Chen-Shu Wang

A recommender mechanism based on case-based reasoning

Case-based reasoning (CBR) algorithm is particularly suitable for solving ill-defined and unstructured decision-making problems in many different areas. The traditional CBR algorithm, however, is inappropriate to deal with complicated problems and therefore needs to be further revised. This study thus proposes a next-generation CBR (GCBR) model and algorithm. GCBR presents as a new problem-solving paradigm that is a case-based recommender mechanism for assisting decision making. GCBR can resolve decision-making problems by using hierarchical criteria architecture (HCA) problem representation which involves multiple decision objectives on each level of hierarchical, multiple-level decision criteria, thereby enables decision makers to identify problems more precisely. Additionally, the proposed GCBR can also provide decision makers with series of cases in support of these multiple decision-making stages. GCBR furthermore employs a genetic algorithm in its implementation in order to reduce the effort involved in case evaluation. This study found experimentally that using GCBR for making travel-planning recommendations involved approximately 80% effort than traditional CBR, and therefore concluded that GCBR should be the next generation of case-based reasoning algorithms and can be applied to actual case-based recommender mechanism implementation.

Integrated framework for reverse logistics

Although reverse logistics has been disregarded for many years, pressures from both environmental awareness and business sustainability have risen. Reverse logistical activities include return, repair and recycle products. Traditionally, since the information transparency of the entire supply chain is restricted, business is difficult to predict, and prepare for these reverse activities. This study presents an agent-based framework to increase the degree of information transparency. The cooperation between sensor and disposal agents helps predict reverse activities, avoid return, speed up repair and prepare for recycling behaviors.

Applying Fuzzy AHP to Understand the Factors of Cloud Storage Adoption

Cloud storage service (CSS) is one of the widely applications in cloud computing. However, most users tend to doubt the mechanism of the data security and privacy protection, which may reduce the expected benefits of CSS. Therefore, it‘s an important issue to understand the factors that affect the intention of user to adopt CSS. This study summarize various risk reliever strategies that could be taken by general users when they face a newly emerged technological service. Therefore, it total 4 evaluation dimensions and 10 factors were finally chosen. Then, the Fuzzy Analytic Hierarchy Process method was used to analyze. From the results, it is found the user’s acquaintance with the cloud service provider, the positive online word-of-mouth about the CSS, and the CSS provider’s statement and warranty of data security and privacy protection are three key factors for the users when they consider whether or not to use this service.

Impersonate human decision making process: an interactive context-aware recommender system

A considerable amount of information is quickly disseminated worldwide and users struggled to survive on such data tsunami. Context-recommender-aware systems (CAR) are then developed which enabling users to locate valuable and useful information from a large amount of disordered data. However, human decision-making contains multiple steps and a recursive loop, most users tend to adjust their decision many times instead of achieving the final decision-making immediately. Therefore, to replicate such a recursive process among multiple steps, the traditional CAR system should be altered as an interactive CAR (iCAR) system for improving the recommendation accuracy. In view of the deficiency in the present CAR, this study leads the concept of human-computer interaction in tradition CAR and establishes an interactive context-aware recommender System (iCAR). To validate the feasibility and applicability of the proposed iCAR system, a car rental website which is designed based on iCAR is shown as a demonstration. According to the car rental case shown, after couples of iterations, the decision criteria can be gradually clarified by the proposed algorithm of inferring engine. Also, iCAR can find users a car that most satisfies their requirements by using the contexts information. iCAR can improve the accuracy of traditional CAR system and provide user more precise recommendation results according to 3-dimensions information, including: user, item and context information. The iCAR system can be further expected to apply to various fields, such as online shopping or travel packages recommendations, to optimize recommendations results.

A Personalization Recommendation Framework of IT Certification e-Learning System

Obtaining IT certifications is a common and permanent goal of almost all IT students and IT workers. However, approximately 200---400 computer-related certifications exist and constantly increase or change. Examinee indeed requires the personalization recommendation about these certifications and so on. This study proposed a personalization recommendation framework contains two reasoning mechanisms: experience-based and capability-based reasoning. The experienced-based reasoning provides personalized suggestions based on the sophistic examinees experience, while the capability-based reasoning considers the learners background only. The proposed framework can provide the beneficial certification path to examine and a practice scenario is presented to illustrate the feasibility of proposed framework.

To Solve the TDVRPTW via Hadoop MapReduce Parallel Computing

The convenience of online shopping has made it common to everyone. With the increase of online transaction, optimization of VRP is an important issue in logistics and transportation. TDVRPTW is a crucial problem which considers a given time window in VRP. This paper targets solving TDVRPTW by using Hadoop MapReduce and compares the effectiveness of Hadoop with a single machine. We used an existing program to cluster the demand nodes and then calculated a route for every cluster by using random method and heuristic algorithm including nearest time window algorithm, nearest neighbor algorithm and 2-opt. After that, we executed parallel computing in Hadoop by implementing program on MapReduce. We used Solomon benchmarking problem as the base of experimental examples and made the experiments. This research proved that Hadoop MapReduce has better efficacy to calculate the best solution than a single machine.

MapReduce-Based Frequent Pattern Mining Framework with Multiple Item Support

The analysis of big data mining for frequent patterns is become even more problematic. Many efficient itemset mining algorithms to set a multiple support values for each transaction which could seem feasible as real life applications. To solve problem of single support have been discovered in the past. Since, we know that parallel and distributed computing are valid approaches to deal with large datasets. In order to reduce the search space, we using MISFP-growth algorithm without the process of rebuilding and post pruning steps. Accordingly, in this paper we proposed a model to use of MapReduce framework for implement the parallelization under multi-sup values, thereby improving the overall performance of mining frequent patterns and rare items accurately and efficiently.

Balanced k-Means

K-Means is a very common method of unsupervised learning in data mining. It is introduced by Steinhaus in 1956. As time flies, many other enhanced methods of k-Means have been introduced and applied. One of the significant characteristic of k-Means is randomize. Thus, this paper proposes a balanced k-Means method, which means number of items distributed within clusters are more balanced, provide more equal-sized clusters. Cases those are suitable to apply this method are also discussed, such as Travelling Salesman Problem (TSP). In order to enhance the performance and usability, we are in the process of proposing a learning ability of this method in the future.

Combining FAHP with MDS to analyze the key factors of different consumer groups for tablet PC purchasing

People living become highly informationized, resulting in Tablet PC has developed vigorously in recent years. To understand the consideration of the customers when purchasing Tablet PC is getting important. This study applies Fuzzy Analytic Hierarchy Process (FAHP) to find out the key factors affecting the consumer’s purchasing of Tablet PC. Further, combining Multidimensional Scaling (MDS), decision maker can realize that the similarity and difference among the consumer groups. Through literature review and expert interview, we select appropriate evaluation components to construct the hierarchical structure of evaluation and conduct the AHP questionnaire on 15 experts. The FAHP analysis results show that the importance of evaluation criteria in following order: operating system, color and hardware while customer intend to buy a Tablet PC. Furthermore, through the perceptual map of MDS, we could be find out the consumer groups of Businessman and Officer, as well as Student and Housewife, have similar demands when purchasing Tablet PC.