Automatic Recommendation Research Articles

Sistem Auto Recommendation Objek Wisata Menggunakan Metode SAW

The tourism sector is a complex multidimensional sector, which has an influence over the interrelation of other sectors in tourism activities and now the tourism sector is concerned by the government. Every tourist who will visit the tourist sites must have some consideration. The fact that today is happening is a lot of tourists who are disappointed because the destination is not in accordance with what they want. Seeing that the media campaign that is currently done is still not efficient, so Auto Recommendation System is needed which is capable to do visualized in digital mapping, and the system built can show the automatic recommendation of tourist destinations in accordance with the wishes of tourists. Auto Recommendation System is a system that implements Simple Additive Weighting (SAW) method. The results of this research factors other than the value of weight given by tourists, is the value factor owned by each alternative for each criteria, because the higher the value owned by the higher the higher the value of the final ranking obtained by the alternative. The end result of the SAW method is in the form of alternative ranking of existing tourism objects. Based on the results of these rankings the system also produces output in the form of location maps along with detailed information of the tourism object. Religious tourism Sunan Kudus and Sunan Muria became the recommended tourist attraction with first and second priority.

Automatic recommendation to omitted steps in use case specification

Completeness is one of the key attributes for a high-quality software requirements specification. Although incomplete requirements frequently occur in the requirements specification, it is rarely discovered. This turns out to be one of the major causes of software project failure. In order to handle this issue, this paper proposes an automatic approach to recommending omitted steps in a use case-based requirements specification. First, we automatically extract diverse scenario patterns by using the verb clustering algorithm and scenario flow graphs. Based on the scenario patterns, our approach detects omitted steps of user’s scenarios by the pattern matching algorithm and automatically recommends appropriate steps for the omitted parts. For validation of our approach, we have developed tool support, named ScenarioAmigo, and collected 231 use case specifications composing of 1874 scenario steps from 12 academic or proprietary projects. We first carried out the preliminary study to decide appropriate thresholds and weights. Then, we conducted three experiments as a quantitative performance evaluation. First, the cross-validation for the collected scenarios shows the 76% precision and 80% recall. Second, the comparison of recall of ScenarioAmigo to that of human experts obtained the 20% higher score. As the last experiment, we compared the result of ScenarioAmigo and human experts in terms of severity of each scenario and found that our approach could recommend normal as well as important scenarios, compared to the human experts.

Location- and time-dependent meeting point recommendations for shared interurban rides

Drivers offering spare seats in their vehicles on long-distance (interurban) trips often have to pick up or drop off passengers in cities en route. In that case, it is necessary to agree on a meeting point. Often, this is done by proposing well-known locations like train stations, which frequently induces unnecessary detours through the inner-city districts. In contrast, meeting points in the vicinity of motorways and arterial roads with good public transport connection can reduce driving time and mileage. This work proposes a location-based approach to enable a fast and automatic recommendation of suitable pick up (and drop off) points for drivers and passengers using a GIS workflow and comprehensive precomputation of travel times.

Recommendation system with Automated Web Usage data mining using K-Nearest Neighbor(KNN) classification

The major problem of many on-line web sites is the presentation of many choices to the various clients at a time. This usually results into time consumingtask in finding outthe right product or information on the site. The user’s current interest depends upon the navigational behavior which helps the organizations to guideusers in their browsing activities and obtain some relevant information in a short span of time. Since, the resulting patterns which are obtained through data mining techniques did not perform well in the prediction of future browsing patterns because of the low matching rate of resulting rules and of user’s browsing behavior. This paper focuses on the study of the automatic web usage data mining and recommendation system which is based on current user behavior through his/her click stream data. The K-Nearest-Neighbor (KNN) classification method has been trained to be used in real-time and on-line to identify clients and visitors click stream data, matching it to a particular user group and recommends a tailored browsing option that meet the needs of the specific user at particular time.

IPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning

To achieve automatic recommendation of privacy settings for image sharing, a new tool called iPrivacy (image privacy) is developed for releasing the burden from users on setting the privacy preferences when they share their images for special moments. Specifically, this paper consists of the following contributions: 1) massive social images and their privacy settings are leveraged to learn the object-privacy relatedness effectively and identify a set of privacy-sensitive object classes automatically; 2) a deep multi-task learning algorithm is developed to jointly learn more representative deep convolutional neural networks and more discriminative tree classifier, so that we can achieve fast and accurate detection of large numbers of privacy-sensitive object classes; 3) automatic recommendation of privacy settings for image sharing can be achieved by detecting the underlying privacy-sensitive objects from the images being shared, recognizing their classes, and identifying their privacy settings according to the object-privacy relatedness; and 4) one simple solution for image privacy protection is provided by blurring the privacy-sensitive objects automatically. We have conducted extensive experimental studies on real-world images and the results have demonstrated both the efficiency and effectiveness of our proposed approach.

A similarity-based automatic data recommendation approach for geographic models

ABSTRACTThe complexity of geographic modelling is increasing; hence, preparing data to drive geographic models is becoming a time-consuming and difficult task that may significantly hinder the application of such models. Meanwhile, a huge number of data sets have been shared and have become publicly accessible through the Internet. This study presents a data similarity-based approach to automatically recommend available data sets to fulfil the data requirements of geographic models. Unified description factors are adopted to provide a consistent description of public data sets and input data requirements of geographic models. Five elementary data similarities between them, specifically content, spatial coverage, temporal coverage, spatial precision, and temporal granularity similarities, are calculated. An overall similarity is estimated from aggregating the elementary data similarities. Thereafter, the candidate data for running the models are recommended in the order of overall data similarity. As a case study, the approach has been applied to recommend data from the China National Data Sharing Platform of Earth System Science to drive the population spatialization model (PSM). The approach has successfully recommended the most related data sets to run PSM. The result also suggests that the data recommendation approach can facilitate the intelligent identification of geographic data and the building of links between the open data sets.

Predicting user behavior in electronic markets based on personality-mining in large online social networks

Determining a user’s preferences is an important condition for effectively operating automatic recommendation systems. Since personality theory claims that a user’s personality substantially influences preference, I propose a personality-based product recommender (PBPR) framework to analyze social media data in order to predict a user’s personality and to subsequently derive its personality-based product preferences. The PBRS framework will be evaluated as an IT-artefact with a unique online social network XING dataset and a unique coffeemaker preference dataset. My evaluation results show (a) the possibility of predicting a user’s personality from social media data, as I reached a predictive gain between 23.2 and 41.8 percent and (b) the possibility of recommending products based on a user’s personality, as I reached a predictive gain of 45.1 percent.

A Health Surveillance Software Framework to deliver information on preventive healthcare strategies

A software framework can reduce costs related to the development of an application because it allows developers to reuse both design and code. Recently, companies and research groups have announced that they have been employing health software frameworks. This paper presents the design, proof-of-concept implementations and experimentation of the Health Surveillance Software Framework (HSSF). The HSSF is a framework that tackles the demand for the recommendation of surveillance information aiming at supporting preventive healthcare strategies. Examples of such strategies are the automatic recommendation of surveillance levels to patients in need of healthcare and the automatic recommendation of scientific literature that elucidates epigenetic problems related to patients. HSSF was created from two systems we developed in our previous work on health surveillance systems: the Automatic-SL and CISS systems. The Automatic-SL system aims to assist healthcare professionals in making decisions and in identifying children with developmental problems. The CISS service associates genetic and epigenetic risk factors related to chronic diseases with patient’s clinical records. Towards evaluating the HSSF framework, two new systems, CISS+ and CISS-SW, were created by means of abstractions and instantiations of the framework (design and code). We show that HSSF supported the development of the two new systems given that they both recommend scientific papers using medical records as queries even though they exploit different computational technologies. In an experiment using simulated patients’ medical records, we show that CISS, CISS+, and CISS-SW systems recommended more closely related and somewhat related documents than Google, Google Scholar and PubMed. Considering recall and precision measures, CISS+ surpasses CISS-SW in terms of precision.

Using a web-based tool to facilitate tailored exercise for people with osteoarthritis

Using a web-based tool to facilitate tailored exercise for people with osteoarthritis

Study of Recommendation System for Yoga and Raga for Personalized Health based on Constitution (Prakriti)

nowadays globalization has increased beyond its growth, many challenges and stressful situations need to be faced by human. In Current scenario Ayurveda experts diagnose the diseases by interacting with patients and by examining the patient. They suggest medicine, yoga, raga, and diet according to their observations. Such process is very time consuming. Designing automatic recommendation systems which recommend the yoga and raga for specific prakriti.it will help experts to reduce certain amount of time, such system recommend accurate yoga and raga according to physical and mental characteristics of person (Prakriti).Neural Network Technology and decision tree will be used to design recommendation system for generating rules based on Constitution, Timing, Atmosphere, Mood, and Disorder. KeywordsNetwork, Decision Tree, Prakriti, Raga, Yoga.

ソーシャル絵本推薦システムにおける自動推薦機能導入の試み

子どもの質問には，その子どもの興味が表れることに着目して，登録された質問に人手で絵本を推薦するソーシャル絵本推薦システム「ぴくぶく」を開発している．本稿では，「ぴくぶく」に自動推薦機能を追加することで，絵本の推薦率を向上させ，活発な絵本推薦を促すことを試みた．14組の親子を対象とした利用実験を行った結果を報告する．

A Method of Power Supply Mode Selection for Urban Distribution Network Planning Based on Association Rules

AbstractBased on association rules, this article proposed a method for intelligent recommendation of power supply mode, which helps decision-makers in the selection of many schemes. Firstly, a history database which includes the forecasting models and correlative factors was first built and association rule mining was conducted; then combined with the correlative factors in the designated area, the criteria matching in the rules mined were carried out with CBR technique; finally automatic recommendation of the power supply modes was achieved under the given conditions. By application of an example, it is demonstrated that the proposed method can not only automatically analyze the applicability of power supply modes and the intrinsic relationship between correlative factors but also provide, to some extent, theoretical basis for selection of power supply modes and practical utility for urban distribution network planning.

A Semantic Recommender System for Adaptive Learning

The ever-more complex labor world and the current economic crisis ask learners and workers to continuously update their qualification levels to stay relevant on the job. Hence, education and training providers need to adjust their offerings to cope with such evolving requirements. However, the huge number of variables to consider means that finding the right learning content that lets an individual fill his or her competency gap might be difficult. The authors' semantic-based recommender system crosses heterogeneous information about learners' and workers' backgrounds as well as advertised job positions with a catalog of online courses to identify the most appropriate learning resources. Experimental observations showed a good agreement between human and automatic recommendations, confirming the applicability of the emerging semantic technology to the generation of user-centered services that can adapt to individuals' learning needs. The authors discuss the details of their system in a video Web extra at http://youtu.be/A8c5MwnlGvA. A second Web extra shows the training booklet for evaluating the authors' system and the results of the automatic recommendation; it's available at https://s3.amazonaws.com/ieeecs.cdn.csdl.public/mags/it/2015/05/mit2015050050s.pdf.

A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences.

Order-preserving submatrices (OPSMs) have been applied in many fields, such as DNA microarray data analysis, automatic recommendation systems, and target marketing systems, as an important unsupervised learning model. Unfortunately, most existing methods are heuristic algorithms which are unable to reveal OPSMs entirely in NP-complete problem. In particular, deep OPSMs, corresponding to long patterns with few supporting sequences, incur explosive computational costs and are completely pruned by most popular methods. In this paper, we propose an exact method to discover all OPSMs based on frequent sequential pattern mining. First, an existing algorithm was adjusted to disclose all common subsequence (ACS) between every two row sequences, and therefore all deep OPSMs will not be missed. Then, an improved data structure for prefix tree was used to store and traverse ACS, and Apriori principle was employed to efficiently mine the frequent sequential pattern. Finally, experiments were implemented on gene and synthetic datasets. Results demonstrated the effectiveness and efficiency of this method.

Modelling multilevel data in multimedia: A hierarchical factor analysis approach

Multimedia content understanding research requires rigorous approach to deal with the complexity of the data. At the crux of this problem is the method to deal with multilevel data whose structure exists at multiple scales and across data sources. A common example is modeling tags jointly with images to improve retrieval, classification and tag recommendation. Associated contextual observation, such as metadata, is rich that can be exploited for content analysis. A major challenge is the need for a principal approach to systematically incorporate associated media with the primary data source of interest. Taking a factor modeling approach, we propose a framework that can discover low-dimensional structures for a primary data source together with other associated information. We cast this task as a subspace learning problem under the framework of Bayesian nonparametrics and thus the subspace dimensionality and the number of clusters are automatically learnt from data instead of setting these parameters a priori. Using Beta processes as the building block, we construct random measures in a hierarchical structure to generate multiple data sources and capture their shared statistical at the same time. The model parameters are inferred efficiently using a novel combination of Gibbs and slice sampling. We demonstrate the applicability of the proposed model in three applications: image retrieval, automatic tag recommendation and image classification. Experiments using two real-world datasets show that our approach outperforms various state-of-the-art related methods.

Towards the Automatic Recommendation of Musical Parameters based on Algorithm for Extraction of Linguistic Rules

In the present article the authors describe an analysis of data associated to the emotional responses to fractal generated music. This analysis is done via discovery of rules, and it constitutes the basis to elevate computer-assisted creativity: Our ultimate goal is to cre­ ate musical pieces by retrieving the right set of parame­ ters associated to a target emotion. This paper contains the description of (i) variables associated to fractal music and emotions; (ii) the data gathering method to obtain the tuples relating input parameters and emotional re­ sponses; (iii) the rules that where discovered by using an algorithm LR-FIR. Even though similar experiments whose intention is to elucidate emotional responses from music have been reported, this study stands because a connection is appointed between fractal-generated mu­ sic and emotional responses, all with the purpose of advancing in computer-assisted creativity.

Automatic stress-relieving music recommendation system based on photoplethysmography-derived heart rate variability analysis.

This paper presents an automatic stress-relieving music recommendation system (ASMRS) for individual music listeners. The ASMRS uses a portable, wireless photoplethysmography module with a finger-type sensor, and a program that translates heartbeat signals from the sensor to the stress index. The sympathovagal balance index (SVI) was calculated from heart rate variability to assess the user's stress levels while listening to music. Twenty-two healthy volunteers participated in the experiment. The results have shown that the participants' SVI values are highly correlated with their prespecified music preferences. The sensitivity and specificity of the favorable music classification also improved as the number of music repetitions increased to 20 times. Based on the SVI values, the system automatically recommends favorable music lists to relieve stress for individuals.

Shared service recommendations from requirement specifications: A hybrid syntactic and semantic toolkit

ContextSoftware Requirement Specifications (SRSs) are central to software lifecycles. An SRS defines the functionalities and constraints of a desired software system, hence it often serves as reference for further development. Software lifecycles concerned with the conversion of traditional systems into more service-oriented infrastructures can benefit from understanding potential shared capabilities through the analysis of SRSs. ObjectiveIn this paper, we propose an automated approach capable of recommending shared software services from multiple text-based SRSs created by different organizations. Our goal is to facilitate the identification of overlapping requirements in these specifications and subsequently recommend shared components, which promotes software reuse. The shared components can be implemented as services that are invoked across different systems. MethodOur approach leverages the syntactic similarity of the SRS text augmented with semantic information derived from the WordNet database. This work extends our earlier studies by introducing an algorithm that utilizes noun, verb, and predicate relations to enhance the discovery of equivalent requirements and the recommendation of reusable services. A prototype system is implemented to evaluate the approach and experimental results have shown effective recommendation of requirements and their realized shared services. ResultsOur automatic recommendation approach generates recommendations in few minutes compared to 9h when services are manually inspected by developers. Our approach is also able to recommend services that are overlooked by the same developers, and to identify similarity between requirements even if these requirements are reworded. ConclusionWe show through experimentation that we can efficiently recommend services by leveraging both the syntactical structure and the semantic information of a requirements document and that our approach is more effective than the manual selection of services by experts. We also show that our approach is effective in detecting similar requirements for a single system and hence discovering opportunities for software reuse.

GlUCModel: A monitoring and modeling system for chronic diseases applied to diabetes

Chronic patients must carry out a rigorous control of diverse factors in their lives. Diet, sport activity, medical analysis or blood glucose levels are some of them. This is a hard task, because some of these controls are performed very often, for instance some diabetics measure their glucose levels several times every day, or patients with chronic renal disease, a progressive loss in renal function, should strictly control their blood pressure and diet. In order to facilitate this task to both the patient and the physician, we have developed a web application for chronic diseases control which we have particularized to diabetes. This system, called glUCModel, improves the communication and interaction between patients and doctors, and eventually the quality of life of the former. Through a web application, patients can upload their personal and medical data, which are stored in a centralized database. In this way, doctors can consult this information and have a better control over patient records. glUCModel also presents three novelties in the disease management: a recommender system, an e-learning course and a module for automatic generation of glucose levels model. The recommender system uses Case Based Reasoning. It provides automatic recommendations to the patient, based on the recorded data and physician preferences, to improve their habits and knowledge about the disease. The e-learning course provides patients a space to consult information about the illness, and also to assess their own knowledge about the disease. Blood glucose levels are modeled by means of evolutionary computation, allowing to predict glucose levels using particular features of each patient. glUCModel was developed as a system where a web layer allows the access of the users from any device connected to the Internet, like desktop computers, tablets or mobile phones.

Predicting Quality of Answer in Collaborative Question Answer Learning

Studies over the years shown that students had actively and more interactively involved in a classroom discussion to gain their knowledge. By posting questions for other participants to answer, students could obtain several answers to their question. The problem is sometimes the answer chosen by student as the best answer is not necessarily the best quality answer. Therefore, an automatic recommender system based on student activity, may improve these situations as it will choose the best answer objectively. On the other side, in the implementation of collaborative learning, in addition to sharing information, sometimes students also need a reference or domain knowledge which relevant with the topic. In this paper, we proposed answer quality predictor in collaborative question answer (CQA) learning, to predict the quality of answer either from recommender system based on users activity or domain knowledge as reference information.