A generative approach to product bundling in the e-Commerce domain

Birtolo, Cosimo; Chiara, Diego De; Ascione, Maria; Armenise, Roberto

doi:10.1109/nabic.2011.6089454

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…Note that dependent parameters and their values are given in parentheses (see, e.g. (2,3,4) and (4,4,6) in line S# 6 of Table 2 and sub-section 4.4). Table 3 presents results (Task 1 and Task 2) of all possible transformations 1…”

Section: Program Generation (Seementioning

confidence: 99%

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Refactoring of Heterogeneous Meta-Program into k-stage Meta-Program

Štuikys

Bespalova

Burbaitė

2014

ITC

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The paper presents: (1) a graph-based theoretical background to refactoring a correct heterogeneous meta-program into its k-stage representation; (2) the refactoring method; (3) refactoring experiments with tasks taken from different domains, including real world tasks, such as meta-programs to teach Computer Science (CS) topics using educational robots. Refactoring meta-programs by staging enables to flexibly adapt them to the different context of use. To do that (semi-)automatically, we use the contextual information as a priority relation (e.g. highest, lowest, etc.) introduced within the meta-program specification. We implement the refactoring method using the so-called activating/de-activating label (index) to change the role of metalanguage constructs at different stages. The contribution of the paper is: (1) applying the known (in programming) staging concept to heterogeneous metaprogramming; (2) a theoretical background, properties and the method to solve tasks of this kind of refactoring.

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Section: Program Generation (Seementioning

confidence: 99%

“…We consider a few domains with such features, where metaprogramming-based generators have been approved as a relevant technology. Examples are: e-learning, e-commerce and components of embedded systems [6,44].…”

Section: Introductionmentioning

confidence: 99%

Refactoring of Heterogeneous Meta-Program into k-stage Meta-Program

Štuikys

Bespalova

Burbaitė

2014

ITC

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“…This framework is a result of research activities within an Italian re- , is to study and define the models, methods and technologies for e-Business based on Knowledge and on the Social Web. In details, the project focuses on the design and implementation of an electronic mall that enhances the shopping experiences of users by means of the adoption, integration and extension of existing components with some innovative features such as "Personalized suggestions" [4], "Automatic generation of personalized product bundles" [3], Marketing Intelligence techniques, and "Faceted Navigation".…”

Section: A Context-aware Recommenda-tion Frameworkmentioning

confidence: 99%

A flexible framework for context-aware recommendations in the social commerce domain

Ronca

Calvanese

Birtolo

2013

Proceedings of the Joint EDBT/ICDT 2013 Workshops

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Identifying the products to suggest to the target customer in order to best fit his profile of interests is a challenging task in the Social Commerce domain. In this paper we investigate the application of different model-based Collaborative Filtering (CF) techniques and we propose a framework able to provide different suggestions to the customers. The framework is aimed at expanding the customer suggestions in order to guarantee a serendipitous discovery of wished products and at the same time, it is aimed at assisting merchants in discovering potential interests of their customers. We compare several approaches by means of Epinions, MovieLens and Poste Italiane dataset (with real customers). Experimental results show an increased value of coverage of the recommendations provided by our approach without affecting recommendation quality. Moreover, personalized strategy guarantees a high level recommendation for different user navigation behaviors.

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“…Users' profiles are either explicitly defined or inferred by analyzing their behaviors during the interaction with the system. • Automatic generation of personalized product bundles [16]. They find the combinations of products that try to satisfy user preferences and requirements, guaranteeing, at the same time, the satisfaction of merchants needs, such as the minimization of the dead stocks.…”

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confidence: 99%

Designing a Scalable Social e-Commerce Application

Zimeo¹,

Oliva²,

Baldi³

et al. 2013

SCPE

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Abstract. eCommerce is gaining a momentum due to the wide diffusion of Web 2.0 technology. Social mining, recommenders and data semantics are moving the focus of eCommerce applications towards context-awareness and personalization. However, the design of these software systems needs specific architectures to support intelligent behaviors, still ensuring important nonfunctional properties, such as flexibility, efficiency and scalability. This paper proposes an architectural pattern that helps designers to easily identify the subsystems that characterize intelligent enterprise systems. By decoupling transactional behavior from batch processing, the pattern avoids the interference of knowledge extraction and reasoning processes with the state and the performance of the transactional subsystem, so improving scalability. The pattern has been experimented in eCommerce by designing an intelligent and scalable virtual mall.Key words: Architectural Pattern, Software Systems Design, Scalability, Enterprise Systems, Intelligent Systems, eCommerce 1. Introduction. Enterprise systems represent today an important class of large-scale software that supports many fundamental processes of complex organizations, ranging from resource planning to business intelligence. In this class, eCommerce applications often groups many enterprise assets to offer an integrated and coherent view to merchants and customers for performing business actions.The adoption of eCommerce as a prevalent channel for selling products is changing the market rules, since competition among vendors is being migrated on the Web. Reaching the widest set of potential customers with information about the commercial products of their interest is one of the challenges that characterize the new solutions and technologies for eCommerce applications.In the evolution of the Web, Web 2.0 technology represents a milestone with its emphasis on supporting social collaboration and reasoning over data semantics. The new way of interaction is introducing a radical innovation in eCommerce [21], moving the focus towards context-awareness and personalization. If on the one hand, these innovative features improve product selling, they also significantly impact the way modern eCommerce applications are designed and implemented.Traditional architectures exploited to design enterprise systems (in particular eCommerce applications) need to be revised in order to take into account the desired ability of these systems to "generate" knowledge while working, on the basis of several information sources that could be available as enterprise assets. These sources could be tightly related to the eCommerce application or belonging to different enterprise subsystems that support cross-business features; they can change over time or can be enriched with additional ones when new needs emerge.Designing an architecture for eCommerce applications in this new scenario is not simple, since designers have to combine the expected intelligent behavior of the system with non-functional requirements, such ...

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A generative approach to product bundling in the e-Commerce domain

Cited by 7 publications

References 9 publications

Refactoring of Heterogeneous Meta-Program into k-stage Meta-Program

Refactoring of Heterogeneous Meta-Program into k-stage Meta-Program

A flexible framework for context-aware recommendations in the social commerce domain

Designing a Scalable Social e-Commerce Application

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