Xiaolan Cui, Shuqin Cai and Yuchu Qin
The purpose of this paper is to propose a similarity-based approach to accurately retrieve reference solutions for the intelligent handling of online complaints.
Abstract
Purpose
The purpose of this paper is to propose a similarity-based approach to accurately retrieve reference solutions for the intelligent handling of online complaints.
Design/methodology/approach
This approach uses a case-based reasoning framework and firstly formalizes existing online complaints and their solutions, new online complaints, and complaint products, problems and content as source cases, target cases and distinctive features of each case, respectively. Then the process of using existing word-level, sense-level and text-level measures to assess the similarities between complaint products, problems and contents is explained. Based on these similarities, a measure with high accuracy in assessing the overall similarity between cases is designed. The effectiveness of the approach is evaluated by numerical and empirical experiments.
Findings
The evaluation results show that a measure simultaneously considering the features of similarity at word, sense and text levels can obtain higher accuracy than those measures that consider only one level feature of similarity; and that the designed measure is more accurate than all of its linear combinations.
Practical implications
The approach offers a feasible way to reduce manual intervention in online complaint handling. Complaint products, problems and content should be synthetically considered when handling an online complaint. The designed procedure of the measure with high accuracy can be applied in other applications that consider multiple similarity features or linguistic levels.
Originality/value
A method for linearly combining the similarities at all linguistic levels to accurately assess the overall similarities between online complaint cases is presented. This method is experimentally verified to be helpful to improve the accuracy of online complaint case retrieval. This is the first study that considers the accuracy of the similarity measures for online complaint case retrieval.
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Maria Soledad Pera and Yiu‐Kai Ng
Tens of thousands of news articles are posted online each day, covering topics from politics to science to current events. To better cope with this overwhelming volume of…
Abstract
Purpose
Tens of thousands of news articles are posted online each day, covering topics from politics to science to current events. To better cope with this overwhelming volume of information, RSS (news) feeds are used to categorize newly posted articles. Nonetheless, most RSS users must filter through many articles within the same or different RSS feeds to locate articles pertaining to their particular interests. Due to the large number of news articles in individual RSS feeds, there is a need for further organizing articles to aid users in locating non‐redundant, informative, and related articles of interest quickly. This paper aims to address these issues.
Design/methodology/approach
The paper presents a novel approach which uses the word‐correlation factors in a fuzzy set information retrieval model to: filter out redundant news articles from RSS feeds; shed less‐informative articles from the non‐redundant ones; and cluster the remaining informative articles according to the fuzzy equivalence classes on the news articles.
Findings
The clustering approach requires little overhead or computational costs, and experimental results have shown that it outperforms other existing, well‐known clustering approaches.
Research limitations/implications
The clustering approach as proposed in this paper applies only to RSS news articles; however, it can be extended to other application domains.
Originality/value
The developed clustering tool is highly efficient and effective in filtering and classifying RSS news articles and does not employ any labor‐intensive user‐feedback strategy. Therefore, it can be implemented in real‐world RSS feeds to aid users in locating RSS news articles of interest.
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Andreas Gschwentner, Manfred Kaltenbacher, Barbara Kaltenbacher and Klaus Roppert
Performing accurate numerical simulations of electrical drives, the precise knowledge of the local magnetic material properties is of utmost importance. Due to the various…
Abstract
Purpose
Performing accurate numerical simulations of electrical drives, the precise knowledge of the local magnetic material properties is of utmost importance. Due to the various manufacturing steps, e.g. heat treatment or cutting techniques, the magnetic material properties can strongly vary locally, and the assumption of homogenized global material parameters is no longer feasible. This paper aims to present the general methodology and two different solution strategies for determining the local magnetic material properties using reference and simulation data.
Design/methodology/approach
The general methodology combines methods based on measurement, numerical simulation and solving an inverse problem. Therefore, a sensor-actuator system is used to characterize electrical steel sheets locally. Based on the measurement data and results from the finite element simulation, the inverse problem is solved with two different solution strategies. The first one is a quasi Newton method (QNM) using Broyden's update formula to approximate the Jacobian and the second is an adjoint method. For comparison of both methods regarding convergence and efficiency, an artificial example with a linear material model is considered.
Findings
The QNM and the adjoint method show similar convergence behavior for two different cutting-edge effects. Furthermore, considering a priori information improved the convergence rate. However, no impact on the stability and the remaining error is observed.
Originality/value
The presented methodology enables a fast and simple determination of the local magnetic material properties of electrical steel sheets without the need for a large number of samples or special preparation procedures.
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Jeanetta D. Sims, Ed Cunliff, Atoya Sims and Kristi Robertson
San-Yih Hwang, Chih-Ping Wei, Chien-Hsiang Lee and Yu-Siang Chen
The information needs of the users of literature database systems often come from the task at hand, which is short term and can be represented as a small number of articles…
Abstract
Purpose
The information needs of the users of literature database systems often come from the task at hand, which is short term and can be represented as a small number of articles. Previous works on recommending articles to satisfy users’ short-term interests have utilized article content, usage logs, and more recently, coauthorship networks. The usefulness of coauthorship has been demonstrated by some research works, which, however, tend to adopt a simple coauthorship network that records only the strength of coauthorships. The purpose of this paper is to enhance the effectiveness of coauthorship-based recommendation by incorporating scholars’ collaboration topics into the coauthorship network.
Design/methodology/approach
The authors propose a latent Dirichlet allocation (LDA)-coauthorship-network-based method that integrates topic information into the links of the coauthorship networks using LDA, and a task-focused technique is developed for recommending literature articles.
Findings
The experimental results using information systems journal articles show that the proposed method is more effective than the previous coauthorship network-based method over all scenarios examined. The authors further develop a hybrid method that combines the results of content-based and LDA-coauthorship-network-based recommendations. The resulting hybrid method achieves greater or comparable recommendation effectiveness under all scenarios when compared to the content-based method.
Originality/value
This paper makes two contributions. The authors first show that topic model is indeed useful and can be incorporated into the construction of coaurthoship-network to improve literature recommendation. The authors subsequently demonstrate that coauthorship-network-based and content-based recommendations are complementary in their hit article rank distributions, and then devise a hybrid recommendation method to further improve the effectiveness of literature recommendation.
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Suganeshwari G., Syed Ibrahim S.P. and Gang Li
The purpose of this paper is to address the scalability issue and produce high-quality recommendation that best matches the user’s current preference in the dynamically growing…
Abstract
Purpose
The purpose of this paper is to address the scalability issue and produce high-quality recommendation that best matches the user’s current preference in the dynamically growing datasets in the context of memory-based collaborative filtering methods using temporal information.
Design/methodology/approach
The proposed method is formalized as time-dependent collaborative filtering method. For each item, a set of influential neighbors is identified by using the truncated version of similarity computation based on the timestamp. Then, recent n transactions are used to generate the recommendation that reflect the recent preference of the active user. The proposed method, lazy collaborative filtering with dynamic neighborhoods (LCFDN), is further scaled up by implementing in spark using parallel processing paradigm MapReduce. The experiments conducted on MovieLens dataset reveal that LCFDN implemented on MapReduce is more efficient and achieves good performance than the existing methods.
Findings
The results of the experimental study clearly show that not all ratings provide valuable information. Recommendation system based on LCFDN increases the efficiency of predictions by selecting the most influential neighbors based on the temporal information. The pruning of the recent transactions of the user also addresses the user’s preference drifts and is more scalable when compared to state-of-art methods.
Research limitations/implications
In the proposed method, LCFDN, the neighborhood space is dynamically adjusted based on the temporal information. In addition, the LCFDN also determines the user’s current interest based on the recent preference or purchase details. This method is designed to continuously track the user’s preference with the growing dataset which makes it suitable to be implemented in the e-commerce industry. Compared with the state-of-art methods, this method provides high-quality recommendation with good efficiency.
Originality/value
The LCFDN is an extension of collaborative filtering with temporal information used as context. The dynamic nature of data and user’s preference drifts are addressed in the proposed method by dynamically adapting the neighbors. To improve the scalability, the proposed method is implemented in big data environment using MapReduce. The proposed recommendation system provides greater prediction accuracy than the traditional recommender systems.
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Christopher P. Neck and Jeffery D. Houghton
The purpose of this paper is to provide a thorough review of self‐leadership literature past and present, including a historical overview of how the concept was created and…
Abstract
Purpose
The purpose of this paper is to provide a thorough review of self‐leadership literature past and present, including a historical overview of how the concept was created and expanded as well as a detailed look at more recent self‐leadership research trends and directions. The paper also presents a theoretical and conceptual explanation and differentiation of the self‐leadership concept relative to other related motivational, personality, and self‐influence constructs.
Design/methodology/approach
Self‐leadership research and related literatures of motivation, personality and self‐influence are discussed and described in order to present the current state of the self‐leadership body of knowledge and to suggest future directions to explore and study.
Findings
It is suggested that self‐leadership is a normative model of self‐influence that operates within the framework of more descriptive and deductive theories such as self‐regulation and social cognitive theory.
Research limitations/implications
While self‐leadership research composes an impressive body of knowledge, it is a domain of study that has been under‐investigated in some aspects, both empirically and conceptually.
Practical implications
This paper suggests several future directions that researchers can undertake to advance self‐leadership knowledge.
Originality/value
This paper fills a void in the organizational literature by reviewing the body of self‐leadership knowledge, by stating how self‐leadership is a distinctive theory in its own, and by presenting directions for future self‐leadership research.
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San‐Yih Hwang and Shi‐Min Chuang
In a large‐scale digital library, it is essential to recommend a small number of useful and related articles to users. In this paper, a literature recommendation framework for…
Abstract
In a large‐scale digital library, it is essential to recommend a small number of useful and related articles to users. In this paper, a literature recommendation framework for digital libraries is proposed that dynamically provides recommendations to an active user when browsing a new article. This framework extends our previous work that considers only Web usage data by utilizing content information of articles when making recommendations. Methods that make use of pure content data, pure Web usage data, and both content and usage data are developed and compared using the data collected from our university's electronic thesis and dissertation (ETD) system. The experimental results demonstrate that content data and usage data are complements of each other and hybrid methods that take into account of both types of information tend to achieve more accurate recommendations.
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Sanjay Rawat, V.P. Gulati and Arun K. Pujari
This paper discusses a new similarity measure for the anomaly‐based intrusion detection scheme using sequences of system calls. With the increasing frequency of new attacks, it is…
Abstract
This paper discusses a new similarity measure for the anomaly‐based intrusion detection scheme using sequences of system calls. With the increasing frequency of new attacks, it is getting difficult to update the signatures database for misuse‐based intrusion detection system (IDS). While anomaly‐based IDS has a very important role to play, the high rate of false positives remains a cause for concern. Defines a similarity measure that considers the number of similar system calls, frequencies of system calls and ordering‐of‐system calls made by the processes to calculate the similarity between the processes. Proposes the use of Kendall Tau distance to calculate the similarity in terms of ordering of system calls in the process. The k nearest neighbor (kNN) classifier is used to categorize a process as either normal or abnormal. The experimental results, performed on 1998 DARPA data, are very promising and show that the proposed scheme results in a high detection rate and low rate of false positives.