Content-Based Filtering Recommendation in Abstract Search Using Neo4j

Wita, Ratsameetip; Bubphachuen, Kawinwit; Chawachat, Jakarin

doi:10.1109/icsec.2017.8443957

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…Graph database merupakan keterhubungan antar data yang optimal dan membentuk suatu karakter spesifik. Graph database dimodelkan dengan nodes dan property menghubungkan antar data menggunakan edges [9]. Database Neo4j adalah sebuah database grafik open source berbasis java dengan kinerja yang tinggi [10].…”

Section: Graph Database Neo4junclassified

Pemodelan Pengetahuan Graph Database Untuk Jejaring Penelitian Kesehatan di Indonesia

Sahria

Fudholi

2020

mib

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Health research is research approved in the medical field. Health research in Indonesia is increasingly being approved by researchers in Indonesia, therefore research is stored in repositories. Many health research journal repositories are available, but there is minimal research for analysis and modeling in the health research network in Indonesia. This research proposes a knowledge modeling with Neo4j graphic database to implement it. In this study, data obtained from the SINTA Journal with web scraping techniques. The purpose of this research is to produce network knowledge using CQL (Chyper Query Language) which is useful in the medical field. The results of this study are expected to be useful for the government, academics, researchers, or the public for health research network researchers in Indonesia. The results of research testing modeling of health research network knowledge using a graph database of 95.2% said very good

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Section: Graph Database Neo4junclassified

Pemodelan Pengetahuan Graph Database Untuk Jejaring Penelitian Kesehatan di Indonesia

Sahria

Fudholi

2020

mib

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“…In [6] the authors use a graph database for the ontology transformation and then, using rule-based recommendations and RFM (recency, frequency, monetary) analysis for customer behavioral knowledge, make personal goods recommendation lists. A content-based filtering recommendation approach using Neo4j [4] proposes a search of article abstracts based on document-keyword relation. The graph was used to filter keyword-document co-occurrence as a similarity weight between documents in order to reduce searching space and recommend relative documents our graph-based method differs from all these previous methods in the fact that we use session data to recommend items for the next session step based on simple co-οccurrence statistics.…”

Section: Introductionmentioning

confidence: 99%

A Graph-based Method for Session-based Recommendations

Delianidi

Salampasis

Diamantaras

et al. 2020

24th Pan-Hellenic Conference on Informatics

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We present a graph-based approach for the data management tasks and the efficient operation of a system for sessionbased next-item recommendations. The proposed method can collect data continuously and incrementally from an ecommerce web site, thus seemingly prepare the necessary data infrastructure for the recommendation algorithm to operate without any excessive training phase. Our work aims at developing a recommender method that represents a balance between data processing and management efficiency requirements and the effectiveness of the recommendations produced. We use the Neo4j graph database to implement a prototype of such a system. Furthermore, we use an industry dataset corresponding to a typical e-commerce session-based scenario, and we report on experiments using our graph-based approach and other state-of-the-art machine learning and deep learning methods.

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Integrating Graph-Based Document Recommendation in Digital Libraries of Theses Collection

Panyadee

Wita

2021

Lecture Notes in Networks and Systems

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Content-Based Filtering Recommendation in Abstract Search Using Neo4j

Cited by 5 publications

References 5 publications

Pemodelan Pengetahuan Graph Database Untuk Jejaring Penelitian Kesehatan di Indonesia

Pemodelan Pengetahuan Graph Database Untuk Jejaring Penelitian Kesehatan di Indonesia

A Graph-based Method for Session-based Recommendations

Integrating Graph-Based Document Recommendation in Digital Libraries of Theses Collection

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