Generating Groups of Products Using Graph Mining Techniques

Ríos, Sebastián A.; Videla-Cavieres, Ivan F.

doi:10.1016/j.procs.2014.08.155

Cited by 12 publications

(5 citation statements)

References 16 publications

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“…Many authors have highlighted and raised these approaches in their work. 70,[127][128][129][130][131][132][133][134][135] This is categorized as:…”

Section: Community Filtering Algorithms and Methodsmentioning

confidence: 99%

“…Community‐based recommender systems are responsible for showing the relations and behavior between users. Many authors have highlighted and raised these approaches in their work 70,127‐135 . This is categorized as: Similarity‐based : This approach adopted weights to release predictions, as some studies highlighted 14 …”

Section: Investigation and Analysis Questions With Classificationmentioning

confidence: 99%

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Towards the significance of taxi recommender systems in smart cities

Katarya

2022

Concurrency and Computation

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Since their launch in the early 1990's, recommender systems (RSs) have played an essential role in information filtering and providing personalized information to users by utilizing the web, mobile, context, personalized, and practical applications. Many research articles have been published in various application domains such as movies, books, documents, news, images, music, shopping, TV programs, tourism, group, social, taxi, and others. However, from these domains, taxi recommendation is a new and thrust area of research, as publications in this domain are few and recently published.Taxi recommendations are associated with drivers-passengers with their: behavior, hotspots, trajectory, point of interest, route planning or prediction, taxi finding, anomaly or fraud detection, urban computing, mobility patterns and so forth. Due to this diversity and various agendas in taxi recommendations, more exploration is required, as it is a less mature and growing field than other domains. So we have done a literature review in the taxi domain by classification, incorporating reputed articles published from 2001 to August 2022. We include the reputed papers which highlight, analyze and perform studies only on the taxi domain. This article categorizes and discusses the research and development in taxi recommendations as nature, approaches, algorithms, methods, datasets, models, devices, information, and evaluation techniques. This survey article will actively help the researchers and professionals understand the current situations in taxi RS and resolve future trends, opportunities, and research.

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“…Many authors have highlighted and raised these approaches in their work. 70,[127][128][129][130][131][132][133][134][135] This is categorized as:…”

Section: Community Filtering Algorithms and Methodsmentioning

confidence: 99%

Section: Investigation and Analysis Questions With Classificationmentioning

confidence: 99%

Towards the significance of taxi recommender systems in smart cities

Katarya

2022

Concurrency and Computation

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“…Graph models have been applied successfully for data management and real-time recommendations by e-commerce companies, such as eBay and Walmart [11,36]. Ríos et al [28] represented co-purchased products in different transactions of e-commerce data in the form of a graph; they applied community detection techniques to find the clusters of similar products. Ranganath [27] utilised a graph model for representing query attributes which helped improve the ranking of relevant items for e-commerce queries.…”

Section: Schemaless Data Models In E-commercementioning

confidence: 99%

A Unification of Heterogeneous Data Sources into a Graph Model in E-commerce

Tuteja

Kumar

2021

Data Sci. Eng.

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The incorporation of heterogeneous data models into large-scale e-commerce applications incurs various complexities and overheads, such as redundancy of data, maintenance of different data models, and communication among different models for query processing. Graphs have emerged as data modelling techniques for large-scale applications with heterogeneous, schemaless, and relationship-centric data. Models exist for mapping different types of data to a graph; however, the unification of data from heterogeneous source models into a graph model has not received much attention. To address this, we propose a new framework in this study. The proposed framework first transforms data from various source models into graph models individually and then unifies them into a single graph. To justify the applicability of the proposed framework in e-commerce applications, we analyse and compare query performance, scalability, and database size of the unified graph with heterogeneous source data models for a predefined set of queries. We also access some qualitative measures, such as flexibility, completeness, consistency, and maturity for the proposed unified graph. Based on the experimental results, the unified graph outperforms heterogeneous source models for query performance and scalability; however, it falls behind for database size.

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“…ALGORITHM 1: Constructing the benchmarks that accurately approximate a real-life network. 4 Complexity possible. Structural characteristics of the benchmarks generated by all the methods on each real-life network are summarized in Tables 1-4, respectively.…”

Section: Mesoscale Structure Characteristic Calculationmentioning

confidence: 99%

“…Community detection provides an effective tool to gain insights into the nontrivial internal organization of networks, allowing us to unearth in-depth network information that may not be obtained from direct observation [1][2][3], e.g., functional and dynamic characteristics [2,3]. In recent years, we have experienced an increasing demand for detecting communities from a wide range of science domains, such as online targeted advertising [4], social crisis response [5,6], disease prevention, and medical diagnosis [7].…”

Section: Introductionmentioning

confidence: 99%

Constructing Real-Life Benchmarks for Community Detection by Rewiring Edges

Xiao

Ren

2020

Complexity

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In order to make the performance evaluation of community detection algorithms more accurate and deepen our analysis of community structures and functional characteristics of real-life networks, a new benchmark constructing method is designed from the perspective of directly rewiring edges in a real-life network instead of building a model. Based on the method, two kinds of novel benchmarks with special functions are proposed. The first kind can accurately approximate the microscale and mesoscale structural characteristics of the original network, providing ideal proxies for real-life networks and helping to realize performance analysis of community detection algorithms when a real network varies characteristics at multiple scales. The second kind is able to independently vary the community intensity in each generated benchmark and make the robustness evaluation of community detection algorithms more accurate. Experimental results prove the effectiveness and superiority of our proposed method. It enables more real-life networks to be used to construct benchmarks and helps to deepen our analysis of community structures and functional characteristics of real-life networks.

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Generating Groups of Products Using Graph Mining Techniques

Cited by 12 publications

References 16 publications

Towards the significance of taxi recommender systems in smart cities

Towards the significance of taxi recommender systems in smart cities

A Unification of Heterogeneous Data Sources into a Graph Model in E-commerce

Constructing Real-Life Benchmarks for Community Detection by Rewiring Edges

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