E-commerce Recommendation with Personalized Promotion

Zhao, Qi; Zhang, Yi; Friedman, Daniel; Tan, Fangfang

doi:10.1145/2792838.2800178

Cited by 57 publications

(25 citation statements)

References 21 publications

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“…U (q) is the dollar value to the consumer of being able to consume q units of the good in question, and U (q) is her Willingness To Pay (WTP) [13,42], i.e., the maximum amount of money she would pay to acquire another unit of good. This enables us to align the utility in the same scale as price so as to calculate the surplus of consumers and producers [12].…”

Section: Utilitymentioning

confidence: 99%

“…To estimate the consumer and producer surplus experienced on a given job, we adopt the economic assumption that the percentage surplus against the price that the consumer pays or the producer obtains is proportional to the normalized ratings that they cast on each other [12,42], i.e., a higher rating implies a higher percentage surplus.…”

Section: Online Freelancing Platformsmentioning

confidence: 99%

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Economic Recommendation with Surplus Maximization

Zhang

Zhao

Zhang

et al. 2016

Proceedings of the 25th International Conference on World Wide Web

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Discussion papers of the WZB serve to disseminate the research results of work in progress prior to publication to encourage the exchange of ideas and academic debate. Inclusion of a paper in the discussion paper series does not constitute publication and should not limit publication in any other venue. The discussion papers published by the WZB represent the views of the respective author(s) and not of the institute as a whole. Terms of use: Documents in Copyright remains with the authors.ABSTRACT A prime function of many major World Wide Web applications is Online Service Allocation (OSA), the function of matching individual consumers with particular services/goods (which may include loans or jobs as well as products) each with its own producer. In the applications of interest, consumers are free to choose, so OSA usually takes the form of personalized recommendation or search in practice. The performance metrics of recommender and search systems currently tend to focus on just one side of the match, in some cases the consumers (e.g. satisfaction) and in other cases the producers (e.g., profit). However, a sustainable OSA platform needs benefit both consumers and producers; otherwise the neglected party eventually may stop using it.In this paper, we show how to adapt economists' traditional idea of maximizing total surplus (the sum of consumer net benefit and producer profit) to the heterogeneous world of online service allocation, in an effort to promote the web intelligence for social good in online eco-systems. Modifications of traditional personalized recommendation algorithms enable us to apply Total Surplus Maximization (TSM) to three very different types of real-world tasks -e-commerce, P2P lending and freelancing. The results for all three tasks suggest that TSM compares very favorably to currently popular approaches, to the benefit of both producers and consumers.

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Section: Utilitymentioning

confidence: 99%

Section: Online Freelancing Platformsmentioning

confidence: 99%

Economic Recommendation with Surplus Maximization

Zhang

Zhao

Zhang

et al. 2016

Proceedings of the 25th International Conference on World Wide Web

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“…In recent years, recommender systems have become more and more popular to process vast amounts of client data and to make specific product and service recommendations for individuals [2], [3]. This technique has been used very successfully in the B2C space -Amazon [4] and Alibaba [5] are just two of the many examples.…”

Section: Introductionmentioning

confidence: 99%

GreedyBoost: An Accurate, Efficient and Flexible Ensemble Method for B2B Recommendations

Zhang

Enders

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Recommender systems have achieved great success in finding relevant products and services for individual customers, e.g. in B2C markets, during recent years. However, due to the diversity of enterprise clients' requirements it is still an open question on how to successfully apply existing recommendation techniques in the B2B domain. This paper presents GreedyBoost -an accurate, efficient and flexible ensemble method for product and service recommendations in the B2B domain. Given a set of base models, GreedyBoost can sequentially add base models to the ensemble by a linear approach to minimize training error, so that the ensemble process is efficient. Meanwhile, GreedyBoost does not have any special requirement on base models and evaluation metrics, so that any kind of client requirements and sale & distribution purposes can be adapted. Experimental results on real-world B2B data demonstrate that GreedyBoost can achieve higher recommendation accuracy compared with two popular ensemble methods.

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“…We compared the CBRF algorithm to Introduction Personalized coupon recommendation is a common service offered by e-commerce vendors and retailers to increase their sales and maintain relationships with consumers (e.g., Danaher, Smith, Ranasinghe, & Danaher, 2015;Zhao, Zhang, Friedman, & Tan, 2015). Although context-aware recommender systems (CARS) have been widely analyzed, personalized pricing or discount optimization in recommender systems to improve recommendations' accuracy and commercial KPIs has hardly been researched.…”

mentioning

confidence: 99%