scholarly journals Keywords-Driven and Weight-aware Paper Recommendation via Paper Correlation Pattern Mining

2021 ◽  
Author(s):  
Hanwen Liu
Keyword(s):  
Recommender Systems ◽  
Link Prediction ◽  
Pattern Mining ◽  
Correlation Pattern ◽  
Relation Graph ◽  
Prediction Approach ◽  
Tools And Methods

Abstract Nowadays, recommender systems have become one of the main tools and methods for users to search for their interested papers from massive candidates. Considering the above drawbacks, in this paper, we propose a link prediction approach that combines time, keywords and authors information for constructing a new relation graph. Finally, a case study is employed to explain our approach step by step and demonstrate the feasibility of our proposal.

scholarly journals Keywords-Driven and Weight-aware Paper Recommendation via Paper Correlation Pattern Mining

2021 ◽  
Author(s):  
Hanwen Liu ◽  
Jun Hou ◽  
Qianmu Li ◽  
Jian Jiang
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Pattern Mining ◽  
Academic Research ◽  
Correlation Pattern ◽  
Keyword Query ◽  
Correlation Graph ◽  
Paper Citation ◽  
Citation Graph

Abstract Currently, readers often prefer to search for their interested papers based on a set of typed query keywords. As the keywords of a paper is often limited, paper recommender systems often need to recommend a set of papers which collectively satisfy the readers’ keyword query. However, the topics of recommended papers are probably not correlated with each other, which fail to meet the readers’ requirements on in-depth and continuous academic research. Furthermore, although existing paper citation graphs can model the papers’ correlations, they often face the data sparse problem which blocks accurate paper recommendations. To address these issues, we propose a keywords-driven and weight-aware paper recommendation approach, named LP-PRk+w (link prediction-paper recommendation), based on a weighted paper correlation graph. Concretely, we firstly optimize the existing paper citation graph modes by introducing a weighted similarity, after which we obtain a weighted paper correlation graph. Then we recommend a set of correlated papers based on the weighted paper correlation graph and the query keywords from readers. At last, we conduct large-scale experiments on a real-world Hep-Th dataset. Experimental results demonstrate that our proposal can improve the paper recommendation performances considerably, compared to other related solutions.

scholarly journals Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1478
Author(s):  
Penugonda Ravikumar ◽  
Palla Likhitha ◽  
Bathala Venus Vikranth Raj ◽  
Rage Uday Kiran ◽  
Yutaka Watanobe ◽  
...  
Keyword(s):  
Real World ◽  
Pattern Mining ◽  
Transportation Network ◽  
Frequent Pattern Mining ◽  
Temporal Databases ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Temporal Database ◽  
First Case

Discovering periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Though several algorithms were described in the literature to tackle the problem of periodic-frequent pattern mining, most of these algorithms use the traditional horizontal (or row) database layout, that is, either they need to scan the database several times or do not allow asynchronous computation of periodic-frequent patterns. As a result, this kind of database layout makes the algorithms for discovering periodic-frequent patterns both time and memory inefficient. One cannot ignore the importance of mining the data stored in a vertical (or columnar) database layout. It is because real-world big data is widely stored in columnar database layout. With this motivation, this paper proposes an efficient algorithm, Periodic Frequent-Equivalence CLass Transformation (PF-ECLAT), to find periodic-frequent patterns in a columnar temporal database. Experimental results on sparse and dense real-world and synthetic databases demonstrate that PF-ECLAT is memory and runtime efficient and highly scalable. Finally, we demonstrate the usefulness of PF-ECLAT with two case studies. In the first case study, we have employed our algorithm to identify the geographical areas in which people were periodically exposed to harmful levels of air pollution in Japan. In the second case study, we have utilized our algorithm to discover the set of road segments in which congestion was regularly observed in a transportation network.

Research on Ship Classification Based on Trajectory Features

2017 ◽  
Vol 71 (1) ◽  
pp. 100-116 ◽  
Author(s):  
Kai Sheng ◽  
Zhong Liu ◽  
Dechao Zhou ◽  
Ailin He ◽  
Chengxu Feng
Keyword(s):  
Pattern Mining ◽  
Extraction Methods ◽  
Behaviour Pattern ◽  
Automatic Identification ◽  
Identification System ◽  
Trajectory Data ◽  
Trajectory Features ◽  
Ship Classification ◽  
Historical Trajectory

It is important for maritime authorities to effectively classify and identify unknown types of ships in historical trajectory data. This paper uses a logistic regression model to construct a ship classifier by utilising the features extracted from ship trajectories. First of all, three basic movement patterns are proposed according to ship sailing characteristics, with related sub-trajectory partitioning algorithms. Subsequently, three categories of trajectory features with their extraction methods are presented. Finally, a case study on building a model for classifying fishing boats and cargo ships based on real Automatic Identification System (AIS) data is given. Experimental results indicate that the proposed classification method can meet the needs of recognising uncertain types of targets in historical trajectory data, laying a foundation for further research on camouflaged ship identification, behaviour pattern mining, outlier behaviour detection and other applications.

scholarly journals Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan

2018 ◽  
Author(s):  
Xinning Zhu ◽  
Tianyue Sun ◽  
Hao Yuan ◽  
Zheng Hu ◽  
Jiansong Miao
Keyword(s):  
Pattern Mining ◽  
Historical Data ◽  
Movement Pattern ◽  
Phone Call ◽  
Specific Group ◽  
Group Movement ◽  
Trajectory Similarity ◽  
Group Members ◽  
Call Detail Records

Identifying group movement patterns of crowds and understanding group behaviors is valuable for urban planners, especially when the groups are special such as tourist groups. In this paper, we present a framework to discover tourist groups and investigate the tourist behaviors using mobile phone call detail records (CDRs). Unlike GPS data, CDRs are relatively poor in spatial resolution with low sampling rates, which makes it a big challenge to identify group members from thousands of tourists. Moreover, since touristic trips are not on a regular basis, no historical data of the specific group can be used to reduce the uncertainty of trajectories. To address such challenges, we propose a method called group movement pattern mining based on similarity (GMPMS) to discover tourist groups. To avoid large amounts of trajectory similarity measurements, snapshots of the trajectories are firstly generated to extract candidate groups containing co-occurring tourists. Then, considering that different groups may follow the same itineraries, additional traveling behavioral features are defined to identify the group members. Finally, with Hainan province as an example, we provide a number of interesting insights of travel behaviors of group tours as well as individual tours, which will be helpful for tourism planning and management.

Masculinity, Industrial Education and Fascism in Egypt

2021 ◽  
Vol 34 (1-2) ◽  
pp. 125-157
Author(s):  
Annalaura Turiano
Keyword(s):  
Technical Expertise ◽  
Major Site ◽  
Industrial Education ◽  
Teaching Tools ◽  
Industrial Schools ◽  
Tools And Methods

Abstract Starting from the premise that school is a ‘major site of masculinity formation’ (Connell, 1996), this article studies the construction of boyhood in the industrial schools of the Salesian mission in Egypt between 1900 and 1939. Analysis of the teaching tools and methods, and of the processes of conformity used in these schools run by Italian missionaries, reveals that they became forums for confrontation and negotiation between different models of masculinity. This article uses the case study of the Salesian schools to shed new light on the interconnections between missionary programmes, fascist imperialism and schooling strategies of the pupils and their families. Ultimately, it seeks to demonstrate how missionary schools contributed to the gendered construction of technical expertise and industrial education in Egypt.

Synonymy Expansion Using Link Prediction Methods: A Case Study of Assamese WordNet

2022 ◽  
Vol 21 (1) ◽  
pp. 1-21
Author(s):  
Bornali Phukon ◽  
Akash Anil ◽  
Sanasam Ranbir Singh ◽  
Priyankoo Sarmah
Keyword(s):  
Network Analysis ◽  
Target Word ◽  
Complex Network ◽  
Link Prediction ◽  
Expansion Method ◽  
Prediction Methods ◽  
Network Embedding ◽  
Per Se ◽  
Local Proximity

WordNets built for low-resource languages, such as Assamese, often use the expansion methodology. This may result in missing lexical entries and missing synonymy relations. As the Assamese WordNet is also built using the expansion method, using the Hindi WordNet, it also has missing synonymy relations. As WordNets can be visualized as a network of unique words connected by synonymy relations, link prediction in complex network analysis is an effective way of predicting missing relations in a network. Hence, to predict the missing synonyms in the Assamese WordNet, link prediction methods were used in the current work that proved effective. It is also observed that for discovering missing relations in the Assamese WordNet, simple local proximity-based methods might be more effective as compared to global and complex supervised models using network embedding. Further, it is noticed that though a set of retrieved words are not synonyms per se, they are semantically related to the target word and may be categorized as semantic cohorts.

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