Item-based Approach Research Articles

Symmetrical and Asymmetrical Sampling Audit Evidence Using a Naive Bayes Classifier

Taiwan’s auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target risker samples. We first classify data using a Naive Bayes classifier into some classes. Next, a user-based, item-based, or hybrid approach is employed to draw audit evidence. The representativeness index is the primary metric for measuring its representativeness. The user-based approach samples data symmetrically around the median of a class as audit evidence. It may be equivalent to a combination of monetary and variable samplings. The item-based approach represents asymmetric sampling based on posterior probabilities for obtaining risky samples as audit evidence. It may be identical to a combination of non-statistical and monetary samplings. Auditors can hybridize those user-based and item-based approaches to balance representativeness and riskiness in selecting audit evidence. Three experiments show that sampling using machine learning integration has the benefits of drawing unbiased samples; handling complex patterns, correlations, and unstructured data; and improving efficiency in sampling big data. However, the limitations are the classification accuracy output by machine learning algorithms and the range of prior probabilities.

The effectiveness of item parceling to increase the model fit: A case study of PAPs

The impact of item parceling to improve model fit indexes in confirmatory factor analysis has been on debate amongst psychometricians. In this study, the effectiveness of item parceling was examined using Tes Potensi Akademik Pascasarjana (PAPS) or Postgraduate Academic Potential Test in Universitas Gadjah Mada. Item parceling approach, second-order approach, and item-based approach of confirmatory factor analysis (CFA) were used for examination. Data were collected from a sample of 1374 postgraduate candidates in 2017. The result found that model fit indices such as the chi-squared test, comparative fit index, Tucker-Lewis index, and standardized root mean square residual were improved in the item parceling approach when compared to item based approach. Interestingly, the root mean square error of approximation were deteriorating in the item parceling approach. The finding of this study suggested that model dimensionality and sample size should be carefully considered when using the item parceling approach.

Evidence of method effects in the authoritarianism-conservatism-traditionalism scales

Right-wing authoritarianism (RWA) is notoriously complex, multifaceted, and difficult to measure as a psychological construct. Recently, Duckitt et al. developed the ACT scales, offering theoretical refinement of the RWA construct. Although the validity of the ACT scales had been supported by a considerable body of research, shortcomings in previous analyses cannot rule out the existence of possible method effects. In the present research, we sought to test for the presence of such effects in a representative community sample of adults in Singapore (N = 738). We re-evaluated the factor structure of the ACT scales by assessing four separate models using an item-based approach in our confirmatory factor analyses. Results found significant method effects associated with both the pro-trait and con-trait items in the ACT scales. The implications of these results and possible strategies for controlling method effects in the ACT scales are discussed.

NFC: a deep and hybrid item-based model for item cold-start recommendation

New items, also called cold-start items, are introduced every day in the catalogs of numerous online systems. Due to the absence of previous preferences, recommending these items is difficult but important task for a recommender system. For this reason, the item cold-start recommendation problem still represents an interesting research topic for the community. In this work, we propose Neural Feature Combiner (NFC), a novel deep learning, item-based approach for cold-start item recommendation. The model learns to map the content features of the items into a low-dimensional hybrid embedding space. The features that compose the embeddings are then combined in order to reproduce collaborative item similarity values. We compare NFC with three variants of the same model that learn from user feedback, showing the advantages of learning from similarities in terms of accuracy and convergence time. With an extensive set of experiments on four datasets, we show that NFC outperforms several cutting edge approaches in the top-n recommendation task of cold-start items. Results in extremely cold (i.e., with a very low amount of interactions for training) and cold-warm hybrid scenarios prove that NFC effectively exploits collaborative information, leading to state-of-the-art accuracy. We finally conduct a qualitative analysis of the embeddings generated by different models, and we provide an analysis of the importance that different models assign to the input features, empirically demonstrating the robustness of the hybrid representations produced by our new model.

A Comparison of Two Five-Factor Model Operationalizations of the Triarchic Model of Psychopathy in a Clinical Sample.

Structural models of personality traits, particularly the five-factor model (FFM), continue to inform ongoing debates regarding what personality attributes and trait domains are central to psychopathy. A growing body of literature has linked the constructs of the triarchic model of psychopathy (boldness, meanness, disinhibition) to the FFM. Recently, researchers developed both item and regression-based measures of the triarchic model of psychopathy using the NEO Personality Inventory-Revised-a popular measure of the FFM. The current study examines the correlates of these two FFM-derived operationalizations of the triarchic model using data from the Collaborative Longitudinal Personality Disorders Study. The two approaches had strong convergent validity coefficients and similar patterns of criterion-related validity coefficients. Meanness related to greater personality pathology characterized by exploitation of others and poor attachment, whereas disinhibition related to indicators of greater negative affect and poor behavioral constraint. Boldness related to reduced negative affect and greater narcissistic personality traits. Although the item and regression-based approaches showed similar patterns of associations with criterion-variables, the item-based approach has some practical and psychometric advantages over the regression-based approach given strong correlations between the meanness and disinhibition scores from the regression approach.

Hybrid Recommender for Research Papers and Articles

In digital libraries and other e-commerce sites, recommender system is the solution that supports the users in information search and decision making. Some of these recommender systems will make predictions by matching the content of an item against the user profile otherwise known as Content-Based recommendation approach. Other recommenders will provide recommendation based on ratings of items from current user and other users and then use it to recommend similar items the current user has not seen, this is known as Collaborative-Based recommender approach. There exist several other approaches that are used in recommending articles and other items to users of different search engines. Over the years several researchers have tried combining these approaches in an attempt to design more efficient recommendations in search engines. This research proposed and designed a prototype of a Hybrid recommender called Zira, which is a model that combines both the Collaborative filtering, Content-based filtering, attribute-based approach to look at contextual information as well as an item-based approach that will solve the issues associated with cold-start problems all working concurrently to complement one another. The proposed system supports multi-criteria ratings, provide more flexible and less intrusive types of recommendations to ensure the improvement in recommendations of e-learning materials to users of digital libraries.

A Bayesian Inference Based Hybrid Recommender System

The large mass of various products/services accessible on the Internet has motivated the development of recommender systems to refine the selection of items aligned with users' expectations. Recommender systems have been developed to tackle the item targeting problem. They are crucial tools that quickly target items fitting users' needs, thus allowing them to easily identify the items that fit their tastes and preferences. Following state-of-the-art methods, a distinction is made between content-based recommender approaches and collaborative filtering-based recommender approaches. Collaborative filtering-based recommender approaches are the most widely adopted methods. They are divided into memory-based methods that show the advantage of their easy-understandability, and model-based methods that are data sparsity resilient and high-accurate. In this paper, we propose a hybrid model-based recommendation approach, a combination of a user-based approach and an item-based approach. Our method estimates the probability with which a user would rate an item. It performs a Bayesian inference of future end-user interests and shows the advantage of the easy-understandability of memory-based methods and the effectiveness of model-based methods. Experiments are conducted on real-world datasets and show that our method outperforms several state-of-the-art recommendation methods regarding the prediction accuracy and the recommendation quality.

Performance Comparison of Collaborative-Filtering Approach with Implicit and Explicit Data

Challenge in developing a collaborative filtering (CF)-based recommendation system is the problem of cold-starting of items that causes the data to sparse and reduces the accuracy of the recommendations. Therefore, to produce high accuracy a match is needed between the types of data and the approach used. Two approaches in CF include user-based and item-based CFs, both of which can process two types of data; implicit and explicit data. This work aims to find a combination of approaches and data types that produce high accuracy. Cosine-similarity is used to measure the similarity between users and also between items. Mean Absolute Error is also measured to discover the accuracy of a recommendation. Testing of three groups of data based on sparseness results in the best accuracy in an explicit data-based approach that has the smallest MAE value. The result is that the average MAE value for user based (implicit data) is 0.1032, user based (explicit data) is 0.2320, item based (implicit data) is 0.3495, and item based (explicit data) is 0.0926. The best accuracy is in the item-based (explicit-data) approach which is the smallest average MAE value.

Review of Intent Diversity in Information Retrieval : Approaches, Models and Trends

The fast increasing volume of information databases made some difficulties for a user to find the information that they need. Its important for researchers to find the best method for challenging this problem. user intention detection can be used to increase the relevancies of information delivered from the information retrieval system. This research used a systematic mapping process to identify what area, approaches, and models that mostly used to detect user intention in information retrieval in four years later. the result of this research identified that item-based approach is still the most approach researched by researchers to identify intent diversity in information retrieval. The used of item-based approach still increasing from 2015 until 2017. 34% paper used topic models in their research. It means that Topic models still the necessary models explored by the researchers in this study.

Exploring the motivational antecedents of Nepalese learners of L2 English

AbstractThis paper is the first to examine the motivational disposition of Nepalese learners of L2 English. Based on an adapted version of the questionnaire in (Kormos, Judit & Kata Csizér. 2008. Age-related differences in motivation of learning English as a foreign language: Attitudes, selves, and motivated behavior.Language Learning58. 327–355. Doi:10.1111/j.1467-9922.2008.00443.x.), we test the robustness and culture-specific applicability of well-known motivational antecedents to this learner population, and we investigate how the effects of these antecedents are mediated by the learners’ gender, age and regional aspects of the educational setting. In doing so, we offer novel ways of analyzing the data: Firstly, we employ random forests and conditional inference trees for assessing the relative importance of motivational antecedents. Secondly, we complement the traditional ‘scale-based approach’, which focuses on holistic constructs like the ‘Ideal L2 Self’, with an ‘item-based approach’ that highlights more specific components of such scales. The results are interpreted with reference to the L2 Motivational Self System (Dörnyei, Zoltán. 2005.The psychology of the language learner: Individual differences in second language acquisition. Mahwah, NJ: Lawrence Erlbaum) and to previous studies on other Asian populations of L2 learners.

Improving sparsity and new user problems in collaborative filtering by clustering the personality factors

In collaborative filtering recommender systems, items recommended to an active user are selected based on the interests of users similar to him/her. Collaborative filtering systems suffer from the ‘sparsity’ and ‘new user’ problems. The former refers to the insufficiency of data about users’ preferences and the latter addresses the lack of enough information about the new-coming user. Clustering users is an effective way to improve the performance of collaborative filtering systems in facing the aforementioned problems. In previous studies, users were clustered based on characteristics such as ratings given by them as well as their age, gender, occupation, and geographical location. On the other hand, studies show that there is a significant relationship between users’ personality traits and their interests. To alleviate the sparsity and new user problems, this paper presents a new collaborative filtering system in which users are clustered based on their ‘personality traits’. In the proposed method, the personality of each user is described according to the big-5 personality model and users with similar personality are placed in the same cluster using K-means algorithm. The unknown ratings of the sparse user-item matrix are then estimated based on the clustered users, and recommendations are found for a new user according to a user-based approach which relays on the interests of the users with similar personality to him/her. In addition, for an existing user in the system, recommendations are offered in an item-based approach in which the similarity of items is estimated based on the ratings of users similar to him/her in personality. The proposed method is compared to some former collaborative filtering systems. The results demonstrate that in facing the data sparsity and new user problems, this method reduces the mean absolute error and improves the precision of the recommendations.

A New Method for E-Government Procurement Using Collaborative Filtering and Bayesian Approach

Nowadays, as the Internet services increase faster than ever before, government systems are reinvented as E-government services. Therefore, government procurement sectors have to face challenges brought by the explosion of service information. This paper presents a novel method for E-government procurement (eGP) to search for the optimal procurement scheme (OPS). Item-based collaborative filtering and Bayesian approach are used to evaluate and select the candidate services to get the top-M recommendations such that the involved computation load can be alleviated. A trapezoidal fuzzy number similarity algorithm is applied to support the item-based collaborative filtering and Bayesian approach, since some of the services' attributes can be hardly expressed as certain and static values but only be easily represented as fuzzy values. A prototype system is built and validated with an illustrative example from eGP to confirm the feasibility of our approach.

NINU: An Incremental User-Based Algorithm for Data Sparsity Recommender Systems

Collaborative Filtering (CF) algorithms are widely used in recommender systems to deal with information overload. However, with the rapid growth in the amount of information and the number of visitors to web sites in recent years, CF researchers are facing challenges with improving the quality of recommendations for users with sparse data and improving the scalability of the CF algorithms. To address these issues, an incremental user-based algorithm combined with item-based approach is proposed in this paper. By using N-nearest users and N-nearest items in the prediction generation, the algorithm requires an O(N) space for storing necessary similarities for the online prediction computation and at the same time gets improvement of scalability. The experiments suggest that the incremental user-based algorithm provides better quality than the best available classic Pearson correlation-based CF algorithms when the data set is sparse.

ALLEVIATING THE SPARSITY PROBLEM OF COLLABORATIVE FILTERING USING AN EFFICIENT ITERATIVE CLUSTERED PREDICTION TECHNIQUE

Collaborative filtering (CF) is one of the most prevalent recommendation techniques, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. Although CF has been widely applied in various applications, its applicability is restricted due to the data sparsity, the data inadequateness of new users and new items (cold start problem), and the growth of both the number of users and items in the database (scalability problem). In this paper, we propose an efficient iterative clustered prediction technique to transform user-item sparse matrix to a dense one and overcome the scalability problem. In this technique, spectral clustering algorithm is utilized to optimize the neighborhood selection and group the data into users' and items' clusters. Then, both clustered user-based and clustered item-based approaches are aggregated to efficiently predict the unknown ratings. Our experiments on MovieLens and book-crossing data sets indicate substantial and consistent improvements in recommendations accuracy compared to the hybrid user-based and item-based approach without clustering, hybrid approach with k-means and singular value decomposition (SVD)-based CF. Furthermore, we demonstrated the effectiveness of the proposed iterative technique and proved its performance through a varying number of iterations.

Dimensionality and Summary Measures of the SF-36 v1.6: Comparison of Scale- and Item-Based Approach Across ECRHS II Adults Population

Dimensionality and Summary Measures of the SF-36 v1.6: Comparison of Scale- and Item-Based Approach Across ECRHS II Adults Population

Dimensionality and Summary Measures of the SF-36 v1.6: Comparison of Scale- and Item-Based Approach Across ECRHS II Adults Population

Objectives The objective of this study was twofold: 1) to confirm the hypothetical eight scales and two-component summaries of the questionnaire Short Form 36 Health Survey (SF-36), and 2) to evaluate the performance of two alternative measures to the original physical component summary (PCS) and mental component summary (MCS). Methods We performed principal component analysis (PCA) based on 35 items, after optimal scaling via multiple correspondence analysis (MCA), and subsequently on eight scales, after standard summative scoring. Item-based summary measures were planned. Data from the European Community Respiratory Health Survey II follow-up of 8854 subjects from 25 centers were analyzed to cross-validate the original and the novel PCS and MCS. Results Overall, the scale- and item-based comparison indicated that the SF-36 scales and summaries meet the supposed dimensionality. However, vitality, social functioning, and general health items did not fit data optimally. The novel measures, derived a posteriori by unit-rule from an oblique (correlated) MCA/PCA solution, are simple item sums or weighted scale sums where the weights are the raw scale ranges. These item-based scores yielded consistent scale-summary results for outliers profiles, with an expected known-group differences validity. Conclusions We were able to confirm the hypothesized dimensionality of eight scales and two summaries of the SF-36. The alternative scoring reaches at least the same required standards of the original scoring. In addition, it can reduce the item-scale inconsistencies without loss of predictive validity.

Item Parceling in Structural Equation Modeling: A Primer

This article provides a primer of the statistical technique called parceling, or aggregating items and using those aggregates as indicators of latent constructs, for structural equation modeling (SEM). First, two major types of parceling (subset-item-parcel and all-item-parcel approaches), alongside the traditional item-based approach, are illustrated. Second, both pros and cons of parceling are explicated. Parceling provides psychometric and modeling-related benefits. Risks associated with parceling are also noted. Particularly, potential induction of estimation bias and model misspecification are pointed out. In relation to the latter problem, unidimensionality of the scale is highlighted as an important prerequisite for the use of parceling. Finally, issues of the number of parcels per factor and parcel-building algorithms are discussed. Forming three parcels per factor by the random algorithm is recommended. Suggestions and general guidelines for the use of item parceling for SEM are also provided.

EcoLens: Integration and interactive visualization of ecological datasets

Complex multi-dimensional datasets are now pervasive in science and elsewhere in society. Better interactive tools are needed for visual data exploration so that patterns in such data may be easily discovered, data can be proofread, and subsets of data can be chosen for algorithmic analysis. In particular, synthetic research such as ecological interaction research demands effective ways to examine multiple datasets. This paper describes our integration of hundreds of food-web datasets into a common platform, and the visualization software, EcoLens, we developed for exploring this information. This publicly-available application and integrated dataset have been useful for our research predicting large complex food webs, and EcoLens is favorably reviewed by other researchers. Many habitats are not well represented in our large database. We confirm earlier results about the small size and lack of taxonomic resolution in early food webs but find that they and a non-food-web source provide trophic information about a large number of taxa absent from more modern studies. Corroboration of Tuesday Lake trophic links across studies is usually possible, but lack of links among congeners may have several explanations. While EcoLens does not provide all kinds of analytical support, its label- and item-based approach is effective at addressing concerns about the comparability and taxonomic resolution of food-web data.

Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering

Recommender systems are being widely applied in many application settings to suggest products, services, and information items to potential consumers. Collaborative filtering, the most successful recommendation approach, makes recommendations based on past transactions and feedback from consumers sharing similar interests. A major problem limiting the usefulness of collaborative filtering is the sparsity problem, which refers to a situation in which transactional or feedback data is sparse and insufficient to identify similarities in consumer interests. In this article, we propose to deal with this sparsity problem by applying an associative retrieval framework and related spreading activation algorithms to explore transitive associations among consumers through their past transactions and feedback. Such transitive associations are a valuable source of information to help infer consumer interests and can be explored to deal with the sparsity problem. To evaluate the effectiveness of our approach, we have conducted an experimental study using a data set from an online bookstore. We experimented with three spreading activation algorithms including a constrained Leaky Capacitor algorithm, a branch-and-bound serial symbolic search algorithm, and a Hopfield net parallel relaxation search algorithm. These algorithms were compared with several collaborative filtering approaches that do not consider the transitive associations: a simple graph search approach, two variations of the user-based approach, and an item-based approach. Our experimental results indicate that spreading activation-based approaches significantly outperformed the other collaborative filtering methods as measured by recommendation precision, recall, the F-measure, and the rank score. We also observed the over-activation effect of the spreading activation approach, that is, incorporating transitive associations with past transactional data that is not sparse may "dilute" the data used to infer user preferences and lead to degradation in recommendation performance.