developed to support freshmen students’ decision making during program selection and uses for first time of enrolment. The challenges of decision-making among students is formed by many influential factors like family, agents, universities and others. Popular decision making models include rational, intuitive, among others. Rational models have series of sequential steps that involve a thinking process, while intuitive models are more on people experiences and recognition of the pattern based on what people believe and think how it will work. Other models attempt to combine both rational and intuitive aspects of decision. The challenge is foreseen in capturing experts’ opinion as part of students’ decision making, since experts are the good source of knowledge to strengthen the process. A prototype is developed for this purpose and constructive feedback from experts and students were collected to examine the significant use of crowd-vote using questionnaire survey. The objectives of this paper are to investigate the use of crowd-vote in leveraging decision making, and to evaluate the implementation of crowd-vote in supporting decision making during program selection. The results from expert opinions and students’ evaluation are discussed.

Case Based Reasoning: Recommendations of High School Student Program

Study Program Recommendation System is a computer-based information system that aims to provide an overview or recommendation of study program based on students’ interests or character from testing through Case Based Reasoning method. The main purpose of this article is to develop of expert system as study program recommendation for High School student through Case Based Reasoning. This system is made because many parents tend to force their health when choosing a college for their children. It can be impact on the children’s developing. Determining the recommendations of a student‘s study program with a certain character, often not enough for the expert‘s knowledge, but need to pay attention to cases or events similar to those of the student. A concept held in the field of artificial intelligence that uses case-based or CBR reasons for analysis and decision making. Decisions are determined by calculating the similarities between the new cases and the expert case that has occurred. This study is observation and literature study. The results of this study showed that an engineering inference expertise with the aim of obtaining a decision of study program recommendations can be done using the CBR method based on the characteristics of students.

Clustering University Programs in Accounting to Enhance Selection Productivity: Precursor to Recommendation System Development

ABSTRACT Selecting prospective programs in higher education can be a problematic and inefficient task for applicants. In particular, one of the most significant challenges entails locating a specific subset of programs likely to be a good fit. In this paper, clustering techniques are employed in evaluating a specifically created data set of AACSB-accredited doctoral programs in accounting so as to aggregate them by type. In so doing, one is then better positioned to identify which schools best align with his/her relevant characteristics and objectives, thus gaining insight concerning the most appropriate subset of schools to initially consider for application purposes. This approach provides meaningful differentiation between the various program types, offers a means for improving productivity relative to the university application process, and demonstrates promise as an initial foundation for eventual construction of a program recommendation system for use in ostensibly any program application initiative within the higher education domain.

A Smart Program Recommender System Based on the Hybrid Broadcast Broadband Television

A Smart Program Recommender System Based on the Hybrid Broadcast Broadband Television

An intelligent movie recommendation system through group-level sentiment analysis in microblogs

The emergence of the online media sharing sites (e.g. Youtube, Youku, and Hulu) have introduced new challenges in program recommendation in online networks. However, there is a bottleneck that the amount of available viewing logs and user friendship networks are too limited to design effective recommendation algorithms. Thus, carrying out an intelligent program recommendation system is important for these sites. In this work, we propose a novel model which turns to the social networks and mine user preferences information expressed in microblogs for evaluating the similarity between online movies and TV episodes. To the best of our knowledge, it is the first effort to bridge the gap between movie and TV watchers domain with social network activities. Moreover, it is the first approach that can solve the “cold-start” problem in movie and TV recommendation system. Series of data mining approaches and social computing models have been adopted in this work. Similar programs found from the social network are further used to suggest programs in other media devices. This work can be easily applied in online media streaming sites in order that intelligent recommendations of programs can be made to the customers through mining microblogs.

Recommendation System of Program Based on REST Style

With the popularity of digital TV, TV programs have been on the increase no matter in both the number and species, which brought many choice to the users. Although the digital TV has increased largely in the selectivity, it has become a fussy process that users search for programs which they are interested in. So there is need to have an efficient program recommendation system to solve the problem that is overload for users. It can not only help users to get the program which they require, but also bring convenience to people's life. The program recommendation system named MyView is planned and designed, aimed to providing an efficient information platform. The system also involves intelligent recommendation. The information guide will trigger the recommendation engine after users registering information, the engine will accord to the data in the guide information to make the personalized program recommendation. The system was deployed in the Tomcat and Apache integration servers on my localhost, so it also belongs to the Web application based on J2EE platform. AJAX is used that can achieve a good user experience to develop web presentation layer on MyView PC browser with flexible interface performance. The background of business services uses the hierarchical form. MyView System uses the CXF framework and Hibernate to equip controller and data persistence layer in the Spring container. The overall framework of the system uses the REST style, in order to extend the performance and function later. Background service layer with uniform interface, marked by the URI resource. At the same time, HTTP requestion is submitted by the AJAX to obtain services provided by resources. Finally, we can analyze and summary the features of MyView System.

시청시간패턴을 활용한 TV 프로그램 추천 시스템

Abstract As a number of TV programs broadcast today, researches about TV program recommender system have been studied and many researchers have been studying recommende r system to produce recommendation with high accuracy. Recommender system recommends TV program to user by using metadata like genre, plot or calculating users’ preferences about TV programs. In th is paper, we propose a new TV program Collaborative Filtering Recommender System that exploits viewin g time pattern like viewing ratio, relation with finish time and recently viewing history to calculate prefe rence for high-quality of recommendation. To verify usefulness of our research, we also compare our method wh ich utilizes viewing time patterns and baseline which simply recommends TV program of user's most freq uently watched channel. Through this ex-periments, we show that our method very effectively works and r ecommendation performance increases.Key Words : TV Program, Recommender System, Viewing time Patterns, Collaborative Filtering Received: Mar. 22, 2015Revised : Apr. 5, 2015Accepted: Sep. 15, 2015

A fair scheduler using cloud computing for digital TV program recommendation system

With hundreds of TV channels, a good TV program recommendation system can save time. Hadoop fair scheduler cloud computing is designed to make information processing and filtering effective and scalable. In cloud computing, computers are connected over a network and perform computation simultaneously; more computation power can be obtained by adding more computer nodes. In the present study, cloud computing is used to build a TV program recommendation system. A fair scheduler cloud structure is applied to improve the system performance. For program recommendation, the K-means recursive clustering algorithm is used for user clustering, the term frequency/inverse document frequency algorithm is applied for finding related popular programs, and k-nearest neighbor is used to recommend programs. Most TV program recommendation systems focus on providing a personal recommendation system. The proposed system also considers user groups and the program watching preferences of the majority. The proposed fair scheduler cloud-based architecture is scalable; a massive amount of information can be processed in real-time to obtain program recommendation results that can represent almost all users.

Digital TV Program Recommendation System Based on Latent Factor Model

In order to solve the problem of information overload, which is a consequence of the extreme abundance of digital television (TV) program resource, this paper proposed a digital TV program recommendation system based on latent factor model (LFM). This system constructed a digital TV program recommendation system structure including information inputting unit, system analysis unit and information recommendation sending unit. The proposed program type analysis method searches for the relation between audience interest and the programs which audiences watch. To classify the audience crowd, there are two types of analysis method which are program type threshold analysis and program type cluster analysis that based on artificial classification of experience value and automatic clustering with arbitrary number of clusters respectively. The feasibility of the algorithm has been proved by simulated analysis.

Study on Personalized Recommendation Technology of Digital TV Programs

This paper aims at one of key technologies in digital television development ---intelligent personalized recommendation technology of digital TV programs for study. This paper proposes to take advantage of ample TV-Anytime to describe metadata so as to perform specific plans of guide service for TV programs based on TV-Anytime metadata specification. It combines technology such as data mining and artificial intelligence etc with a view of building a personalized TV program recommendation system on the framework of the multi-agent. Besides, a hybrid algorithm with content filtering and collaborative filtering based on the systematical recommendation algorithm has been put forward. In order to overcome the deficiencies of traditional collaborative filtering algorithm which relies on users explicit evaluation, the paper represents an improved algorithm with the footing of content collaborative filtering.

Toward Blind Scheduling in Mobile Media Cloud: Fairness, Simplicity, and Asymptotic Optimality

Recent advances in mobile media cloud (MMC) make it possible for users to enjoy the multimedia applications at anytime and anywhere. Most existing scheduling algorithms for MMC assume that the system parameters, such as user demand rate and server service time, are known to the scheduler. However, this assumption is invalid in many practical scenarios. In this paper, we consider a blind scenario where the above system parameters are unavailable. We aim at developing a blind scheduling algorithm (BSA) that performs well across magnitudes of fairness, simplicity and asymptotic optimality for a relatively general MMC. Specifically, the blind scheduling is first formulated as a finite time horizon optimization problem and fairness is required to be maintained at each time point with a given probability from the scheduler. Next, BSA routes the new users to the media service provider (MSP) whose weighted idle time is the longest, then assigns the available MSPs according to the fairness on the idle time. Importantly, we demonstrate that BSA is asymptotically optimal in the Halfin-Whitt heavy traffic (HWHT) regime. The asymptotic optimality is in the sense that the scheduling asymptotically and stochastically minimizes the steady-state waiting time of all the users. Our analysis also shows that in the HWHT regime the heterogeneous MSP system outperforms its homogeneous MSP counterpart in terms of the user waiting time. Moreover, we apply BSA to the program recommendation system and investigate its property.

A cloud-based intelligent TV program recommendation system

In recent years, cloud computing technology has matured significantly, as has the development of digital TV services. This, therefore, has led to an increased demand for improved quality TV services. In this paper, cloud computing technology is used to build a program recommendation system for digital TV programs, and the Hadoop Fair Scheduler is utilized to improve processing performance. Historical data of watched TV programs are collected through an electronic program guide, and then processed using K-means clustering, term frequency/inverse document frequency and k-nearest neighbor algorithms, to obtain clusters of audience groups and to find popular TV programs for each cluster. The proposed system can process massive amounts of user data in real-time, and can easily be scaled up.

A TV Program Recommender Framework

In the area of intelligent systems, research about recommender systems is a critical topic and has been applied in many fields. In this paper, we focus on TV program recommender systems. We give an overview of literature research about TV program recommender systems and propose a smart and social TV program recommender framework for Smart TV, which integrates the Internet and Web 2.0 features into television sets and set-top boxes. In addition, we also address several issues, such as accuracy, diversity, novelty, explanation and group recommendations, which are important in building a TV program recommender system. The proposed framework could be used to help designers/developers to build TV program recommender systems/engines for smart TV.

CPRS: A cloud-based program recommendation system for digital TV platforms

Traditional electronic program guides (EPGs) cannot be used to find popular TV programs. A personalized digital video broadcasting-terrestrial (DVB-T) digital TV program recommendation system is ideal for providing TV program suggestions based on statistics results obtained from analyzing large-scale data. The frequency and duration of the programs that users have watched are collected and weighted by data mining techniques. A large dataset produces results that best represent a viewer’s preferences of TV programs in a specific area. To process such a massive amount of viewer preference data, the bottleneck of scalability and computing power must be removed. In this paper, an architecture for a TV program recommendation system based on cloud computing and a map-reduce framework, the map-reduce version of k -means and the k -nearest neighbor (kNN) algorithm, is introduced and applied. The proposed architecture provides a scalable and powerful backend to support the demand of large-scale data processing for a program recommendation system.

A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition

With the advent of new cable and satellite services, and the next generation of digital TV systems, people are faced with an unprecedented level of program choice. This often means that viewers receive much more information than they can actually manage, which may lead them to believe that they are missing programs that could likely interest them. In this context, TV program recommendation systems allow us to cope with this problem by automatically matching user’s likes to TV programs and recommending the ones with higher user preference. This paper describes the design, development, and startup of queveo.tv: a Web 2.0 TV program recommendation system. The proposed hybrid approach (which combines content-filtering techniques with those based on collaborative filtering) also provides all typical advantages of any social network, such as supporting communication among users as well as allowing users to add and tag contents, rate and comment the items, etc. To eliminate the most serious limitations of collaborative filtering, we have resorted to a well-known matrix factorization technique in the implementation of the item-based collaborative filtering algorithm, which has shown a good behavior in the TV domain. Every step in the development of this application was taken keeping always in mind the main goal: to simplify as much as possible the user task of selecting what program to watch on TV.