Effects of product characteristics on the bundling strategy implemented by recommendation systems

Ge, Xinyu; Zhang, Yousha; Qian, Yu; Yuan, Hua

doi:10.1109/icsssm.2017.7996297

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Some studies recommend bundles to groups of users by aggregating user preferences within groups [47,50]. The effect of product characteristics (e.g., popularity) on bundling strategy and its complexity have been explored in [21] and [15], respectively. There are also potential item relations for bundle generation to maintain consistency [26,28,37,39,59] and diversity [1,44].…”

Section: Constraint Based Methodsmentioning

confidence: 99%

Revisiting Bundle Recommendation

Sun

Yang

Feng

et al. 2022

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Product bundling is a commonly-used marketing strategy in both offline retailers and online e-commerce systems. Current research on bundle recommendation is limited by: (1) noisy datasets, where bundles are defined by heuristics, e.g., products co-purchased in the same session; and (2) specific tasks, holding unrealistic assumptions, e.g., the availability of bundles for recommendation directly. In this paper, we propose to take a step back and consider the process of bundle recommendation from a holistic user experience perspective. We first construct high-quality bundle datasets with rich meta information, particularly bundle intents, through a carefully designed crowd-sourcing task. We then define a series of tasks that together, support all key steps in a typical bundle recommendation process, from bundle detection, completion, ranking, to explanation and auto-naming. Finally, we conduct extensive experiments and in-depth analysis that demonstrate the challenges of bundle recommendation, arising from the need for capturing complex relations among users, products and bundles, as well as the research opportunities, especially in graph-based neural methods. To sum up, our study delivers new data sources, opens up new research directions, and provides useful guidance for product bundling in real e-commerce platforms. Our datasets are available at GitHub (https://github.com/BundleRec/bundle_recommendation).

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Section: Constraint Based Methodsmentioning

confidence: 99%

Revisiting Bundle Recommendation

Sun

Yang

Feng

et al. 2022

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

View full text Add to dashboard Cite

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“…One pricing scheme that deals with charging [11] back to user whoever utilize the other network is called reverse charging scheme [12,13]. Reverse charging is applied for electrical devices [14][15][16], even vehicles [17,18] and can be formulates as probabilistic mathematical modelling [19]. Currently, the extensive use of wireless network make ISP attempts to set up new pricing plan based on reverse charging (internet reverse charing-IRC) scheme in wired and wireless network [20].…”

Section: Introductionmentioning

confidence: 99%

Models of improved multilink reverse charging network by utilizing the bit error rate QoS attribute

Puspita

Rohania

Yuliza

et al. 2021

IJEECS

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<span>In this paper, a modification model for single-link reverse charging of internet is formed on a multi-link wireless network. The pricing scheme also takes into account the base costs and quality of services provided by the service provider. Bit error rate (BER) was utilized as one of the well-known quality of service (QoS) attribute that can guarantee best performance for internet service provider (ISP) and users. The base price is determined as a decision variable to help ISP to maximize profit. This optimization model can be solved using the LINGO 13.0 program to gain optimal values. The computational results show that by setting costs as constants and service quality as variables, optimal results are obtained for ISPs. This can make ISP considerations in determining the base price that can benefit the ISP and according to the services provided</span>.

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