A Hybrid Approach for Knowledge-Based Product Recommendation

Godse, Manish; Sonar, Rajendra M.; Jadhav, Anil

doi:10.1007/978-3-642-00405-6_29

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Local similarity for numeric type of features is calculated using the functions given below (Avramenko and Kraslawski, 2006). These similarity measures have also been used for online product recommendation systems developed using iKenStudio [http://www.ikenstudio.com] (Godse et al, 2009). Other functions that have been used in HKBS to calculate individual feature level similarity for numeric type of features are as follows.…”

Section: Evaluation Using Hkbsmentioning

confidence: 99%

Framework for evaluation and selection of the software packages: A hybrid knowledge based system approach

Jadhav

Sonar

2011

Journal of Systems and Software

115

134

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Section: Evaluation Using Hkbsmentioning

confidence: 99%

Framework for evaluation and selection of the software packages: A hybrid knowledge based system approach

Jadhav

Sonar

2011

Journal of Systems and Software

115

134

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“…However, the constructed path recommendation method does not meet the requirements of cloud product recommendations. In [39], a cloud concept KG was constructed for cloud product recommendation, but only manually built the simplest relationship to connect entities, and did not explore further by building more complex semantics in the relationship.…”

Section: B Recommendation Systems Based On Kgmentioning

confidence: 99%

Cloud Computing Product Service Scheme Recommendation System Based on a Hierarchical Knowledge Graph

Xu,

Wu,

Shi

et al. 2023

IEEE Access

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It is difficult for users to understand the complex cloud product information for product selection. Using this information to recommend satisfactory cloud products is a challenge. Previous studies focused on similar information of users and products while neglecting relevance; therefore, they could not create recommendation approaches that account for functional dependencies among cloud products. To overcome this challenge, this study proposes a cloud product set recommendation model based on a hierarchical knowledge graph (KG) with a pre-post correlation of product functionality. There are two main contributions: First, we construct a cloud product functionality and performance KG using the dependency information of layers and entities to represent complicated pre-post logical connections. The KG was designed according to the cloud service model. Second, we designed an improved PageRank algorithm to obtain the importance weight for each functionality and performance, which replaces the original average method with the proportion of connection weight. We considered the release time of the functionality, launch time of the product, and last update time of the product as crucial factors in the recommendation score to reflect the importance of the functionality and current development stage of the product. Finally, our method recommends a product set based on the weighted scores from the above results. In addition, we constructed a cloud product functionality dataset containing 339 functionalities. The experimental results show that the proposed method can generate a closely related set of products, leading to improved accuracy and higher satisfaction compared to mainstream methods.INDEX TERMS product set recommendation, knowledge graph, cloud service, PageRank, cloud product functionality dataset.

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“…This framework uses XML as an integration mechanism to integrate intelligent systems such as RBR, CBR and neural networks. It has been applied in various applications (Jadhav & Sonar, 2011) such as knowledge-based recommender systems Godse, Jadhav & Sonar, 2009). Figure 8 shows building blocks of hybrid development framework.…”

Section: Hybrid Intelligent System Frameworkmentioning

confidence: 99%

Towards Automation of Business Intelligence Services Using Hybrid Intelligent System Approach

Sonar

2013

International Journal of Business Intelligence Research

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Business Intelligence (BI) includes many tools, techniques and technologies. BI processes often involve team of human decision makers and end-users to extract, explore and analyse the data. The results, decisions or models after analysis need to be implemented into operational systems. There can be considerable time delay between business events happening and action taken thus loosing opportunities. Intelligent techniques such as rule-based reasoning and case-based reasoning have been used extensively to address wide range of intelligent tasks including personalisation and recommendation. Some BI tasks can be modelled, automated and delivered through services rather than done on ad-hoc basis. The authors represent a service based approach to BI where a service corresponds to a well defined analytical functionality implemented using intelligent technique(s), filtering techniques or hybrids of them accessing only relevant data from database specifically modelled and designed for such tasks. The authors discuss an application of the approach for a value-added service in mobile domain.

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A Hybrid Approach for Knowledge-Based Product Recommendation

Cited by 4 publications

References 20 publications

Framework for evaluation and selection of the software packages: A hybrid knowledge based system approach

Framework for evaluation and selection of the software packages: A hybrid knowledge based system approach

Cloud Computing Product Service Scheme Recommendation System Based on a Hierarchical Knowledge Graph

Towards Automation of Business Intelligence Services Using Hybrid Intelligent System Approach

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