Ordering policy estimation for high utility item-sets considering negative item values in large databases

Utility mining with negative item values has recently received interest in the data mining field due to its practical considerations. Previously, the values of utility item-sets have been taken into consideration as positive. However, in real-world applications an item-set may be related to negative item values. This paper presents a method for redesigning the ordering policy by including high utility item-sets with negative items. Initially, utility mining algorithm is used to find high utility item-sets. Then, ordering policy is estimated for high utility items considering defective and non-defective items. A numerical example is illustrated to validate the results

Enhanced Collaborative Filtering for Personalized E-Government Recommendation

The problems with the information overload of e-government websites have been a big obstacle for users to make decisions. One promising approach to solve this problem is to deploy an intelligent recommendation system on e-government platforms. Collaborative filtering (CF) has shown its superiority by characterizing both items and users by the latent features inferred from the user–item interaction matrix. A fundamental challenge is to enhance the expression of the user or/and item embedding latent features from the implicit feedback. This problem negatively affected the performance of the recommendation system in e-government. In this paper, we firstly propose to learn positive items’ latent features by leveraging both the negative item information and the original embedding features. We present the negative items mixed collaborative filtering (NMCF) method to enhance the CF-based recommender system. Such mixing information is beneficial for extending the expressiveness of the latent features. Comprehensive experimentation on a real-world e-government dataset showed that our approach improved the performance significantly compared with the state-of-the-art baseline algorithms.

Generalized inductive item tree analysis

Inductive item tree analysis is an established method of Boolean analysis of questionnaires. By exploratory data analysis, from a binary data matrix, the method extracts logical implications between dichotomous test items based on their positive item scores. For example, assume that we have the problems i and j of a test that can be solved or failed by subjects. With inductive item tree analysis, an implication between the items i and j can be uncovered, which has the interpretation "If a subject is able to solve item i, then this subject is also able to solve item j". Hence, in the current form of the method, (a) solely dichotomous items are considered, and (b) conclusions are drawn from only positive item scores. In this paper, we provide extensions to these restrictions. First, as remedy for (b), we focus on the dichotomous formulation of the inductive item tree analysis algorithm and describe a procedure of how to extend the dichotomous variant to also include negative item scores. Second, to address (a), we further extend our approach to the general case of polytomous items, when more than two answer categories are possible. Thus, we introduce extensions of inductive item tree analysis that can deal with nominal polytomous and ordinal polytomous answer scales. To show their usefulness, the dichotomous and polytomous extensions proposed in this paper are illustrated with empirical data and in a simulation study.

Symmetric Metric Learning with Adaptive Margin for Recommendation

Metric learning based methods have attracted extensive interests in recommender systems. Current methods take the user-centric way in metric space to ensure the distance between user and negative item to be larger than that between the current user and positive item by a fixed margin. While they ignore the relations among positive item and negative item. As a result, these two items might be positioned closely, leading to incorrect results. Meanwhile, different users usually have different preferences, the fixed margin used in those methods can not be adaptive to various user biases, and thus decreases the performance as well. To address these two problems, a novel Symmetic Metric Learning with adaptive margin (SML) is proposed. In addition to the current user-centric metric, it symmetically introduces a positive item-centric metric which maintains closer distance from positive items to user, and push the negative items away from the positive items at the same time. Moreover, the dynamically adaptive margins are well trained to mitigate the impact of bias. Experimental results on three public recommendation datasets demonstrate that SML produces a competitive performance compared with several state-of-the-art methods.

Evaluating a stroke-specific version of the Illness Perception Questionnaire–Revised, using the Think-Aloud method

The main purpose was to evaluate, using the Think-Aloud method, a version of the Illness Perception Questionnaire–Revised for stroke survivors. Six stroke survivors (mean age = 58.8 years, range = 31–78 years, standard deviation = 18.9 years) took part in Think-Aloud interviews, analysed according to established guidelines. Overall, 179 problems emerged. The most noteworthy was missing or insufficient Think-Aloud data generated, where participants did not think out loud. Others included complex and negative item wording, and items on the treatment control sub-scale. Questionnaire length, simpler wording and verbal probing are important considerations in further development of an Illness Perception Questionnaire–Revised for stroke survivors.

STUDENTS’ RESPONSE TOWARD THE DEVELOPMENT OF STUDENT WORKSHEET WITH PROBLEM BASED LEARNING IN COMPARISON MATERIAL AT CLASS VII-1 OF SMP NEGERI 1 KUANTAN MUDIK

Abstract— This study aimed to determine students' responses toward the development of student worksheet with problem based learning model in the comparison material. This study used 4-D model of development that consisted of four stages, namely the definition, design, development, and disseminate. Data collection instruments were sheets of validation and questionnaire of students’ responses. The questionnaire of students’ responses used Guttman scale consisting of 20 items of positive and negative statements. Grain statement was based on the aspects of accuracy, conformity with learning models, as well as the fulfillment of the terms of didactic, construction, and technical. Before tested to students, the student worksheet has been validated by three experts on mathematics education with very valid results (3.73). The student worksheet trial conducted to 32 students of class VII-1 SMPN 1 Kuantan Mudik, Kuantan Singingi, Riau Province during the second semester of the academic year 2016/2017. After carrying out learning by using worksheets, students were given the response questionnaire. Overall, students gave a positive response to the student worksheet which amounted to 94.37% categorized as very practical. Although the student worksheet already using Indonesian referring to Spelling Enhanced, there are some students who think that they find the unknown word or term on student worksheet. However, this is not a significant obstacle in learning because the student worksheet was developed for group learning so students can discuss things they did not understand with their group members. Keywords: problem based learning, students’ response, students’ worksheet Abstrak. Penelitian ini bertujuan untuk mengetahui respon siswa terhadap Lembar Kerja Siswa berbasis Problem Based Learning yang dikembangkan. Pengembangan menggunakan model 4-D yang terdiri dari 4 tahap yaitu Definition, Design, Development, dan Disseminate. Pengumpulan data menggunakan lembar validasi dan angket respon siswa. Angket respon siswa menggunakan skala Guttman yang terdiri atas 20 item berisi pernyataan positif dan negative. Item pernyataan didasarkan pada aspek akurasi, kesesuaian dengan model pembelajaran, serta pemenuhan syarat didaktik, konstruksi, dan teknis. Sebelum diuji kepada siswa, lembar kerja siswa divalidasi oleh tiga ahli pendidikan matematika dengan hasil yang sangat valid (3,73). Uji coba lembar kerja siswa dilakukan kepada 32 siswa kelas VII-1 SMPN 1 Kuantan Mudik, Kuantan Singingi, Provinsi Riau pada semester II tahun akademik 2016/2017. Setelah melakukan pembelajaran dengan menggunakan lembar kerja, siswa diberi angket respon. Secara keseluruhan, siswa memberikan respon positif terhadap lembar kerja siswa yang berjumlah 94,37% tergolong sangat praktis. Meskipun lembar kerja siswa sudah menggunakan bahasa Indonesia yang mengacu pada Ejaan yang Disempurnakan, ada beberapa siswa yang menemukan kata atau istilah yang tidak diketahui pada lembar kerja siswa. Namun, ini bukan masalah karena lembar kerja siswa dikembangkan untuk pembelajaran kelompok sehingga siswa dapat mendiskusikan hal-hal yang tidak mereka mengerti dengan anggota kelompok mereka. Keywords: problem based learning, students’ response, students’ worksheet