Academic literature on the topic 'Item-similarity Graph'
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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 (February 1, 2017): 579–99. http://dx.doi.org/10.1177/0146621617692977.
Full textAbstract:
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 analysis. The method is computationally efficient, effective for data with missing or incomplete responses, easy to implement, and often outperforms traditional clustering algorithms in the context of high dimensionality. The spectral clustering algorithm is based on graph theory, a branch of mathematics that studies the properties of graphs. The algorithm first constructs a graph of items, characterizing the similarity structure among items. It then extracts item clusters based on the graphical structure, grouping similar items together. The proposed method is evaluated through simulations and an application to the revised Eysenck Personality Questionnaire.
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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 (August 10, 2019): 55. http://dx.doi.org/10.3390/joitmc5030055.
Full textAbstract:
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 of the networks over the period 2005–2010 are investigated using the sliding window of six-month duration. We study the stability of the market graph and the company co-mention network over time and establish which of the two networks was more stable during the period. In addition, we examine the impact of the crisis of 2008 on the stability of the market graph as well as the company co-mention network. The networks that are considered in this paper and that are the objects of our study (the market graph and the company co-mention network) have a non-changing set of nodes (companies), and can change over time by adding/removing links between these nodes. Different graph similarity measures are used to evaluate these changes. If a network is stable over time, a measure of similarity between two graphs constructed for two different time windows should be close to zero. If there was a sharp change between the graphs constructed for two adjacent periods, then this should lead to a sharp increase in the value of the similarity measure between these two graphs. This paper uses the graph similarity measures which were proposed relatively recently. In addition, to estimate how the networks evolve over time we exploit QAP (Quadratic Assignment Procedure). While there is a sufficient amount of works studying the dynamics of graphs (including the use of graph similarity metrics), in this paper the company co-mention network dynamics is examined both individually and in comparison with the dynamics of market graphs for the first time.
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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 (July 11, 2022): 1071–96. http://dx.doi.org/10.3233/ida-205698.
Full textAbstract:
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 used to model the structure and the evolution of the graph. In contrast to existing work, ITeMs are edge-disjoint directed motifs that measure the temporal evolution of ordered edges within the motif. For a given temporal graph, we produce a feature vector of ITeM frequencies and the time it takes to form the ITeM instances. We apply this distribution to measure the similarity of temporal graphs. We show that ITeM has higher accuracy than other motif frequency-based approaches. We define various ITeM-based metrics that reveal salient properties of a temporal network. We also present importance sampling as a method to efficiently estimate the ITeM counts. We present a distributed implementation of the ITeM discovery algorithm using Apache Spark and GraphFrame. We evaluate our approach on both synthetic and real temporal networks.
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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 (December 6, 2019): 62–69. http://dx.doi.org/10.5755/j01.eie.25.6.24828.
Full textAbstract:
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 ratings. Recommendation generation in a bipartite graph is a link prediction problem. In current literature, modified link prediction approaches are used to distinguish between fundamental relational dualities of like vs. dislike and similar vs. dissimilar. However, the similarity relationship between users/items is mostly disregarded in the complex domain. The proposed model utilizes user-user and item-item cosine similarity value with the relational dualities in order to improve coverage and hits rate of the system by carefully incorporating similarities. On the standard MovieLens Hetrec and MovieLens datasets, the proposed similarity-inclusive link prediction method performed empirically well compared to other methods operating in the complex domain. The experimental results show that the proposed recommender system can be a plausible alternative to overcome the deficiencies in recommender systems.
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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.
Full textAbstract:
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 attributes hidden in different review text. On the other hand, the graph neural network can realize the information interaction in high dimensional space through deep architecture, which has been extensively studied in many fields. Therefore, in order to explore the high dimensional relevance between users and items hidden in the review information, we propose a new recommendation model enhancing interactive graph representation learning for review-based item recommendation, named IGRec. Specifically, we construct the user-review21 item graph with users/items as nodes and reviews as edges. We further add the connection of the user-user and the item-item to the graph by meta-path of user-item user and item-user-item. Then we utilize the attention mechanism to fuse edges information into nodes and apply the multilayer graph convolutional network to learn the high-order interactive information of nodes. Finally, we obtain the final embedding of user/item and adopt the factorization machine to complete the rating prediction. Experiments on the five real-world datasets demonstrate that the pro posed IGRec outperforms the state-of-the-art baselines.
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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 (May 11, 2021): 397–409. http://dx.doi.org/10.3233/xst-210846.
Full textAbstract:
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 categories without the requirement of retraining, we introduce an Attention-Based Graph Matching Network. METHODS: This model applies a few-shot semantic segmentation network to address the issue of prohibited item inspection. First, a pair of graphs are modelled between a query image and several support images. Then, after the pair of graphs are entered into two Graph Attention Units with similarity weights and equal weights, the attentive matching results will be obtained. According to the matching results, the prohibited items can be segmented from the query image. RESULTS: Experiment results and comparison using the Xray-PI dataset and SIXray dataset show that our model outperforms several other state-of-the-art learning models. CONCLUSIONS: This study demonstrates that the similarity loss function and the space restriction module proposed by our model can effectively remove noise and supplement spatial information, which makes the segmentation of the prohibited items on X-ray images more accurate.
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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 (January 20, 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 usefulness of the extracted frequent subgraphs, the methodology was applied to movie rating prediction. Using the frequent subgraphs as user profiles, the graph similarity between the rating graph and new item graph was calculated to predict the rating. The MovieLens dataset was used for the experiment, and a comparison showed that the proposed method outperformed other widely used recommendation methods. This study is meaningful in that it proposed an efficient method for extracting frequent subgraphs while maintaining semantic information and considering scalability in large-scale graphs. Furthermore, the proposed method can provide results that include semantic information to serve as a logical basis for rating prediction or recommendation, which existing methods are unable to provide.
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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.
Full textAbstract:
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 causes information redundancy. To address the above problems, we propose graph neural networks for preference social recommendation. First, a friend influence indicator is proposed to transform social networks into a new view for describing the similarity of friend preferences. We name the new view the Social Preference Network. Next, we use different GNNs to capture the respective information of the social preference network and the user-item interaction network, which effectively avoids information redundancy. Finally, we use two losses to penalize the unobserved user-item interaction and the unit space vector angle, respectively, to preserve the original connection relationship and widen the distance between positive and negative samples. Experiment results show that the proposed PSR is effective and lightweight for recommendation tasks, especially in dealing with cold-start problems.
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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 (August 31, 2022): 8764. http://dx.doi.org/10.3390/app12178764.
Full textAbstract:
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 feature space, the content features related to users and items are extracted through the attention mechanism on the knowledge graph, and then the content feature vectors of users and items are constructed. Finally, through the feature space fusion model, the behavior features and content features are projected into the same preference feature space, and then the fusion of the two feature spaces is completed to construct the complete vector representations of users and items and calculate the vector similarity to predict the score of the user to the item. This paper applies the presented model to public datasets in the fields of music and film. It can be found through the experimental results that KGRFSF can effectively improve the recommendation accuracy compared with the existing models.
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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 (May 12, 2021): 253–67. http://dx.doi.org/10.1177/01466216211013902.
Full textAbstract:
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) and different types of groups (connected components, cliques, or near-cliques). A comparison with an up-to-date method using real and simulated data is presented. Possible extensions and practical recommendations are given.
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.
Full textAbstract:
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 Recommender systems.
They usually deal with indicators of user preferences(purchase history/
rating history) for suggesting/predicting items for a target user.
An e↵ective way to cut-the-queue and straightaway hit the user’s
interest in shortest possible time, RS are extremely popular with
commercial systems today.
To generate relevant recommendations for users, our system tries
to leverage the interest patterns in the downloaded applications on
mobile phones of users themselves by using item-item similarity
graphs. This work essentially tries to overcome the inherent problem
of over-specialization in content based recommender system
by using graph approach.
This thesis first presents the background literature for recommender
systems and then proposes a graph based approach for recommending
serendipitous recommendations to a user.
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, 440–51. Berlin, Heidelberg: 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, 684–89. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30952-7_69.
Full textConference 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}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/642.
Full textAbstract:
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 behaviors) by converting interaction sequence data into multiple graphs (i.e., a user-item graph, an item-item graph, and a user-subseq graph). We design a novel graph convolutional network (PGCN) to learn shared representations of users and items with the three heterogeneous graphs. In our approach, a neighbor pooling and a convolution operation are designed to aggregate features of neighbors. Extensive experiments on two real-world datasets demonstrate that our graph convolution approaches outperform various competitive methods in terms of two metrics, and the heterogeneous graphs are proved effective for improving recommendation performance.
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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}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/283.
Full textAbstract:
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 clustering result. We model this budget allocation problem as a Markov decision process where item pairs are dynamically assigned to workers based on the historical similarity scores they provided. We propose an optimistic knowledge gradient policy where the assignment of items in each stage is based on the minimum-weight K-cut defined on a similarity graph. We provide simulation studies and real data analysis to demonstrate the performance of the proposed method.
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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 non-popular items (long tail). Using the Movielens 100k database, we conducted an experiment to calculate the accuracy, diversity, and popularity of the recommended items. With our hybrid approach we were able to improve the recall by up to 27.97 % when compared to the markov chain-based algorithm, which indicates greater targeting to long tail products. At the same time the recommended items were more diversified and less popular, which indicates greater targeting to long tail products.