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1
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
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Cai, Hongyun, Jichao Ren, Jing Zhao, Shilin Yuan, and Jie Meng. "KC-GCN: A Semi-Supervised Detection Model against Various Group Shilling Attacks in Recommender Systems." Wireless Communications and Mobile Computing 2023 (February 16, 2023): 1–15. http://dx.doi.org/10.1155/2023/2854874.
Повний текст джерелаАнотація:
Various detection methods have been proposed for defense against group shilling attacks in recommender systems; however, these methods cannot effectively detect attack groups generated based on adversarial attacks (e.g., GOAT) or mixed attack groups. In this study, we propose a two-stage method, called KC-GCN, which is based on k -cliques and graph convolutional networks. First, we construct a user relationship graph, generate suspicious candidate groups, and extract influential users by calculating the user nearest-neighbor similarity. We construct the user relationship graph by calculating the edge weight between any two users through analyzing their similarity over suspicious time intervals on each item. Second, we combine the extracted user initial embeddings and the structural features hidden in the user relationship graph to detect attackers. On the Netflix and sampled Amazon datasets, the detection results of KC-GCN surpass those of the state-of-the-art methods under different types of group shilling attacks. The F1-measure of KC-GCN can reach above 93% and 87% on these two datasets, respectively.
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Wang, Huansha, Ruiyang Huang, and Jianpeng Zhang. "Person Entity Alignment Method Based on Multimodal Information Aggregation." Electronics 11, no. 19 (October 1, 2022): 3163. http://dx.doi.org/10.3390/electronics11193163.
Повний текст джерелаАнотація:
Entity alignment is used to determine whether entities from different sources refer to the same object in the real world. It is one of the key technologies for constructing large-scale knowledge graphs and is widely used in the fields of knowledge graphs and knowledge complementation. Because of the lack of semantic connection between the visual modality face attribute of the person entity and the text modality attribute and relationship information, it is difficult to model the visual and text modality into the same semantic space, and, as a result, that the traditional multimodal entity alignment method cannot be applied. In view of the scarcity of multimodal person relation graphs datasets and the difficulty of the multimodal semantic modeling of person entities, this paper analyzes and crawls open-source semi-structured data from different sources to build a multimodal person entity alignment dataset and focuses on using the facial and semantic information of multimodal person entities to improve the similarity of entity structural features which are modeled using the graph convolution layer and the dynamic graph attention layer to calculate the similarity. Through verification on the self-made multimodal person entity alignment dataset, the method proposed in this paper is compared with other entity alignment models which have a similar structure. Compared with AliNet, the probability that the first item in the candidate pre-aligned entity set is correct is increased by 12.4% and average ranking of correctly aligned entities in the candidate pre-aligned entity set decreased by 32.8, which proves the positive effect of integrating multimodal facial information, applying dynamic graph attention and a layer-wise gated network to improve the alignment effect of person entities.
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Chen, Hong, Ming Xin Gan, and Meng Zhao Song. "An Improved Recommendation Algorithm Based on Graph Model." Applied Mechanics and Materials 380-384 (August 2013): 1266–69. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1266.
Повний текст джерелаАнотація:
According to the problem that the traditional search algorithms dont consider the needs of individuals, various recommender systems employing different data representations and recommendation methods are currently used to cope with these challenges. In this paper, inspired by the network-based user-item rating matrix, we introduce an improved algorithm which combines the similarity of items with a dynamic resource allocation process. To demonstrate its accuracy and usefulness, this paper compares the proposed algorithm with collaborative filtering algorithm using data from MovieLens. The evaluation shows that, the improved recommendation algorithm based on graph model achieves more accurate predictions and more reasonable recommendation than collaborative filtering algorithm or the basic graph model algorithm does.
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Shang, Songtao, Wenqian Shang, Minyong Shi, Shuchao Feng, and Zhiguo Hong. "A Video Recommendation Algorithm Based on Hyperlink-Graph Model." International Journal of Software Innovation 5, no. 3 (July 2017): 49–63. http://dx.doi.org/10.4018/ijsi.2017070104.
Повний текст джерелаАнотація:
The traditional graph-based personal recommendation algorithms mainly depend the user-item model to construct a bipartite graph. However, the traditional algorithms have low efficiency, because the matrix of the algorithms is sparse and it cost lots of time to compute the similarity between users or items. Therefore, this paper proposes an improved video recommendation algorithm based on hyperlink-graph model. This method cannot only improve the accuracy of the recommendation algorithms, but also reduce the running time. Furthermore, the Internet users may have different interests, for example, a user interest in watching news videos, and at the same time he or she also enjoy watching economic and sports videos. This paper proposes a complement algorithm based on hyperlink-graph for video recommendations. This algorithm improves the accuracy of video recommendations by cross clustering in user layers.
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Mu, Ruihui, and Xiaoqin Zeng. "Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph." Mathematical Problems in Engineering 2018 (July 31, 2018): 1–11. http://dx.doi.org/10.1155/2018/9617410.
Повний текст джерелаАнотація:
To solve the problem that collaborative filtering algorithm only uses the user-item rating matrix and does not consider semantic information, we proposed a novel collaborative filtering recommendation algorithm based on knowledge graph. Using the knowledge graph representation learning method, this method embeds the existing semantic data into a low-dimensional vector space. It integrates the semantic information of items into the collaborative filtering recommendation by calculating the semantic similarity between items. The shortcoming of collaborative filtering algorithm which does not consider the semantic information of items is overcome, and therefore the effect of collaborative filtering recommendation is improved on the semantic level. Experimental results show that the proposed algorithm can get higher values on precision, recall, and F-measure for collaborative filtering recommendation.
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Ulitzsch, Esther, Qiwei He, Vincent Ulitzsch, Hendrik Molter, André Nichterlein, Rolf Niedermeier, and Steffi Pohl. "Combining Clickstream Analyses and Graph-Modeled Data Clustering for Identifying Common Response Processes." Psychometrika 86, no. 1 (February 5, 2021): 190–214. http://dx.doi.org/10.1007/s11336-020-09743-0.
Повний текст джерелаАнотація:
AbstractComplex interactive test items are becoming more widely used in assessments. Being computer-administered, assessments using interactive items allow logging time-stamped action sequences. These sequences pose a rich source of information that may facilitate investigating how examinees approach an item and arrive at their given response. There is a rich body of research leveraging action sequence data for investigating examinees’ behavior. However, the associated timing data have been considered mainly on the item-level, if at all. Considering timing data on the action-level in addition to action sequences, however, has vast potential to support a more fine-grained assessment of examinees’ behavior. We provide an approach that jointly considers action sequences and action-level times for identifying common response processes. In doing so, we integrate tools from clickstream analyses and graph-modeled data clustering with psychometrics. In our approach, we (a) provide similarity measures that are based on both actions and the associated action-level timing data and (b) subsequently employ cluster edge deletion for identifying homogeneous, interpretable, well-separated groups of action patterns, each describing a common response process. Guidelines on how to apply the approach are provided. The approach and its utility are illustrated on a complex problem-solving item from PIAAC 2012.
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Bin, Zhang, and Wang Xiao Dong. "Friends Recommendation Algorithm Based on Graph Mining and Collaborative Filtering." Applied Mechanics and Materials 235 (November 2012): 399–402. http://dx.doi.org/10.4028/www.scientific.net/amm.235.399.
Повний текст джерелаАнотація:
As an open, free, flexible social platform, microblog develops rapidly recent years. State of art research on friends recommendation has attract both industrial and academical concerns. Compared with traditional social networks, microblog contains both strong social relations based on the real relationship, and weak social relations based on interests, locations and other incidental factors. How to utilize these relationships and characters in personalized friends recommendation is still under research. This paper presents a new hybrid recommendation model, considering both the relationship strength and interest similarity in microblog, using the social graph mining algorithm to find strong social relations and the item-based collaborative filtering algorithm to mine weak social relations. Experimental results show that the proposed hybrid algorithm outperforms the traditional algorithm.
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Liu, Xi, Rui Song, Yuhang Wang, and Hao Xu. "A Multi-Granular Aggregation-Enhanced Knowledge Graph Representation for Recommendation." Information 13, no. 5 (April 29, 2022): 229. http://dx.doi.org/10.3390/info13050229.
Повний текст джерелаАнотація:
Knowledge graph (KG) helps to improve the accuracy, diversity, and interpretability of a recommender systems. KG has been applied in recommendation systems, exploiting graph neural networks (GNNs), but most existing recommendation models based on GNNs ignore the influence of node types and the loss of information during aggregation. In this paper, we propose a new model, named A Multi-Granular Aggregation-Enhanced Knowledge Graph Representation for Recommendation (MAKR), that relieves the sparsity of the network and overcomes the limitation of information loss of the traditional GNN recommendation model. Specifically, we propose a new graph, named the Improved Collaborative Knowledge Graph (ICKG), that integrates user–item interaction and a knowledge graph into a huge heterogeneous network, divides the nodes in the heterogeneous network into three categories—users, items, and entities, and connects the edges according to the similarity between the users and items so as to enhance the high-order connectivity of the graph. In addition, we used attention mechanisms, the factorization machine (FM), and transformer (Trm) algorithms to aggregate messages from multi-granularity and different types to improve the representation ability of the model. The empirical results of three public benchmarks showed that MAKR outperformed state-of-the-art methods such as Neural FM, RippleNet, and KGAT.
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Ma, Manfu, Dongliang Yang, and Yong Li. "Session Recommendation Based on Edge Information Clustering." Journal of Physics: Conference Series 2363, no. 1 (November 1, 2022): 012003. http://dx.doi.org/10.1088/1742-6596/2363/1/012003.
Повний текст джерелаАнотація:
Traditional session recommendation mainly uses the time sequence of users clicking items to construct a user session graph, which often ignores the similarity and differences between user groups. To improve the effect of recommendation, an E-SGNN (E-SGNN, Edge-Session Graph Neural Network) method combining edge information clustering and session recommendation is proposed. Firstly, similar users are clustered by edge information and divided into different session user groups. After extracting the data features of the user site relationship graph in the session, it is reset and updated through the gated graph neural network (GGNN); Secondly, a self-attention mechanism is introduced to adjust the proportion of users’ current preference and historical preference; Finally, the ranking score is obtained through linear transformation and softmax classifier. The higher the score, the more obvious the user’s preference for the item. Experiments show that compared with session-based graph neural network and cross-session information recommendation, the E-SGNN algorithm proposed in this paper has a significant improvement in recall rate and average reciprocal ranking. When the three edge parameters are combined, the recall rate reaches 98.97% and the average reciprocal ranking reaches 45.77%.
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Liu, Yudong, and Wen Chen. "Recommendation Model Based on Semantic Features and a Knowledge Graph." Wireless Communications and Mobile Computing 2021 (July 20, 2021): 1–9. http://dx.doi.org/10.1155/2021/2382892.
Повний текст джерелаАнотація:
In the field of information science, how to help users quickly and accurately find the information they need from a tremendous amount of short texts has become an urgent problem. The recommendation model is an important way to find such information. However, existing recommendation models have some limitations in case of short text recommendation. To address these issues, this paper proposes a recommendation model based on semantic features and a knowledge graph. More specifically, we first select DBpedia as a knowledge graph to extend short text features of items and get the semantic features of the items based on the extended text. And then, we calculate the item vector and further obtain the semantic similarity degrees of the users. Finally, based on the semantic features of the items and the semantic similarity of the users, we apply the collaborative filtering technology to calculate prediction rating. A series of experiments are conducted, demonstrating the effectiveness of our model in the evaluation metrics of mean absolute error (MAE) and root mean square error (RMSE) compared with those of some recommendation algorithms. The optimal MAE for the model proposed in this paper is 0.6723, and RMSE is 0.8442. The promising results show that the recommendation effect of the model on the movie field is significantly better than those of these existing algorithms.
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Zhu, Lingxiao. "E-Commerce Recommendation Algorithm based on Graph Neural Network." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 1264–68. http://dx.doi.org/10.54097/hset.v39i.6752.
Повний текст джерелаАнотація:
While recommender system is becoming an increasingly essential component in e-commerce websites, although previous models, which directly calculate the similarity of user/item history record, has obtained evidence of effectiveness, recommendations based solely on users' current sequence of actions, when user identity and history preference are not present, has been a popular area due to the growing privacy concerns. This paper demonstrates a model using a graph neural network, which takes the user's sequence of purchasing events as input and constructs a graph derived from it, to make the prediction of the most likely subsequent product that the customer may purchase and make personalized recommendations by the combination of session preference and user’s current interest. Experiments on a real-world e-commerce purchasing event dataset and analysis are carried out to test the model’s performance, as well as how the length of sequence may affect the model preference. The result shows that the model performance has attained a local peak on the dataset used.
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Gong, Xiaolong, Linpeng Huang, and Fuwei Wang. "Feature Sampling Based Unsupervised Semantic Clustering for Real Web Multi-View Content." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 102–9. http://dx.doi.org/10.1609/aaai.v33i01.3301102.
Повний текст джерелаАнотація:
Real web datasets are often associated with multiple views such as long and short commentaries, users preference and so on. However, with the rapid growth of user generated texts, each view of the dataset has a large feature space and leads to the computational challenge during matrix decomposition process. In this paper, we propose a novel multi-view clustering algorithm based on the non-negative matrix factorization that attempts to use feature sampling strategy in order to reduce the complexity during the iteration process. In particular, our method exploits unsupervised semantic information in the learning process to capture the intrinsic similarity through a graph regularization. Moreover, we use Hilbert Schmidt Independence Criterion (HSIC) to explore the unsupervised semantic diversity information among multi-view contents of one web item. The overall objective is to minimize the loss function of multi-view non-negative matrix factorization that combines with an intra-semantic similarity graph regularizer and an inter-semantic diversity term. Compared with some state-of-the-art methods, we demonstrate the effectiveness of our proposed method on a large real-world dataset Doucom and the other three smaller datasets.
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Xu, Gongwen, Guangyu Jia, Lin Shi, and Zhijun Zhang. "Personalized Course Recommendation System Fusing with Knowledge Graph and Collaborative Filtering." Computational Intelligence and Neuroscience 2021 (September 25, 2021): 1–8. http://dx.doi.org/10.1155/2021/9590502.
Повний текст джерелаАнотація:
Personalized courses recommendation technology is one of the hotspots in online education field. A good recommendation algorithm can stimulate learners’ enthusiasm and give full play to different learners’ learning personality. At present, the popular collaborative filtering algorithm ignores the semantic relationship between recommendation items, resulting in unsatisfactory recommendation results. In this paper, an algorithm combining knowledge graph and collaborative filtering is proposed. Firstly, the knowledge graph representation learning method is used to embed the semantic information of the items into a low-dimensional semantic space; then, the semantic similarity between the recommended items is calculated, and then, this item semantic information is fused into the collaborative filtering recommendation algorithm. This algorithm increases the performance of recommendation at the semantic level. The results show that the proposed algorithm can effectively recommend courses for learners and has higher values on precision, recall, and F1 than the traditional recommendation algorithm.
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Chen, Ya, Hongliang Yuan, Tingting Liu, and Nan Ding. "Name Disambiguation Based on Graph Convolutional Network." Scientific Programming 2021 (May 8, 2021): 1–11. http://dx.doi.org/10.1155/2021/5577692.
Повний текст джерелаАнотація:
Recently, massive online academic resources have provided convenience for scientific study and research. However, the author name ambiguity degrades the user experience in retrieving the literature bases. Extracting the features of papers and calculating the similarity for clustering constitute the mainstream of present name disambiguation approaches, which can be divided into two branches: clustering based on attribute features and clustering based on linkage information. They cannot however get high performance. In order to improve the efficiency of literature retrieval and provide technical support for the accurate construction of literature bases, a name disambiguation method based on Graph Convolutional Network (GCN) is proposed. The disambiguation model based on GCN designed in this paper combines both attribute features and linkage information. We first build paper-to-paper graphs, coauthor graphs, and paper-to-author graphs for each reference item of a name. The nodes in the graphs contain attribute features and the edges contain linkage features. The graphs are then fed to a specialized GCN and output a hybrid representation. Finally, we use the hierarchical clustering algorithm to divide the papers into disjoint clusters. Finally, we cluster the papers using a hierarchical algorithm. The experimental results show that the proposed model achieves average F1 value of 77.10% on three name disambiguation datasets. In order to let the model automatically select the appropriate number of convolution layers and adapt to the structure of different local graphs, we improve upon the prior GCN model by utilizing attention mechanism. Compared with the original GCN model, it increases the average precision and F1 value by 2.05% and 0.63%, respectively. What is more, we build a bilingual dataset, BAT, which contains various forms of academic achievements and will be an alternative in future research of name disambiguation.
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Guo, Lei, Hongzhi Yin, Tong Chen, Xiangliang Zhang, and Kai Zheng. "Hierarchical Hyperedge Embedding-Based Representation Learning for Group Recommendation." ACM Transactions on Information Systems 40, no. 1 (January 31, 2022): 1–27. http://dx.doi.org/10.1145/3457949.
Повний текст джерелаАнотація:
Group recommendation aims to recommend items to a group of users. In this work, we study group recommendation in a particular scenario, namely occasional group recommendation, where groups are formed ad hoc and users may just constitute a group for the first time—that is, the historical group-item interaction records are highly limited. Most state-of-the-art works have addressed the challenge by aggregating group members’ personal preferences to learn the group representation. However, the representation learning for a group is most complex beyond the aggregation or fusion of group member representation, as the personal preferences and group preferences may be in different spaces and even orthogonal. In addition, the learned user representation is not accurate due to the sparsity of users’ interaction data. Moreover, the group similarity in terms of common group members has been overlooked, which, however, has the great potential to improve the group representation learning. In this work, we focus on addressing the aforementioned challenges in the group representation learning task, and devise a hierarchical hyperedge embedding-based group recommender, namely HyperGroup. Specifically, we propose to leverage the user-user interactions to alleviate the sparsity issue of user-item interactions, and design a graph neural network-based representation learning network to enhance the learning of individuals’ preferences from their friends’ preferences, which provides a solid foundation for learning groups’ preferences. To exploit the group similarity (i.e., overlapping relationships among groups) to learn a more accurate group representation from highly limited group-item interactions, we connect all groups as a network of overlapping sets (a.k.a. hypergraph), and treat the task of group preference learning as embedding hyperedges (i.e., user sets/groups) in a hypergraph, where an inductive hyperedge embedding method is proposed. To further enhance the group-level preference modeling, we develop a joint training strategy to learn both user-item and group-item interactions in the same process. We conduct extensive experiments on two real-world datasets, and the experimental results demonstrate the superiority of our proposed HyperGroup in comparison to the state-of-the-art baselines.
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Liu, Zhen, Huanyu Meng, Shuang Ren, and Feng Liu. "Reliable Collaborative Filtering on Spatio-Temporal Privacy Data." Security and Communication Networks 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/9127612.
Повний текст джерелаАнотація:
Lots of multilayer information, such as the spatio-temporal privacy check-in data, is accumulated in the location-based social network (LBSN). When using the collaborative filtering algorithm for LBSN location recommendation, one of the core issues is how to improve recommendation performance by combining the traditional algorithm with the multilayer information. The existing approaches of collaborative filtering use only the sparse user-item rating matrix. It entails high computational complexity and inaccurate results. A novel collaborative filtering-based location recommendation algorithm called LGP-CF, which takes spatio-temporal privacy information into account, is proposed in this paper. By mining the users check-in behavior pattern, the dataset is segmented semantically to reduce the data size that needs to be computed. Then the clustering algorithm is used to obtain and narrow the set of similar users. User-location bipartite graph is modeled using the filtered similar user set. Then LGP-CF can quickly locate the location and trajectory of users through message propagation and aggregation over the graph. Through calculating users similarity by spatio-temporal privacy data on the graph, we can finally calculate the rating of recommendable locations. Experiments results on the physical clusters indicate that compared with the existing algorithms, the proposed LGP-CF algorithm can make recommendations more accurately.
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Pavithra, C., and M. Saradha. "Integrating Collaborative Filtering Technique Using Rating Approach to Ascertain Similarity Between the Users." Scalable Computing: Practice and Experience 23, no. 4 (December 22, 2022): 171–79. http://dx.doi.org/10.12694/scpe.v23i4.2015.
Повний текст джерелаАнотація:
The recommender system handles the plethora of data by filtering the most crucial information based on the dataset provided by a user and other criterion that are taken into account.(i.e., user's choice and interest). It determines whether a user and an item are compatible and then assumes that they are similar in order to make recommendations. Recommendation system uses Singular value decomposition method as collaborative filtering technique. The objective of this research paper is to propose the recommendation system that has an ability to recommend products to users based on ratings. We collect essential information like ratings given by the users from e-commerce that are required for recommendation, Initially the dataset that are gathered are sparse dataset, cosine similarity is used to find the similarity between the users. Subsequently, we collect non-sparse data and use Euclidian distance and Manhattan distance method to measure the distance between users and the graph is plotted, this ensures the similar liking and preferences between them. This method of making recommendations are more reliable and attainable.
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Wang, Dan. "Analysis of Sentiment and Personalised Recommendation in Musical Performance." Computational Intelligence and Neuroscience 2022 (June 2, 2022): 1–6. http://dx.doi.org/10.1155/2022/2778181.
Повний текст джерелаАнотація:
Music performance research is a comprehensive study of aspects such as emotional analysis and personalisation in music performance, which help to add richness and creativity to the art of music performance. The labels in this paper in collaborative annotation contain rich personalised descriptive information as well as item content information and can therefore be used to help provide better recommendations. The algorithm is based on bipartite graph node structure similarity and restarted random wandering. It analyses the connection between users, items, and tags in the music social network, firstly constructs the adjacency relationship between music and tags, obtains the music recommendation list and indirectly associated music collection, then fuses the results according to the proposed algorithm, and reorders them to obtain the final recommendation list, thus realising the personalised music recommendation algorithm. The experiments show that the proposed method can meet the personalised demand of users for music on this dataset.
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Li, Bin, and Ting Zhang. "An Algorithm of Scene Information Collection in General Football Matches Based on Web Documents." Security and Communication Networks 2021 (October 14, 2021): 1–11. http://dx.doi.org/10.1155/2021/5801631.
Повний текст джерелаАнотація:
In order to obtain the scene information of the ordinary football game more comprehensively, an algorithm of collecting the scene information of the ordinary football game based on web documents is proposed. The commonly used T-graph web crawler model is used to collect the sample nodes of a specific topic in the football game scene information and then collect the edge document information of the football game scene information topic after the crawling stage of the web crawler. Using the feature item extraction algorithm of semantic analysis, according to the similarity of the feature items, the feature items of the football game scene information are extracted to form a web document. By constructing a complex network and introducing the local contribution and overlap coefficient of the community discovery feature selection algorithm, the features of the web document are selected to realize the collection of football game scene information. Experimental results show that the algorithm has high topic collection capabilities and low computational cost, the average accuracy of equilibrium is always around 98%, and it has strong quantification capabilities for web crawlers and communities.
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Zhou, Jieqiong, Zhenhua Wei, Bin Peng, and Fangchun Chi. "Research and Application of Film and Television Literature Recommendation Based on Secure Internet of Things and Machine Learning." Mobile Information Systems 2021 (October 15, 2021): 1–10. http://dx.doi.org/10.1155/2021/4066267.
Повний текст джерелаАнотація:
Film and television literature recommendation is an AI algorithm that recommends related content according to user preferences and records. The wide application in various APPs and websites provides users with great convenience. This article aims to study the Internet of Things and machine learning technology, combining deep learning, reinforcement learning, and recommendation algorithms, to achieve accurate recommendation of film and television literature. This paper proposes to use the ConvMF-KNN recommendation model to verify and analyze the four models of PMF, ConvM, ConvMF-word2vec, and ConvMF-KNN, respectively, on public datasets. Using the path information between vertices in bipartite graph and considering the degree of vertices, the similarity between items is calculated, and the neighbor item set of items is obtained. The experimental results show that the ConvMF-KNN model combined with the KNN idea effectively improves the recommendation accuracy. Compared with the accuracy of the PMF model on the MovieLens 100 k, MovieLens 1 M, and AIV datasets, the accuracy of the ConvMF model on the above three datasets is 5.26%, 6.31%, and 26.71%, respectively, an increase of 2.26%, 1.22%, and 7.96%. This model is of great significance.
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Cai, Hongyun, Jie Meng, Jichao Ren, and Shilin Yuan. "Toward Sequential Recommendation Model for Long-Term Interest Memory and Nearest Neighbor Influence." Wireless Communications and Mobile Computing 2022 (September 27, 2022): 1–15. http://dx.doi.org/10.1155/2022/4612169.
Повний текст джерелаАнотація:
Sequential recommendation can make predictions by fitting users’ changing interests based on the users’ continuous historical behavior sequences. Currently, many existing sequential recommendation methods put more emphasis upon users’ recent preference (i.e., short-term interests), but simplify or even ignore the influence of users’ long-term interests, resulting in important interest features of users not being effectively mined. Moreover, users’ real intentions may not be fully captured by only focusing on their behavior histories, because users’ interests are diverse and dynamic. To solve the above problems, we propose a novel sequential recommendation model for long-term interest memory and nearest neighbor influence. Firstly, item embeddings based on item similarity and dependency are constructed to alleviate the problem of data sparsity in users’ recent interest history. Secondly, in order to effectively capture long-term interests, the long sequence is divided into multiple nonoverlapping subsequences. For these subsequences, the graph attention network with node importance factor is designed to fully extract the main interests of subsequences, and LSTM is introduced to learn the dynamic changes of interest among subsequences. Long-term interests of users are modeled through complex structure within subsequences and sequential dependencies among subsequences. Finally, the user’s neighbor representation is introduced, and a gating module is designed to integrate the user’s neighbor information and self-interests. The influence of users’ short-term and long-term interests on prediction is dynamically controlled by considering nearby features in the gating network. The experimental results on two public datasets show that the proposed sequential recommendation model can outperform the baseline methods in hit rate (HR@K) and normalized discounted cumulative gain (NDCG@K).
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Zhang, Qiaosheng, Vincent Y. F. Tan, and Changho Suh. "Community Detection and Matrix Completion With Social and Item Similarity Graphs." IEEE Transactions on Signal Processing 69 (2021): 917–31. http://dx.doi.org/10.1109/tsp.2021.3052033.
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Han, Lifeng, Li Chen, and Xiaolong Shi. "Recommendation Model Based on Probabilistic Matrix Factorization and Rated Item Relevance." Electronics 11, no. 24 (December 13, 2022): 4160. http://dx.doi.org/10.3390/electronics11244160.
Повний текст джерелаАнотація:
Personalized recommendation has become indispensable in today’s information society. Personalized recommendations play a significant role for both information producers and consumers. Studies have shown that probability matrix factorization can improve personalized recommendation performance. However, most probability matrix factorization models ignore the effect of item-implicit association and user-implicit similarity on recommendation performance. To overcome this lack, we propose a recommendation model based on probability matrix factorization that considers the correlation of user rating items. Our model uses the resource allocation of the bipartite graphs and the random walk of meta-paths in heterogeneous networks to determine the implicit association of items and the implicit similarity of users, respectively. Thus, the final item association and user similarity are obtained. The final item and user similarity relationships are integrated into the probability matrix factorization model to obtain the user’s prediction score for a specific project. Finally, we validated the model on the Delicious-2k, Movielens-2k and last.fm-2k datasets. The results show that our proposed algorithm model has higher recommendation accuracy than other recommendation algorithms.
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Grishin, John, and Douglas J. Gillan. "Structure Matters: Effects of Semantic Relatedness and Proximity on Consumer Search and Integration Tasks." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 1088–92. http://dx.doi.org/10.1177/1541931213601251.
Повний текст джерелаАнотація:
Information displays should be clear and easily understood. This research examined whether principles developed by Kosslyn (1989) and Carswell and Wickens (1987) for charts, graphs, and object displays could be extended, or adapted, to another type of display, the food item package. We hypothesized that a food package on which label items had been arranged according to their similarity, or semantic relatedness, would facilitate better user performance than a package on which label items had been arranged in other ways. Participants rated the semantic relatedness of 12 label items found on a common food item package. Using multi-dimensional scaling (MDS) outputs from the ratings, we created three versions of a consumer cough drop package: 1) Similarity version—label elements that received higher similarity ratings were depicted closer together than elements with lower similarity ratings, 2) Dissimilarity version—elements that received higher similarity ratings were depicted farther apart than elements with lower similarity ratings, 3) Random version—rating values were randomly assigned to the pairs of elements. We tested user performance on search tasks and integrative tasks on each of the three versions. We hypothesized that the Similarity version would produce the best user performance and the Dissimilarity version would produce the worst. Results only partially supported the hypotheses. On the search tasks, the best performance was achieved on the Similarity and Dissimilarity versions, and the worst on the Random version. On the integrative tasks, the version made no difference in performance. Possible reasons for these results are discussed. Similar results by Fitts and Deininger (1954) and Morin and Grant (1955) suggest that performance on tasks are superior when the relationships are in an ordered structure, rather than randomly assigned, possibly because ordered structures make possible the development of search strategies, whereas random arrangements do not.
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Jing, Mengyuan, Yanmin Zhu, Yanan Xu, Haobing Liu, Tianzi Zang, Chunyang Wang, and Jiadi Yu. "Learning Shared Representations for Recommendation with Dynamic Heterogeneous Graph Convolutional Networks." ACM Transactions on Knowledge Discovery from Data, October 10, 2022. http://dx.doi.org/10.1145/3565575.
Повний текст джерелаАнотація:
Graph Convolutional Networks (GCNs) have been widely used for collaborative filtering, due to their effectiveness in exploiting high-order collaborative signals. However, two issues have not been well addressed by existing studies. First, usually only one kind of information is utilized, i.e., user preference in user-item graphs or item dependency in item-item graphs. Second, they usually adopt static graphs, which cannot retain the temporal evolution of the information. These can limit the recommendation quality. To address these limitations, we propose to mine three kinds of information (user preference, item dependency, and user behavior similarity) and their temporal evolution by constructing multiple discrete dynamic heterogeneous graphs (i.e., a user-item dynamic graph, an item-item dynamic graph, and a user-subseq dynamic graph) from interaction data. A novel network (PDGCN) is proposed to learn the representations of users and items in these dynamic graphs. Moreover, we design a structural neighbor aggregation module with novel pooling and convolution operations to aggregate the features of structural neighbors. We also design a temporal neighbor aggregation module based on self-attention mechanism to aggregate the features of temporal neighbors. We conduct extensive experiments on four real-world datasets. The results indicate that our approach outperforms several competing methods in terms of Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG). Dynamic graphs are also shown to be effective in improving recommendation performance.
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Khan, Nasrullah, Zongmin Ma, Aman Ullah, and Kemal Polat. "Similarity Attributed Knowledge Graph Embedding Enhancement for Item Recommendation." Information Sciences, September 2022. http://dx.doi.org/10.1016/j.ins.2022.08.124.
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ÖZCAN, Alper. "Applying Graph Convolution Networks to Recommender Systems based on graph topology." DÜMF Mühendislik Dergisi, June 28, 2022. http://dx.doi.org/10.24012/dumf.1081137.
Повний текст джерелаАнотація:
The recommender systems are widely used in online applications to suggest products to the potential users. The main aim of recommender system is to produce meaningful recommendation to a potential user by monitoring user’s purchasing habits, history, and useful information. Recently, graph representation learning methods based on node embedding have drawn attention in Recommender systems such as Graph Convolutional Networks (GCNs) that is powerful method for collaborative filtering. The GCN performs neighborhood aggregation mechanism to extract high level representation for both user and items. In this paper, we propose a recommendation algorithm based on node similarity convolutional matrices with topological property in GCNs where the linkage measure is illustrated as a bipartite graph. The experiments indicate the necessity of capturing user–item graph structure in recommendation. The experimental results show that node similarity-based convolution matrices and GCN-based embeddings significantly improve the prediction accuracy in recommender systems compared to state-of-art approaches.
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Lei, Fei, Zhongqi Cao, Yuning Yang, Yibo Ding, and Cong Zhang. "Learning the User’s Deeper Preferences for Multi-modal Recommendation Systems." ACM Transactions on Multimedia Computing, Communications, and Applications, December 7, 2022. http://dx.doi.org/10.1145/3573010.
Повний текст джерелаАнотація:
Recommendation system plays an important role in the rapid development of micro-video sharing platform. Micro-video has rich modal features, such as visual, audio and text. It is of great significance to carry out personalized recommendation by integrating multi-modal features. However, most of the current multi-modal recommendation systems can only enrich the feature representation on the item side,while leads to poor learning of user preferences. To solve this problem, we propose a novel module named Learning the User’s Deeper Preferences(LUDP), which constructs the item-item modal similarity graph and user preference graph in each modality to explore the learning of item and user representation. Specifically, we construct item-item similar modalities graph using multi-modal features, the item ID embedding is propagated and aggregated on the graph to learn the latent structural information of items; The user preference graph is constructed through the historical interaction between the user and item, on which the multi-modal features are aggregated as the user’s preference for the modal. Finally, combining the two parts as auxiliary information enhances the user and item representation learned from the collaborative signals to learn deeper user preferences. Through a large number of experiments on two public datasets (TikTok, Movielens), our model is proved to be superior to the most advanced multi-modal recommendation methods.
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Khan, Bilal, Jia Wu, Jian Yang, and Xiaoxiao Ma. "Heterogeneous Hypergraph Neural Network for Social Recommendation using Attention Network." ACM Transactions on Recommender Systems, August 7, 2023. http://dx.doi.org/10.1145/3613964.
Повний текст джерелаАнотація:
Graph neural networks (GNNs) have been used extensively as a backbone for social recommendation. However, their application to a diverse range of situations is still rather limited. This is because graph structures only leverage pairwise user relationships. They cannot capture the higher-order relationships so common in the real world, and ignoring the interest friends and strangers might have in similar items is severely hampering the expressiveness of the current graph-based recommendation models. Hence, in this paper, we outline a heterogeneous hypergraph neural network for social recommendation called HHGSA that incorporates an attention network to address these issues. The hypergraph is able to represent higher-order relationships through five motifs: friend and stranger item appeal, item similarity, user similarity based on interactions with items, and social relations. Two modules, the attentive vertex aggregation module, and the attentive hyperedge aggregation module, capture user and item attention. In addition, it has been discovered that similar items have identical appeal when displayed to users. A GNN aggregates the user embedding data, including information about the friend and stranger and item embeddings. Finally, information about users and items is aggregated for social recommendations. Extensive experiments on four datasets demonstrate that the HHGSA model outperforms a wide range of baselines and can significantly improve the accuracy of recommendations.
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Xu, Haifeng, Rasha F. Kashef, Hans De Sterck, and Geoffrey Sanders. "Efficient Algebraic Multigrid Methods for Multilevel Overlapping Coclustering of User-Item Relationships." INFORMS Journal on Computing, January 31, 2022. http://dx.doi.org/10.1287/ijoc.2021.1137.
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Various digital data sets that encode user-item relationships contain a multilevel overlapping cluster structure. The user-item relation can be encoded in a weighted bipartite graph and uncovering these overlapping coclusters of users and items at multiple levels in the bipartite graph can play an important role in analyzing user-item data in many applications. For example, for effective online marketing, such as placing online ads or deploying smart online marketing strategies, identifying co-occurring clusters of users and items can lead to accurately targeted advertisements and better marketing outcomes. In this paper, we propose fast algorithms inspired by algebraic multigrid methods for finding multilevel overlapping cocluster structures of feature matrices that encode user-item relations. Starting from the weighted bipartite graph structure of the feature matrix, the algorithms use agglomeration procedures to recursively coarsen the bipartite graphs that represent the relations between the coclusters on increasingly coarser levels. New fast coarsening routines are described that circumvent the bottleneck of all-to-all similarity computations by exploiting measures of direct connection strength between row and column variables in the feature matrix. Providing accurate coclusters at multiple levels in a manner that can scale to large data sets is a challenging task. In this paper, we propose heuristic algorithms that approximately and recursively minimize normalized cuts to obtain coclusters in the aggregated bipartite graphs on multiple levels of resolution. Whereas the main novelty and focus of the paper lies in algorithmic aspects of reducing computational complexity to obtain scalable methods specifically for large rectangular user-item matrices, the algorithmic variants also define several new models for determining multilevel coclusters that we justify intuitively by relating them to principles that underlie collaborative filtering methods for user-item relationships. Experimental results show that the proposed algorithms successfully uncover the multilevel overlapping cluster structure for artificial and real data sets. Summary of Contribution: This paper develops new and efficient computational methods for finding the multilevel overlapping cocluster structure of feature matrices that encode user-item relationships. We base our approach on the use of pairwise similarity measures between features, seeking clusters of points that are similar to each other and dissimilar from the points outside the cluster. We approximately solve the problem of finding optimal overlapping coclusters on multiple levels by employing a framework that is based on efficient multilevel methods that have been used previously to solve sparse linear systems and to cluster graphs. Our main contribution is that we extend these methods in efficient manners to find coclusters in the bipartite graphs that encode common and important user-item relationships or social network relations. The novel methods that we propose are inherently scalable to large problem sizes and are naturally able to uncover overlapping coclusters at multiple levels, whereas existing methods generally only find coclusters at the fine level. We illustrate the algorithm and its performance on some standard test problems from the literature and on a proof-of-concept real-world data set that relates LinkedIn users to their skills and expertise.
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Zhang, Yiwen, Li Zhang, Yunchun Dong, Jun Chu, Xing Wang, and Zuobin Ying. "A movie recommendation method based on knowledge graph and time series." Journal of Intelligent & Fuzzy Systems, July 3, 2023, 1–10. http://dx.doi.org/10.3233/jifs-230795.
Повний текст джерелаАнотація:
Traditional collaborative filtering algorithms use user history rating information to predict movie ratings Other information, such as plot and director, which could provide potential connections are not fully mined. To address this issue, a collaborative filtering recommendation algorithm named a movie recommendation method based on knowledge graph and time series is proposed, in which the knowledge graph and time series features are effectively integrated. Firstly, the knowledge graph gains a deep relationship between users and movies. Secondly, the time series could extract user features and then calculates user similarity. Finally, collaborative filtering of ratings can calculate the user similarity and predicts ratings more precisely by utilizing the first two phases’ outcomes. The experiment results show that the A Movie Recommendation Method Fusing Knowledge Graph and Time Series can reduce the MAE and RMSE of user-based collaborative filtering and Item-based collaborative filtering by 0.06,0.1 and 0.07,0.09 respectively, and also enhance the interpretability of the model.
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Di Sipio, Claudio, Juri Di Rocco, Davide Di Ruscio, and Phuong T. Nguyen. "MORGAN: a modeling recommender system based on graph kernel." Software and Systems Modeling, April 4, 2023. http://dx.doi.org/10.1007/s10270-023-01102-8.
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AbstractModel-driven engineering (MDE) is an effective means of synchronizing among stakeholders, thereby being a crucial part of the software development life cycle. In recent years, MDE has been on the rise, triggering the need for automatic modeling assistants to support metamodelers during their daily activities. Among others, it is crucial to enable model designers to choose suitable components while working on new (meta)models. In our previous work, we proposed MORGAN, a graph kernel-based recommender system to assist developers in completing models and metamodels. To provide input for the recommendation engine, we convert training data into a graph-based format, making use of various natural language processing (NLP) techniques. The extracted graphs are then fed as input for a recommendation engine based on graph kernel similarity, which performs predictions to provide modelers with relevant recommendations to complete the partially specified (meta)models. In this paper, we extend the proposed tool in different dimensions, resulting in a more advanced recommender system. Firstly, we equip it with the ability to support recommendations for JSON schema that provides a model representation of data handling operations. Secondly, we introduce additional preprocessing steps and a kernel similarity function based on item frequency, aiming to enhance the capabilities, providing more precise recommendations. Thirdly, we study the proposed enhancements, conducting a well-structured evaluation by considering three real-world datasets. Although the increasing size of the training data negatively affects the computation time, the experimental results demonstrate that the newly introduced mechanisms allow MORGAN to improve its recommendations compared to its preceding version.
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Murty, Chakka S. V. V. S. N., G. P. Saradhi Varma, and Ch Satyanarayana. "Content-Based Collaborative Filtering with Hierarchical Agglomerative Clustering Using User/Item based Ratings." Journal of Interconnection Networks, February 4, 2022. http://dx.doi.org/10.1142/s0219265921410267.
Повний текст джерелаАнотація:
The recommender system (RS) plays the major role in online networks, online shopping, and online services etc. The conventional RSs are suffering with the inaccurate quality of experience to the users, so the improper content is recommending to customers. The content based collaborative filtering (CBCF) method is introduced to solve the issues presented in the RSs. However, the CBCF method is suffering with the cold start problem for new users and suffering with data accuracy, data sparsity, and scalable data in clustering process. Thus, to solve these problems, this article proposes hierarchical agglomerative clustering (HAC) based collaborative filtering (HAC-CF) for RSs. The proposed HAC-CF based RS functions by utilizing the incentivized/penalized user (IPU) model with user-based and item-based ratings. To this end, users are divided into several clusters through single link graph partitioning through minimum distance criteria. Then, the final item ranking is computed using Pearson correlation coefficient (PCC) similarity of users. Hence, recommendation efficiency and accuracy are increased at the end user by combining user, item models. The simulation results show the performance enhancement of proposed method with respect to F1-score, recall, and precision as compared to the conventional approaches.
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Morita, Ayako, Yoshimitsu Takahashi, Kunihiko Takahashi, and Takeo Fujiwara. "Depressive symptoms homophily among community-dwelling older adults in japan: A social networks analysis." Frontiers in Public Health 10 (September 20, 2022). http://dx.doi.org/10.3389/fpubh.2022.965026.
Повний текст джерелаАнотація:
Late-life depression is one of the most common mental illnesses that cause serious consequences, but the majority do not reach out for mental health services and relapses are common. The present study investigated profiled similarity of older adults' social networks in terms of depressive symptoms. In 2017, we distributed questionnaires inquiring about confidants in the community, depressive symptoms based on the 15-item Geriatric Depression Scale (GDS-15), and demographic and functional characteristics to all the community-dwelling older adults under the national insurance system in Wakuya City (Miyagi prefecture, Japan). Applying the Exponential Random Graph Model, we estimated the likelihood of a confidant relational tie by the similarity of overall and specific depressive symptoms within 217,470 potential ties among 660 respondents eligible for analysis. The overall depressive symptom homophily was marginally significant (p < 0.10), indicating that the likelihood of a confidant relational tie between two community-dwelling older adults was decreased by 5%, with one point increase in their difference in the total number of depressive symptoms (odds ratio [OR], 0.95; 95% confidence interval [CI], 0.90–1). Focusing on specific domains of depressive symptoms, we found significant apathy homophily (p < 0.05) but no significant suicidal ideation of homophily. The results indicated that there is a 19% decrease in the likelihood of a confidant relational tie between two community-dwelling older adults by one point increase in their difference in the total number of apathy symptoms (OR, 0.81; 95%CI, 0.67–0.98) but no change by increasing the difference in their total number of suicidal ideation symptoms (OR, 1; 95%CI, 0.87–1.14). These findings suggest depressive symptom homophily, particularly with respect to apathy domains, in confidant social networks of community-dwelling older adults, and the importance of network intervention in preventing late-life depression.
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Zhang, Qiaosheng, Geewon Suh, Changho Suh, and Vincent Y. F. Tan. "MC2G: An Efficient Algorithm for Matrix Completion with Social and Item Similarity Graphs." IEEE Transactions on Signal Processing, 2022, 1. http://dx.doi.org/10.1109/tsp.2022.3174423.
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Szádoczki, Zsombor, Sándor Bozóki, Patrik Juhász, Sergii V. Kadenko, and Vitaliy Tsyganok. "Incomplete pairwise comparison matrices based on graphs with average degree approximately 3." Annals of Operations Research, June 21, 2022. http://dx.doi.org/10.1007/s10479-022-04819-9.
Повний текст джерелаАнотація:
AbstractA crucial, both from theoretical and practical points of view, problem in preference modelling is the number of questions to ask from the decision maker. We focus on incomplete pairwise comparison matrices based on graphs whose average degree is approximately 3 (or a bit more), i.e., each item is compared to three others in average. In the range of matrix sizes we considered, $$n=5,6,7,8,9,10$$ n = 5 , 6 , 7 , 8 , 9 , 10 , this requires from 1.4n to 1.8n edges, resulting in completion ratios between 33% ($$n=10$$ n = 10 ) and 80% ($$n=5$$ n = 5 ). We analyze several types of union of two spanning trees (three of them building on additional ordinal information on the ranking), 2-edge-connected random graphs and 3-(quasi-)regular graphs with minimal diameter (the length of the maximal shortest path between any two vertices). The weight vectors are calculated from the natural extensions, to the incomplete case, of the two most popular weighting methods, the eigenvector method and the logarithmic least squares. These weight vectors are compared to the ones calculated from the complete matrix, and their distances (Euclidean, Chebyshev and Manhattan), rank correlations (Kendall and Spearman) and similarity (Garuti, cosine and dice indices) are computed in order to have cardinal, ordinal and proximity views during the comparisons. Surprisingly enough, only the union of two star graphs centered at the best and the second best items perform well among the graphs using additional ordinal information on the ranking. The union of two edge-disjoint spanning trees is almost always the best among the analyzed graphs.