Improving Recommendations for Online Retail Markets Based on Ontology Evolution

Alaa, Rana; Gawish, Mariam; Fernández‐Veiga, Manuel

doi:10.3390/electronics10141650

Cited by 9 publications

(17 citation statements)

References 34 publications

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“…Instead, the idea of combining different methodologies and techniques is supported by practitioners in the field of ontologies [11]. However, the general phases involved in the ontology construction methodology proposed by [12], as shown in Figure 1, can be used as a guideline to construct the ontology. The phases include ontology specification, ontology conceptualization, ontology implementation, ontology validation, and evaluation.…”

Section: Introductionmentioning

confidence: 99%

“…The phases include ontology specification, ontology conceptualization, ontology implementation, ontology validation, and evaluation. In the ontology specification phase, the purpose of ontology building and system development, maintenance, and objects of the ontology application need to be identified, and the domain and field of interest need to be described [12]. Besides, the collection of data also occurs at this phase.…”

Section: Introductionmentioning

confidence: 99%

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A systematic literature review of automatic ontology construction

Zulkipli

Maskat

Teo

2022

IJEECS

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In recent years, ontologies have received a lot of attention as a knowledge representation technique. Constructing an ontology can be a difficult task for several reasons: it necessitates time-consuming expert work, the classification task is not as simple as it appears, and the incredible speed with which knowledge evolves in the real world forces ontology engineers to constantly update and enrich the generated ontologies with new concepts, terms, and lexicon. However, there are other ways to automate ontology construction, such as semi-automatically, where human intervention is required in one or more ontology design tasks, and fully automatically, where the entire ontology construction is delegated to a software system. In this paper, we will conduct a systematic review of literature that will focus on a comparative analysis of different techniques relating to both semi-automatic and fully automatic ontology construction using various techniques and automated algorithms applied. In these fields, the goal is to identify the domain areas, current trends, data architectures, and ongoing challenges. This paper will review academic documents published in peer-reviewed venues from 2017 to 2021, based on a four-step selection process of identification, screening, eligibility, and inclusion for the selection process. To examine these documents, a systematic review was conducted and six main research questions were answered. The results indicate that automatic ontology construction could give higher complexity, shorter time, and reduce the role of the expert knowledge to evaluate ontology than manual ontology construction. The fields that have been investigated in this survey include online retail, biomedical, public security, information security (IS), Quran, Arabic, Dubai government services, Alzheimer’s disease, agriculture, Chinese tax, job portal, sentiment, and ontology learning. Finally, we summarize the most commonly used methods in automatic ontology construction, which we believe will serve as a foundation for future multidisciplinary research.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A systematic literature review of automatic ontology construction

Zulkipli

Maskat

Teo

2022

IJEECS

View full text Add to dashboard Cite

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“…Amazon Marketplace products are presented on detailed product pages and, as on other e-commerce websites [7][8][9], are arranged in a taxonomy of categories to favor consistent navigation structures, thereby enhancing the user experience and the website's usability. Each detailed product page provides a proper description of the product, which includes its characteristics, consumer reviews, stock availability, the price or product rating, and the Buy Box .…”

Section: Introductionmentioning

confidence: 99%

Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm

2022

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Purchase processes on Amazon Marketplace begin at the Buy Box, which represents the buy click process through which numerous sellers compete. This study aimed to estimate empirically the relevant seller characteristics that Amazon could consider featuring in the Buy Box. To that end, 22 product categories from Italy’s Amazon web page were studied over a ten-month period, and the sellers were analyzed through their products featured in the Buy Box. Two different experiments were proposed and the results were analyzed using four classification algorithms (a neural network, random forest, support vector machine, and C5.0 decision trees) and a rule-based classification. The first experiment aimed to characterize sellers unspecifically by predicting their change at the Buy Box. The second one aimed to predict which seller would be featured in it. Both experiments revealed that the customer experience and the dynamics of the sellers’ prices were important features of the Buy Box. Additionally, we proposed a set of default features that Amazon could consider when no information about sellers was available. We also proposed the possible existence of a relationship or composition among important features that could be used for sellers to be featured in the Buy Box.

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“…With so much data and services available, it is challenging not only for users to identify products they are interested in fast, but also for e-commerce and similar systems to recommend the product from the data. Recommendation systems (RSs) [1] are decision-support information systems created to assist users in locating things that fit their interests from the vast variety of choices [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…While these methods have shown effectiveness and accuracy in constructing RSs, all are based on a fixed ground truth (the dataset) captured at a given time instant, so it is difficult to recommend diversified, personalized products since the recommendations are based on past observed purchase history, and it is not possible to take advantage of the change in users' preferences with time, their drift [13], because the computational load for building the model is often too large. For instance, ontologies have to follow a slow and complex synthesis process, and the aid of external experts is required [1]; ML needs large datasets and long training times to learn a precise statistical model, and MF runs in time O(n 3 ) at least, where n is the number of users. MF methods suffer also from the sparsity problem [14] (inaccurate inferences when new users or products are added), and ML methods exploit the statistical correlations independently of the logical relationships among user-items.…”

Section: Introductionmentioning

confidence: 99%

Neural Collaborative Filtering with Ontologies for Integrated Recommendation Systems

Ahmed

Fernández‐Veiga

Gawich

2022

Sensors

Self Cite

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Machine learning (ML) and especially deep learning (DL) with neural networks have demonstrated an amazing success in all sorts of AI problems, from computer vision to game playing, from natural language processing to speech and image recognition. In many ways, the approach of ML toward solving a class of problems is fundamentally different than the one followed in classical engineering, or with ontologies. While the latter rely on detailed domain knowledge and almost exhaustive search by means of static inference rules, ML adopts the view of collecting large datasets and processes this massive information through a generic learning algorithm that builds up tentative solutions. Combining the capabilities of ontology-based recommendation and ML-based techniques in a hybrid system is thus a natural and promising method to enhance semantic knowledge with statistical models. This merge could alleviate the burden of creating large, narrowly focused ontologies for complicated domains, by using probabilistic or generative models to enhance the predictions without attempting to provide a semantic support for them. In this paper, we present a novel hybrid recommendation system that blends a single architecture of classical knowledge-driven recommendations arising from a tailored ontology with recommendations generated by a data-driven approach, specifically with classifiers and a neural collaborative filtering. We show that bringing together these knowledge-driven and data-driven worlds provides some measurable improvement, enabling the transfer of semantic information to ML and, in the opposite direction, statistical knowledge to the ontology. Moreover, the novel proposed system enables the extraction of the reasoning recommendation results after updating the standard ontology with the new products and user behaviors, thus capturing the dynamic behavior of the environment of our interest.

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Improving Recommendations for Online Retail Markets Based on Ontology Evolution

Cited by 9 publications

References 34 publications

A systematic literature review of automatic ontology construction

A systematic literature review of automatic ontology construction

Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm

Neural Collaborative Filtering with Ontologies for Integrated Recommendation Systems

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