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Relevant bibliographies by topics / Item-similarity Graph

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Academic literature on the topic 'Item-similarity Graph'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "Item-similarity Graph"

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Chen, Yunxiao, Xiaoou Li, Jingchen Liu, Gongjun Xu, and Zhiliang Ying. "Exploratory Item Classification Via Spectral Graph Clustering." Applied Psychological Measurement 41, no. 8 (2017): 579–99. http://dx.doi.org/10.1177/0146621617692977.

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Large-scale assessments are supported by a large item pool. An important task in test development is to assign items into scales that measure different characteristics of individuals, and a popular approach is cluster analysis of items. Classical methods in cluster analysis, such as the hierarchical clustering, K-means method, and latent-class analysis, often induce a high computational overhead and have difficulty handling missing data, especially in the presence of high-dimensional responses. In this article, the authors propose a spectral clustering algorithm for exploratory item cluster an

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Faizliev, Alexey, Vladimir Balash, Vladimir Petrov, Alexey Grigoriev, Dmitriy Melnichuk, and Sergei Sidorov. "Stability Analysis of Company Co-Mention Network and Market Graph Over Time Using Graph Similarity Measures." Journal of Open Innovation: Technology, Market, and Complexity 5, no. 3 (2019): 55. http://dx.doi.org/10.3390/joitmc5030055.

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The aim of the paper is to provide an analysis of news and financial data using their network representation. The formation of network structures from data sources is carried out using two different approaches: by building the so-called market graph in which nodes represent financial assets (e.g., stocks) and the edges between nodes stand for the correlation between the corresponding assets, by constructing a company co-mention network in which any two companies are connected by an edge if a news item mentioning both companies has been published in a certain period of time. Topological changes

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Purohit, Sumit, George Chin, and Lawrence B. Holder. "ITeM: Independent temporal motifs to summarize and compare temporal networks." Intelligent Data Analysis 26, no. 4 (2022): 1071–96. http://dx.doi.org/10.3233/ida-205698.

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Networks are a fundamental and flexible way of representing various complex systems. Many domains such as communication, citation, procurement, biology, social media, and transportation can be modeled as a set of entities and their relationships. Temporal networks are a specialization of general networks where every relationship occurs at a discrete time. The temporal evolution of such networks is as important to understand as the structure of the entities and relationships. We present the Independent Temporal Motif (ITeM) to characterize temporal graphs from different domains. ITeMs can be us

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Kurt, Zuhal, Kemal Ozkan, Alper Bilge, and Omer Nezih Gerek. "A Similarity-Inclusive Link Prediction Based Recommender System Approach." Elektronika ir Elektrotechnika 25, no. 6 (2019): 62–69. http://dx.doi.org/10.5755/j01.eie.25.6.24828.

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Despite being a challenging research field with many unresolved problems, recommender systems are getting more popular in recent years. These systems rely on the personal preferences of users on items given in the form of ratings and return the preferable items based on choices of like-minded users. In this study, a graph-based recommender system using link prediction techniques incorporating similarity metrics is proposed. A graph-based recommender system that has ratings of users on items can be represented as a bipartite graph, where vertices correspond to users and items and edges to ratin

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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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Zhu, Zhenyue, Shujing Lyu, and Yue Lu. "A few-shot segmentation method for prohibited item inspection." Journal of X-Ray Science and Technology 29, no. 3 (2021): 397–409. http://dx.doi.org/10.3233/xst-210846.

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BACKGROUND: With the rapid development of deep learning, several neural network models have been proposed for automatic segmentation of prohibited items. These methods usually based on a substantial amount of labelled training data. However, for some prohibited items of rarely appearing, it is difficult to obtain enough labelled samples. Furthermore, the category of prohibited items varies in different scenarios and security levels, and new items may appear from time to time. OBJECTIVE: In order to predict prohibited items with only a few annotated samples and inspect prohibited items of new c

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Lee, Kwangyon, Haemin Jung, June Seok Hong, and Wooju Kim. "Learning Knowledge Using Frequent Subgraph Mining from Ontology Graph Data." Applied Sciences 11, no. 3 (2021): 932. http://dx.doi.org/10.3390/app11030932.

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In many areas, vast amounts of information are rapidly accumulating in the form of ontology-based knowledge graphs, and the use of information in these forms of knowledge graphs is becoming increasingly important. This study proposes a novel method for efficiently learning frequent subgraphs (i.e., knowledge) from ontology-based graph data. An ontology-based large-scale graph is decomposed into small unit subgraphs, which are used as the unit to calculate the frequency of the subgraph. The frequent subgraphs are extracted through candidate generation and chunking processes. To verify the usefu

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Ma, Gang-Feng, Xu-Hua Yang, Yue Tong, and Yanbo Zhou. "Graph neural networks for preference social recommendation." PeerJ Computer Science 9 (May 19, 2023): e1393. http://dx.doi.org/10.7717/peerj-cs.1393.

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Social recommendation aims to improve the performance of recommendation systems with additional social network information. In the state of art, there are two major problems in applying graph neural networks (GNNs) to social recommendation: (i) Social network is connected through social relationships, not item preferences, i.e., there may be connected users with completely different preferences, and (ii) the user representation of current graph neural network layer of social network and user-item interaction network is the output of the mixed user representation of the previous layer, which ca

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Zhang, Suqi, Xinxin Wang, Rui Wang, Junhua Gu, and Jianxin Li. "Knowledge Graph Recommendation Model Based on Feature Space Fusion." Applied Sciences 12, no. 17 (2022): 8764. http://dx.doi.org/10.3390/app12178764.

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The existing recommendation model based on a knowledge graph simply integrates the behavior features in a user–item bipartite graph and the content features in a knowledge graph. However, the difference between the two feature spaces is ignored. To solve this problem, this paper presents a new recommendation model named the knowledge graph recommendation model based on feature space fusion (KGRFSF). Specifically, in the behavioral feature space, the behavioral features of users and items are constructed by extracting the behavioral feature from the user–item bipartite graph. In the content fea

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Belov, Dmitry I., and James A. Wollack. "Graph Theory Approach to Detect Examinees Involved in Test Collusion." Applied Psychological Measurement 45, no. 4 (2021): 253–67. http://dx.doi.org/10.1177/01466216211013902.

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Test collusion (TC) is sharing of test materials or answers to test questions before or during the test (important special case of TC is item preknowledge). Because of potentially large advantages for examinees involved, TC poses a serious threat to the validity of score interpretations. The proposed approach applies graph theory methodology to response similarity analyses for identifying groups of examinees involved in TC without using any knowledge about parts of test that were affected by TC. The approach supports different response similarity indices (specific to a particular type of TC) a

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Dissertations / Theses on the topic "Item-similarity Graph"

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BHANDARI, UPASANA. "SEREDIPITIOUS RECOMMENDATION FOR MOBILE APPLICATIONS USING ITEM - SIMILARITY GRAPH." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14508.

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Abstract The domain of mobile applications(apps) has recently surfaced and generated lot of interest in academia and industry alike. With an App-store for every leading operating system - Apple, Android, Blackberry,Windows, an explosive growth of Mobile applications is not a surprise. The absolute number of apps currently in existence, as well as their rates of growth, are remarkable. This might be good news for the developers from the revenue perspective but for consumers it means the inherent task of ”App Discovery” being intensified. A reasonable solution to this problem are Recom

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Book chapters on the topic "Item-similarity Graph"

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Bhandari, Upasna, Kazunari Sugiyama, Anindya Datta, and Rajni Jindal. "Serendipitous Recommendation for Mobile Apps Using Item-Item Similarity Graph." In Information Retrieval Technology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45068-6_38.

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Xu, Wei, Zhuoming Xu, and Bo Zhao. "A Graph Kernel Based Item Similarity Measure for Top-N Recommendation." In Web Information Systems and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30952-7_69.

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Conference papers on the topic "Item-similarity Graph"

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Xu, Yanan, Yanmin Zhu, Yanyan Shen, and Jiadi Yu. "Learning Shared Vertex Representation in Heterogeneous Graphs with Convolutional Networks for Recommendation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/642.

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Collaborative Filtering (CF) is among the most successful techniques in recommendation tasks. Recent works have shown a boost of performance of CF when introducing the pairwise relationships between users and items or among items (users) using interaction data. However, these works usually only utilize one kind of information, i.e., user preference in a user-item interaction matrix or item dependency in interaction sequences which can limit the recommendation performance. In this paper, we propose to mine three kinds of information (user preference, item dependency, and user similarity on beha

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Wang, Xiaozhou, Xi Chen, Qihang Lin, and Weidong Liu. "Bayesian Decision Process for Budget-efficient Crowdsourced Clustering." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/283.

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The performance of clustering depends on an appropriately defined similarity between two items. When the similarity is measured based on human perception, human workers are often employed to estimate a similarity score between items in order to support clustering, leading to a procedure called crowdsourced clustering. Assuming a monetary reward is paid to a worker for each similarity score and assuming the similarities between pairs and workers' reliability have a large diversity, when the budget is limited, it is critical to wisely assign pairs of items to different workers to optimize the cl

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De Sousa Silva, Diogo Vinícius, and Frederico Araújo Durão. "A Hybrid Approach to Recommend Long Tail Items." In XXIV Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/webmedia.2018.4550.

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Techniques in recommendation systems generally focuses on recommending the most important items for a user. The purpose of this work is to generate recommendations focusing on long tail items, and then to conduct the user to less popular items. However, such items are of great relevance to the user. Two techniques from the literature were applied in this study in a hybrid way. The first technique is through markov chains to calculate node similarity of a user item graph. The second technique applies clustering, where items are separated into distinct clusters: popular items (short tail) and no

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