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Articles de revues sur le sujet « Item of information »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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1

Wang, Shoujin, Wanggen Wan, Tong Qu, and Yanqiu Dong. "Auxiliary Information-Enhanced Recommendations." Applied Sciences 11, no. 19 (2021): 8830. http://dx.doi.org/10.3390/app11198830.

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Sequential recommendations have attracted increasing attention from both academia and industry in recent years. They predict a given user’s next choice of items by mainly modeling the sequential relations over a sequence of the user’s interactions with the items. However, most of the existing sequential recommendation algorithms mainly focus on the sequential dependencies between item IDs within sequences, while ignoring the rich and complex relations embedded in the auxiliary information, such as items’ image information and textual information. Such complex relations can help us better under

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Burns, Daniel J., Carin L. Jenkins, and Erica E. Dean. "Falsely recalled items are rich in item-specific information." Memory & Cognition 35, no. 7 (2007): 1630–40. http://dx.doi.org/10.3758/bf03193497.

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Akkermans, Wies, and Eiji Muraki. "Item information and discrimination functions for trinary pcm items." Psychometrika 62, no. 4 (1997): 569–78. http://dx.doi.org/10.1007/bf02294643.

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Haladyna, Thomas M., and Michael C. Rodriguez. "Using Full-information Item Analysis to Improve Item Quality." Educational Assessment 26, no. 3 (2021): 198–211. http://dx.doi.org/10.1080/10627197.2021.1946390.

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Wyse, Adam E., and Raymond Mapuranga. "Differential Item Functioning Analysis Using Rasch Item Information Functions." International Journal of Testing 9, no. 4 (2009): 333–57. http://dx.doi.org/10.1080/15305050903352040.

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Wang, Xi, and Yang Liu. "Detecting Compromised Items Using Information From Secure Items." Journal of Educational and Behavioral Statistics 45, no. 6 (2020): 667–89. http://dx.doi.org/10.3102/1076998620912549.

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In continuous testing programs, some items are repeatedly used across test administrations, and statistical methods are often used to evaluate whether items become compromised due to examinees’ preknowledge. In this study, we proposed a residual method to detect compromised items when a test can be partitioned into two subsets of items: secure items and possibly compromised items. We derived the standard error of the residual statistic by taking the sampling error in both ability and item parameter estimate into account. The simulation results suggest that the Type I error is close to the nomi

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Bock, R. Darrell, Robert Gibbons, and Eiji Muraki. "Full-Information Item Factor Analysis." Applied Psychological Measurement 12, no. 3 (1988): 261–80. http://dx.doi.org/10.1177/014662168801200305.

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Guo, Jingfeng, Chao Zheng, Shanshan Li, Yutong Jia, and Bin Liu. "BiInfGCN: Bilateral Information Augmentation of Graph Convolutional Networks for Recommendation." Mathematics 10, no. 17 (2022): 3042. http://dx.doi.org/10.3390/math10173042.

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The current graph-neural-network-based recommendation algorithm fully considers the interaction between users and items. It achieves better recommendation results, but due to a large amount of data, the interaction between users and items still suffers from the problem of data sparsity. To address this problem, we propose a method to alleviate the data sparsity problem by retaining user–item interactions while fully exploiting the association relationships between items and using side-information enhancement. We constructed a “twin-tower” model by combining a user–item training model and an it

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Verbic, Srdjan. "Information value of multiple response questions." Psihologija 45, no. 4 (2012): 467–85. http://dx.doi.org/10.2298/psi1204467v.

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Answers to Multiple Response (MR) questions carry more information than we usually utilize. Simple idea that all options of MR questions should be scored as independent test items has two major difficulties: 1) false options have item-response characteristics that are hard to model and use with other items; and 2) responses to individual options within the same MR question could be too dependent on each other. These difficulties lead to an overestimation of item discrimination and test information function. A few scoring methods that could increase information value obtained from MR questions

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Agrawal, Rakesh, Michalis Potamias, and Evimaria Terzi. "Learning the Nature of Information in Social Networks." Proceedings of the International AAAI Conference on Web and Social Media 6, no. 1 (2021): 2–9. http://dx.doi.org/10.1609/icwsm.v6i1.14257.

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We postulate that the nature of information items plays a vital role in the observed spread of these items in a social network. We capture this intuition by proposing a model that assigns to every information item two parameters: endogeneity and exogeneity. The endogeneity of the item quantifies its tendency to spread primarily through the connections between nodes; the exogeneity quantifies its tendency to be acquired by the nodes, independently of the underlying network. We also extend this item-based model to take into account the openness of each node to new information. We quantify openne

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Tamaki, Mitsushi, and J. George Shanthikumar. "A Full-Information Best-Choice Problem with Allowance." Probability in the Engineering and Informational Sciences 10, no. 1 (1996): 41–56. http://dx.doi.org/10.1017/s0269964800004162.

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This paper considers a variation of the classical full-information best-choice problem. The problem allows success to be obtained even when the best item is not selected, provided the item that is selected is within the allowance of the best item. Under certain regularity conditions on the allowance function, the general nature of the optimal strategy is given as well as an algorithm to determine it exactly. It is also examined how the success probability depends on the allowance function and the underlying distribution of the observed values of the items.

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Iwata, Tomoharu, and Naoki Marumo. "Co-Occurrence Estimation from Aggregated Data with Auxiliary Information." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4247–54. http://dx.doi.org/10.1609/aaai.v34i04.5847.

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Complete co-occurrence data are unavailable in many applications, including purchase records and medical histories, because of their high cost or privacy protection. Even with such applications, aggregated data would be available, such as the number of purchasers for each item and the number of patients with each disease. We propose a method for estimating the co-occurrence of items from aggregated data with auxiliary information. For auxiliary information, we use item features that describe the characteristics of each item. Although many methods have been proposed for estimating the co-occurr

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Sato, Koichi. "Recency Effects and Distinctiveness of Position/Order Information." Perceptual and Motor Skills 71, no. 1 (1990): 259–66. http://dx.doi.org/10.2466/pms.1990.71.1.259.

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Temporal retrieval theory argues that both short-term and long-term recency effects reflect the distinctiveness of position/order information of recent items. The present study tested this proposal in both the standard immediate free-recall paradigm and the continuous-distractor paradigm. Serial-position curves of item information learned intentionally were compared to those of position/order information learned incidentally. In the immediate condition, similar recency effects were observed for item and position/order information; the correlation of item recency with position/order recency was

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Jessen, Annika, Andrew D. Ho, C. Eduardo Corrales, Bevan Yueh, and Jennifer J. Shin. "Improving Measurement Efficiency of the Inner EAR Scale with Item Response Theory." Otolaryngology–Head and Neck Surgery 158, no. 6 (2018): 1093–100. http://dx.doi.org/10.1177/0194599818760528.

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Objectives (1) To assess the 11-item Inner Effectiveness of Auditory Rehabilitation (Inner EAR) instrument with item response theory (IRT). (2) To determine whether the underlying latent ability could also be accurately represented by a subset of the items for use in high-volume clinical scenarios. (3) To determine whether the Inner EAR instrument correlates with pure tone thresholds and word recognition scores. Design IRT evaluation of prospective cohort data. Setting Tertiary care academic ambulatory otolaryngology clinic. Subjects and Methods Modern psychometric methods, including factor an

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Blumenfeld, Robert S., Colleen M. Parks, Andrew P. Yonelinas, and Charan Ranganath. "Putting the Pieces Together: The Role of Dorsolateral Prefrontal Cortex in Relational Memory Encoding." Journal of Cognitive Neuroscience 23, no. 1 (2011): 257–65. http://dx.doi.org/10.1162/jocn.2010.21459.

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Results from fMRI have strongly supported the idea that the ventrolateral PFC (VLPFC) contributes to successful memory formation, but the role the dorsolateral PFC (DLPFC) in memory encoding is more controversial. Some findings suggest that the DLPFC is recruited when one is processing relationships between items in working memory, and this processing specifically promotes subsequent memory for these relationships. However, previous studies could not rule out the possibility that DLPFC promotes memory during all elaborative encoding conditions and contributes to memory on all subsequent associ

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Gaschler, Robert, and Peter A. Frensch. "Is information reduction an item‐specific or an item‐general process?" International Journal of Psychology 42, no. 4 (2007): 218–28. http://dx.doi.org/10.1080/00207590701396526.

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Hidayat, Rachmad. "Rancang Bangun Sistem Informasi Logistik." Jurnal Optimasi Sistem Industri 13, no. 2 (2016): 707. http://dx.doi.org/10.25077/josi.v13.n2.p707-724.2014.

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The purpose of this study to design a logistics management information system, which is able to overcome the problem of delay in sending information data items. This information system is designed to be easier to manage enterprise data, and makes all job become more effective and efficient. One of the conveniences one would be given through this information system is in search for an information about an item because all the data items will be recorded for inclusion into the database so that the information search an item will be easy and fast.Keywords: Information system, logistic, effective,

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Liu, Yezheng, Qiang Xiong, Jianshan Sun, Yuanchun Jiang, Thushari Silva, and Haifeng Ling. "Topic-based hierarchical Bayesian linear regression models for niche items recommendation." Journal of Information Science 45, no. 1 (2018): 92–104. http://dx.doi.org/10.1177/0165551518782831.

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A vital research concern for a personalised recommender system is to target items in the long tail. Studies have shown that sales of the e-commerce platform possess a long-tail character, and niche items in the long tail are challenging to be involved in the recommendation list. Since niche items are defined by the niche market, which is a small market segment, traditional recommendation algorithms focused more on popular items promotion and they do not apply to the niche market. In this article, we aim to find the best users for each niche item and proposed a topic-based hierarchical Bayesian

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Cai, Li, Ji Seung Yang, and Mark Hansen. "Generalized full-information item bifactor analysis." Psychological Methods 16, no. 3 (2011): 221–48. http://dx.doi.org/10.1037/a0023350.

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Zhou, Wei. "RFID and item-level information visibility." European Journal of Operational Research 198, no. 1 (2009): 252–58. http://dx.doi.org/10.1016/j.ejor.2008.09.017.

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Gibbons, Robert D., and Donald R. Hedeker. "Full-information item bi-factor analysis." Psychometrika 57, no. 3 (1992): 423–36. http://dx.doi.org/10.1007/bf02295430.

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Jang, Dongsoo, Seok-Kee Lee, and Qinglong Li. "ITS-Rec: A Sequential Recommendation Model Using Item Textual Information." Electronics 14, no. 9 (2025): 1748. https://doi.org/10.3390/electronics14091748.

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As the e-commerce industry rapidly expands, the number of users and items continues to grow, making it increasingly difficult to capture users’ purchasing patterns. Sequential recommendation models have emerged to address this issue by predicting the next item that a user is likely to purchase based on their historical behavior. However, most previous studies have focused primarily on modeling item sequences using item IDs without leveraging rich item-level information. To address this limitation, we propose a sequential recommendation model called ITS-Rec that incorporates various types of te

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Cui, Zeyu, Feng Yu, Shu Wu, Qiang Liu, and Liang Wang. "Disentangled Item Representation for Recommender Systems." ACM Transactions on Intelligent Systems and Technology 12, no. 2 (2021): 1–20. http://dx.doi.org/10.1145/3445811.

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Item representations in recommendation systems are expected to reveal the properties of items. Collaborative recommender methods usually represent an item as one single latent vector. Nowadays the e-commercial platforms provide various kinds of attribute information for items (e.g., category, price, and style of clothing). Utilizing this attribute information for better item representations is popular in recent years. Some studies use the given attribute information as side information, which is concatenated with the item latent vector to augment representations. However, the mixed item repres

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Wang, Xiaole, Jiwei Qin, Shangju Deng, and Wei Zeng. "Knowledge-Aware Enhanced Network Combining Neighborhood Information for Recommendations." Applied Sciences 13, no. 7 (2023): 4577. http://dx.doi.org/10.3390/app13074577.

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In recent years, the application of knowledge graphs to alleviate cold start and data sparsity problems of users and items in recommendation systems, has aroused great interest. In this paper, in order to address the insufficient representation of user and item embeddings in existing knowledge graph-based recommendation methods, a knowledge-aware enhanced network, combining neighborhood information recommendation (KCNR), is proposed. Specifically, KCNR first encodes prior information about the user–item interaction, and obtains the user’s different knowledge neighbors by propagating them in th

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Robitzsch, Alexander. "Regularized Generalized Logistic Item Response Model." Information 14, no. 6 (2023): 306. http://dx.doi.org/10.3390/info14060306.

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Item response theory (IRT) models are factor models for dichotomous or polytomous variables (i.e., item responses). The symmetric logistic or probit link functions are most frequently utilized for modeling dichotomous or polytomous items. In this article, we propose an IRT model for dichotomous and polytomous items using the asymmetric generalistic logistic link function that covers a lot of symmetric and asymmetric link functions. Compared to IRT modeling based on the logistic or probit link function, the generalized logistic link function additionally estimates two parameters related to the

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Robitzsch, Alexander. "Regularized Mislevy-Wu Model for Handling Nonignorable Missing Item Responses." Information 14, no. 7 (2023): 368. http://dx.doi.org/10.3390/info14070368.

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Missing item responses are frequently found in educational large-scale assessment studies. In this article, the Mislevy-Wu item response model is applied for handling nonignorable missing item responses. This model allows that the missingness of an item depends on the item itself and a further latent variable. However, with low to moderate amounts of missing item responses, model parameters for the missingness mechanism are difficult to estimate. Hence, regularized estimation using a fused ridge penalty is applied to the Mislevy-Wu model to stabilize estimation. The fused ridge penalty functio

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Sari, Halil Ibrahim, and Anthony Raborn. "What Information Works Best?: A Comparison of Routing Methods." Applied Psychological Measurement 42, no. 6 (2018): 499–515. http://dx.doi.org/10.1177/0146621617752990.

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There are many item selection methods proposed for computerized adaptive testing (CAT) applications. However, not all of them have been used in computerized multistage testing (ca-MST). This study uses some item selection methods as a routing method in ca-MST framework. These are maximum Fisher information (MFI), maximum likelihood weighted information (MLWI), maximum posterior weighted information (MPWI), Kullback–Leibler (KL), and posterior Kullback–Leibler (KLP). The main purpose of this study is to examine the performance of these methods when they are used as a routing method in ca-MST ap

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Adetutu, O. M., and H. B. Lawal. "APPLICATIONS OF ITEM RESPONSE THEORY MODELS TO ASSESS ITEM PROPERTIES AND STUDENTS’ ABILITIES IN DICHOTOMOUS RESPONSES ITEMS." Open Journal of Educational Development (ISSN: 2734-2050) 3, no. 1 (2022): 01–19. http://dx.doi.org/10.52417/ojed.v3i1.304.

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A test is a tool meant to measure the ability level of the students, and how well they can recall the subject matter, but items making up a test may be defectives, and thereby unable to measure students’ ability or traits satisfactorily as intended if proper attention is not paid to item properties such as difficulty, discrimination, and pseudo guessing indices (power) of each item. This could be remedied by item analysis and moderation. It is a known fact that the absence or improper use of item analysis could undermine the integrity of assessment, certification and placement in our education

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Arrington, Leticia, Sebastian Ueckert, Malidi Ahamadi, Sreeraj Macha, and Mats O. Karlsson. "Performance of longitudinal item response theory models in shortened or partial assessments." Journal of Pharmacokinetics and Pharmacodynamics 47, no. 5 (2020): 461–71. http://dx.doi.org/10.1007/s10928-020-09697-x.

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Abstract This work evaluates the performance of longitudinal item response (IR) theory models in shortened assessments using an existing model for part II and III of the MDS-UPDRS score. Based on the item information content, the assessment was reduced by removal of items in multiple increments and the models’ ability to recover the item characteristics of the remaining items at each level was evaluated. This evaluation was done for both simulated and real data. The metric of comparison in both cases was the item information function. For real data, the impact of shortening on the estimated di

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Meng, Fanshen, Zhenhua Meng, Ru Jin, Rongheng Lin, and Budan Wu. "DOGE: LLMs-Enhanced Hyper-Knowledge Graph Recommender for Multimodal Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 12399–407. https://doi.org/10.1609/aaai.v39i12.33351.

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In recent years, there has been a burgeoning interest in multimodal recommender systems within the recommendation systems domain. These systems aim to understand user preferences by leveraging both user interaction data and multimodal information associated with items. This approach frequently results in superior recommendation accuracy compared to traditional models that rely solely on user-item interactions. Despite the advancements of these methods, there is a relatively low utilization of image features in propagating item-item characteristics, an overreliance on text feature similarity, a

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Jacoby, Jacob, James J. Jaccard, Imran Currim, Alfred Kuss, Asim Ansari, and Tracy Troutman. "Tracing the Impact of Item-by-Item Information Accessing on Uncertainty Reduction." Journal of Consumer Research 21, no. 2 (1994): 291. http://dx.doi.org/10.1086/209398.

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Dorfman, Jennifer, and George Mandler. "Implicit and Explicit Forgetting: When is Gist Remembered?" Quarterly Journal of Experimental Psychology Section A 47, no. 3 (1994): 651–72. http://dx.doi.org/10.1080/14640749408401132.

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Recognition (YES/NO) and stem completion (cued: complete with a word from the list; and uncued: complete with the first word that comes to mind) were tested following either semantic or non-semantic processing of a categorized input list. Item/instance information was tested by contrasting target items from the input list with new items that were categorically related to them; gist/categorical information was tested by comparing target items semantically related to the input items with unrelated new items. For both recognition and stem completion, regardless of initial processing condition, it

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Endress, Ansgar D. "Memory and Proactive Interference for spatially distributed items." Memory & Cognition 50, no. 4 (2022): 782–816. http://dx.doi.org/10.3758/s13421-021-01239-1.

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AbstractOur ability to briefly retain information is often limited. Proactive Interference (PI) might contribute to these limitations (e.g., when items in recognition tests are difficult to reject after having appeared recently). In visual Working Memory (WM), spatial information might protect WM against PI, especially if encoding items together with their spatial locations makes item-location combinations less confusable than simple items without a spatial component. Here, I ask (1) if PI is observed for spatially distributed items, (2) if it arises among simple items or among item-location c

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Li, Jianfei, Yongbin Wang, Guohui Song, and Nenghuan Zhang. "A Session-Based Recommendation Model That Integrates the Temporal Sequence of Session Interactions and the Global Distance-Awareness of Items with Graph Neural Networks." Applied Sciences 13, no. 24 (2023): 13031. http://dx.doi.org/10.3390/app132413031.

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In the session-based recommendation algorithm, a better class of methods is to model the complex interaction relationship within the session as a graph structure, and then use the graph neural network to capture the deep features of the item from it. However, most models do not deeply mine the effective information contained in the sequence temporal relationship, nor do they pay attention to the auxiliary contribution of the global distance in different sessions to the item representation. This paper proposes a session-based recommendation model that integrates the temporal sequence of session

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Angraini, Angraini, Rose Alinda Alias, and Okfalisa Okfalisa. "Measuring information security policy compliance: content validity of questionnaire." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (2021): 469–75. https://doi.org/10.11591/ijeecs.v22.i1.pp469-475.

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Instruments used to measure compliance with information security policies have been developed by many researchers before, but only a few have conducted validity tests per item, especially for variables selected based on qualitative research. This study aims to validate the questionnaire will be used to measure user compliance with policies of information security. This study began by designing a questionnaire and conducting content validation using content ratio validation (CVR) and content index validation (CVI). As many as eight experts from the university assessed the items given. The resul

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Tu, Dongbo, Yuting Han, Yan Cai, and Xuliang Gao. "Item Selection Methods in Multidimensional Computerized Adaptive Testing With Polytomously Scored Items." Applied Psychological Measurement 42, no. 8 (2018): 677–94. http://dx.doi.org/10.1177/0146621618762748.

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Multidimensional computerized adaptive testing (MCAT) has been developed over the past decades, and most of them can only deal with dichotomously scored items. However, polytomously scored items have been broadly used in a variety of tests for their advantages of providing more information and testing complicated abilities and skills. The purpose of this study is to discuss the item selection algorithms used in MCAT with polytomously scored items (PMCAT). Several promising item selection algorithms used in MCAT are extended to PMCAT, and two new item selection methods are proposed to improve t

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Lopes, Paula, Patrícia Costa, Luisa Araujo, and Patrícia Ávila. "Measuring media and information literacy skills: Construction of a test." Communications 43, no. 4 (2018): 508–34. http://dx.doi.org/10.1515/commun-2017-0051.

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Abstract Research on media and information literacy has been growing exponentially over the past years, but it has focused more on the examination of media practices than on the assessment of media and information literacy skills. In this paper we describe the process of designing and implementing a Media and Information Literacy Test comprising 20 items. We present the results of the analysis carried out to validate the items and to construct a scale of media and information literacy skills using Item Response Theory (IRT). Findings indicate that the conceptual framework adopted is adequate t

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Zhao, Chuanchuan, Jinguo You, Xinxian Wen, and Xiaowu Li. "Deep Bi-LSTM Networks for Sequential Recommendation." Entropy 22, no. 8 (2020): 870. http://dx.doi.org/10.3390/e22080870.

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Recent years have seen a surge in approaches that combine deep learning and recommendation systems to capture user preference or item interaction evolution over time. However, the most related work only consider the sequential similarity between the items and neglects the item content feature information and the impact difference of interacted items on the next items. This paper introduces the deep bidirectional long short-term memory (LSTM) and self-attention mechanism into the sequential recommender while fusing the information of item sequences and contents. Specifically, we deal with the i

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Gu, Tianlong, Haohong Liang, Chenzhong Bin, and Liang Chang. "Combining user-end and item-end knowledge graph learning for personalized recommendation." Journal of Intelligent & Fuzzy Systems 40, no. 5 (2021): 9213–25. http://dx.doi.org/10.3233/jifs-201635.

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How to accurately model user preferences based on historical user behaviour and auxiliary information is of great importance in personalized recommendation tasks. Among all types of auxiliary information, knowledge graphs (KGs) are an emerging type of auxiliary information with nodes and edges that contain rich structural information and semantic information. Many studies prove that incorporating KG into personalized recommendation tasks can effectively improve the performance, rationality and interpretability of recommendations. However, existing methods either explore the independent meta-pa

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Angraini, Angraini, Rose Alinda Alias, and Okfalisa Okfalisa. "Measuring information security policy compliance: content validity of questionnaire." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (2021): 469. http://dx.doi.org/10.11591/ijeecs.v22.i1.pp469-475.

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<span>Instruments used to measure compliance with information security policies have been developed by many researchers before, but only a few have conducted validity tests per item, especially for variables selected based on qualitative research. This study aims to validate the questionnaire will be used to measure user compliance with policies of information security. This study began by designing a questionnaire and conducting content validation using content ratio validation (CVR) and content index validation (CVI). As many as eight experts from the university assessed the items give

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Merk, Christine, Frank Scholze, and Nils Windisch. "Item‐level usage statistics." Library Hi Tech 27, no. 1 (2009): 151–62. http://dx.doi.org/10.1108/07378830910942991.

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Li, Jianfei, Yongbin Wang, and Zhulin Tao. "A Rating Prediction Recommendation Model Combined with the Optimizing Allocation for Information Granularity of Attributes." Information 13, no. 1 (2022): 21. http://dx.doi.org/10.3390/info13010021.

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In recent years, graph neural networks (GNNS) have been demonstrated to be a powerful way to learn graph data. The existing recommender systems based on the implicit factor models mainly use the interactive information between users and items for training and learning. A user–item graph, a user–attribute graph, and an item–attribute graph are constructed according to the interactions between users and items. The latent factors of users and items can be learned in these graph structure data. There are many methods for learning the latent factors of users and items. Still, they do not fully cons

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Jonker, Tanya R., Jeffrey D. Wammes, and Colin M. MacLeod. "Drawing enhances item information but undermines sequence information in memory." Journal of Experimental Psychology: Learning, Memory, and Cognition 45, no. 4 (2019): 689–99. http://dx.doi.org/10.1037/xlm0000610.

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Mislevy, Robert J. "Exploiting Auxiliary Information About Items in the Estimation of Rasch Item Difficulty Parameters." Applied Psychological Measurement 12, no. 3 (1988): 281–96. http://dx.doi.org/10.1177/014662168801200306.

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Mislevy, Robert J. "EXPLOITING AUXILIARY INFORMATION ABOUT ITEMS IN THE ESTIMATION OF RASCH ITEM DIFFICULTY PARAMETERS." ETS Research Report Series 1987, no. 1 (1987): i—48. http://dx.doi.org/10.1002/j.2330-8516.1987.tb00230.x.

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Tepe, Rodger, and Chabha Tepe. "Development and psychometric evaluation of an information literacy self-efficacy survey and an information literacy knowledge test*." Journal of Chiropractic Education 29, no. 1 (2015): 11–15. http://dx.doi.org/10.7899/jce-14-15.

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Objective To develop and psychometrically evaluate an information literacy (IL) self-efficacy survey and an IL knowledge test. Methods In this test–retest reliability study, a 25-item IL self-efficacy survey and a 50-item IL knowledge test were developed and administered to a convenience sample of 53 chiropractic students. Item analyses were performed on all questions. Results The IL self-efficacy survey demonstrated good reliability (test–retest correlation = 0.81) and good/very good internal consistency (mean κ = .56 and Cronbach's α = .92). A total of 25 questions with the best item analysi

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Shen, Guojiang, Jiajia Tan, Zhi Liu, and Xiangjie Kong. "Enhancing interactive graph representation learning for review-based item recommendation." Computer Science and Information Systems, no. 00 (2021): 64. http://dx.doi.org/10.2298/csis210228064s.

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Collaborative filtering has been successful in the recommendation systems of various scenarios, but it is also hampered by issues such as cold start and data sparsity. To alleviate the above problems, recent studies have attempted to integrate review information into models to improve accuracy of rating prediction. While most of the existing models respectively utilize independent module to ex tract the latent feature representation of user reviews and item reviews, ignoring the correlation between the latent features, which may fail to capture the similarity of user preferences and item attri

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Zhang, Suqi, Xinxin Wang, Wenfeng Wang, Ningjing Zhang, Yunhao Fang, and Jianxin Li. "Recommendation model based on intention decomposition and heterogeneous information fusion." Mathematical Biosciences and Engineering 20, no. 9 (2023): 16401–20. http://dx.doi.org/10.3934/mbe.2023732.

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<abstract> <p>In order to solve the problem of timeliness of user and item interaction intention and the noise caused by heterogeneous information fusion, a recommendation model based on intention decomposition and heterogeneous information fusion (IDHIF) is proposed. First, the intention of the recently interacting items and the users of the recently interacting candidate items is decomposed, and the short feature representation of users and items is mined through long-short term memory and attention mechanism. Then, based on the method of heterogeneous information fusion, the int

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Huang, Jinchao, Zhipu Xie, Han Zhang, Bin Yang, Chong Di, and Runhe Huang. "Enhancing Knowledge-Aware Recommendation with Dual-Graph Contrastive Learning." Information 15, no. 9 (2024): 534. http://dx.doi.org/10.3390/info15090534.

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Incorporating knowledge graphs as auxiliary information to enhance recommendation systems can improve the representations learning of users and items. Recommendation methods based on knowledge graphs can introduce user–item interaction learning into the item graph, focusing only on learning the node vector representations within a single graph; alternatively, they can treat user–item interactions and item graphs as two separate graphs and learn from each graph individually. Learning from two graphs has natural advantages in exploring original information and interaction information, but faces

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Chen, Jyun-Hong, Hsiu-Yi Chao, and Shu-Ying Chen. "A Dynamic Stratification Method for Improving Trait Estimation in Computerized Adaptive Testing Under Item Exposure Control." Applied Psychological Measurement 44, no. 3 (2019): 182–96. http://dx.doi.org/10.1177/0146621619843820.

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When computerized adaptive testing (CAT) is under stringent item exposure control, the precision of trait estimation will substantially decrease. A new item selection method, the dynamic Stratification method based on Dominance Curves (SDC), which is aimed at improving trait estimation, is proposed to mitigate this problem. The objective function of the SDC in item selection is to maximize the sum of test information for all examinees rather than maximizing item information for individual examinees at a single-item administration, as in conventional CAT. To achieve this objective, the SDC uses

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