Measuring consumers' willingness to pay with utility-based recommendation systems

Scholz, Michael; Dorner, Verena; Franz, Markus; Hinz, Oliver

doi:10.1016/j.dss.2015.02.006

Cited by 45 publications

(26 citation statements)

References 66 publications

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“…Consumers who are not willing to evaluate all available products need only compare the top-ranked products and select one alternative holistically. Hence, recommender systems are commonly evaluated based on holistic product ratings (Scholz et al, 2015;Xiao and Benbasat, 2007;Herlocker et al, 2004).…”

Section: Analysis and Resultsmentioning

confidence: 99%

“…MAVT-based recommender systems estimate consumer-specific values for all products of a given category at the time of purchase (Huang, 2011;Pu et al, 2011;Scholz et al, 2015), based on individual value functions and attribute weights. The first characteristic of ideal MAVT-based recommender systems, is therefore reliable estimation of attribute weights.…”

Section: Measuring Attribute Weights In Mavt-based Recommender Systemsmentioning

confidence: 99%

“…The intuition behind the two most common approaches, collaborative filtering and content-based recommender systems (e.g., Yue et al, 2014;Adomavicius and Tuzhilin, 2005), is using past information about consumers' purchase decisions to predict future decisions. This can lead to low-quality recommendations when relevant data are missing (i.e., for new customers and new products), and when consumer preferences change over time (Scholz et al, 2015;Ansari et al, 2000). These problems are proposed to be solved by a third approach, multi-attribute value theory (MAVT)-based recommender systems (Pu et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A configuration-based recommender system for supporting e-commerce decisions

Scholz

Dorner

Schryen

et al. 2017

European Journal of Operational Research

Self Cite

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Multi-attribute value theory (MAVT)-based recommender systems have been proposed for dealing with issues of existing recommender systems, such as the cold-start problem and changing preferences. However, as we argue in this paper, existing MAVT-based methods for measuring attribute importance weights do not fit the shopping tasks for which recommender systems are typically used. These methods assume well-trained decision makers who are willing to invest time and cognitive effort, and who are familiar with the attributes describing the available alternatives and the ranges of these attribute levels. Yet, recommender systems are most often used by consumers who are usually not familiar with the available attributes and ranges and who wish to save time and effort. Against this background, we develop a new method, based on a product configuration process, which is tailored to the characteristics of these particular decision makers. We empirically compare our method to SWING, ranking-based conjoint analysis and TRADEOFF in a betweensubjects laboratory experiment with 153 participants. Results indicate that our proposed method performs better than TRADEOFF and CONJOINT and at least as well as SWING in terms of recommendation accuracy, better than SWING and TRADEOFF and at least as well as CONJOINT in terms of cognitive load, and that participants were faster with our method than with any other method. We conclude that our method is a promising option to help support consumers' decision processes in e-commerce shopping tasks.

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Section: Analysis and Resultsmentioning

confidence: 99%

Section: Measuring Attribute Weights In Mavt-based Recommender Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A configuration-based recommender system for supporting e-commerce decisions

Scholz

Dorner

Schryen

et al. 2017

European Journal of Operational Research

Self Cite

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“…The recommendation system [25,26] guesses the consumer shopping preferences according to the characteristics of products that the consumers purchased in the past, and then recommends similar products to him or her [27]. However, a pure CBF system also has its limitations.…”

Section: The Recommendation System and Hybrid Algorithmmentioning

confidence: 99%

An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

Guo

Wang

2017

Symmetry

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Abstract:With the rapid development of e-commerce, the contradiction between the disorder of business information and customer demand is increasingly prominent. This study aims to make e-commerce shopping more convenient, and avoid information overload, by an interactive personalized recommendation system using the hybrid algorithm model. The proposed model first uses various recommendation algorithms to get a list of original recommendation results. Combined with the customer's feedback in an interactive manner, it then establishes the weights of corresponding recommendation algorithms. Finally, the synthetic formula of evidence theory is used to fuse the original results to obtain the final recommendation products. The recommendation performance of the proposed method is compared with that of traditional methods. The results of the experimental study through a Taobao online dress shop clearly show that the proposed method increases the efficiency of data mining in the consumer coverage, the consumer discovery accuracy and the recommendation recall. The hybrid recommendation algorithm complements the advantages of the existing recommendation algorithms in data mining. The interactive assigned-weight method meets consumer demand better and solves the problem of information overload. Meanwhile, our study offers important implications for e-commerce platform providers regarding the design of product recommendation systems.

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“…The fourth method is the utility-based recommendation. For instance, Scholz et al (2015) [19] found that exponential utility functions are better geared to predicting optimal recommendation ranks for products, and linear utility functions perform much better in estimating customers' willingness.…”

Section: Related Workmentioning

confidence: 99%

Fashion Evaluation Method for Clothing Recommendation Based on Weak Appearance Feature

Zhang

Liu

Shi

et al. 2017

Scientific Programming

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With the rapid rising of living standard, people gradually developed higher shopping enthusiasm and increasing demand for garment. Nowadays, an increasing number of people pursue fashion. However, facing too many types of garment, consumers need to try them on repeatedly, which is somewhat time-and energy-consuming. Besides, it is difficult for merchants to master the real-time demand of consumers. Herein, there is not enough cohesiveness between consumer information and merchants. Thus, a novel fashion evaluation method on the basis of the appearance weak feature is proposed in this paper. First of all, image database is established and three aspects of appearance weak feature are put forward to characterize the fashion level. Furthermore, the appearance weak features are extracted according to the characters' facial feature localization method. Last but not least, consumers' fashion level can be classified through support vector product, and the classification is verified with the hierarchical analysis method. The experimental results show that consumers' fashion level can be accurately described based on the indexes of appearance weak feature and the approach has higher application value for the clothing recommendation system.

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Measuring consumers' willingness to pay with utility-based recommendation systems

Cited by 45 publications

References 66 publications

A configuration-based recommender system for supporting e-commerce decisions

A configuration-based recommender system for supporting e-commerce decisions

An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

Fashion Evaluation Method for Clothing Recommendation Based on Weak Appearance Feature

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