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The Decoy Effect and Recommendation Systems
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2023 | Information Systems Research | Citations: 0
Authors: Mousavi, Nasim; Adamopoulos, Panagiotis; Bockstedt, Jesse

Abstract: In this paper, we explore the decoy effect in recommendation systems. Including ... Expand

Abstract: In this paper, we explore the decoy effect in recommendation systems. Including a decoy item in a set of alternatives can influence the attractiveness of the other items by facilitating decision making. Prior research literature has indeed shown the decoy effect to be robust in traditional choice settings, with consistent reporting of an overall positive impact. Practitioners often use decoys to help drive demand for specific items. Recommendation systems too are increasingly being used to present item choice sets to customers and users. Both recommendation systems and the decoy effect can be used as strategies to help facilitate decision making. However, previous work has not examined the decoy effect in the context of recommendations. The decoy effect may facilitate consumer decision making and positively impact user behavior when used with recommendation systems. However, in the recommendation context, customers often have different expectations for the reliability and quality of the presented information. Hence, a decoy as a recommendation could signal issues in system reliability, resulting in a negative effect. We perform a randomized, controlled laboratory experiment and use persuasion theory as the theoretical lens to demonstrate that the decoy effect works differently in the context of recommendation systems. Specifically, we show that depending on the recommendation context, the decoy effect can or cannot drive demand for target items. We find that including a decoy minimizes the demand for the target option when personalized recommendations are presented, which deviates from the traditional decoy effect. However, a decoy increases the target’s demand when nonpersonalized recommendations are shown, following the conventional decoy effect. We explore the mechanism behind these findings and show the robustness of our results by conducting multiple analyses and additional experiments. The findings of our paper have important implications for the design of recommendation systems and our understanding of consumer decision making. History: Ravi Bapna, Senior Editor; Idris Adjerid, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1197. Collapse

Semantic filters: collaborative filtering data quality

Topics: recommender system usability personalization database system user behavior

Methods: experiment experimental group laboratory experiment computational algorithm experimental task
Something’s Missing? A Procedure for Extending Item Content Data Sets in the Context of Recommender Systems
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2022 | Information Systems Frontiers | Citations: 0
Authors: Heinrich, Bernd; Hopf, Marcus; Lohninger, Daniel; Schiller, Alexander; Szubartowicz, Michael

Abstract: The rapid development of e-commerce has led to a swiftly increasing number of co ... Expand

Abstract: The rapid development of e-commerce has led to a swiftly increasing number of competing providers in electronic markets, which maintain their own, individual data describing the offered items. Recommender systems are popular and powerful tools relying on this data to guide users to their individually best item choice. Literature suggests that data quality of item content data has substantial influence on recommendation quality. Thereby, the dimension completeness is expected to be particularly important. Herein resides a considerable chance to improve recommendation quality by increasing completeness via extending an item content data set with an additional data set of the same domain. This paper therefore proposes a procedure for such a systematic data extension and analyzes effects of the procedure regarding items, content and users based on real-world data sets from four leading web portals. The evaluation results suggest that the proposed procedure is indeed effective in enabling improved recommendation quality. Collapse

Semantic filters: collaborative filtering data quality

Topics: missing data recommender system data quality electronic commerce decision support system

Methods: computational algorithm data imputation literature study binary classification literature sample
Data modalities, consumer attributes and recommendation performance in the fashion industry
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2022 | Electronic Markets | Citations: 0
Authors: Sysko-Romańczuk, Sylwia; Zaborek, Piotr; Wróblewska, Anna; Dąbrowski, Jacek; Tkachuk, Sergiy

Abstract: This paper investigates determinants of recommendation systems’ performance in a ... Expand

Abstract: This paper investigates determinants of recommendation systems’ performance in an online experiment in a large European Internet footwear store. By combining transactional data and archival customer records, a unique database was compiled from which proxy variables were extracted to represent dimensions of consumer loyalty and shopping involvement. These variables were combined in regression analysis with technical characteristics of two types of algorithms employed for generating recommendations: the EMDE algorithm, relying on the LSH method, and the industry-standard CF-RS. Statistical analysis reveals that recommendations are more successful when visual data modality is combined with behavioural data. Better recommendation performance was found to be associated with lower levels of consumer involvement in shopping, as well as higher levels of trust and engagement with the vendor. Experience with the vendor showed a negative correlation with recommendation performance through both its main effect and by its interactions with other consumer-related variables. Collapse

Semantic filters: collaborative filtering data quality

Topics: database system marketing management consumer behavior recommender system internet technology

Methods: computational algorithm experiment experimental group machine learning logistic regression
Impact of End-User Privacy Enhancing Technologies (PETs) on Firms’ Analytics Performance

2020 | International Conference on Information Systems | Citations: 0
Authors: Chen, Dawei; Hahn, Jungpil

Abstract: Big data analytics in digital commerce requires vast amounts of personal informa ... Expand

Abstract: Big data analytics in digital commerce requires vast amounts of personal information from consumers, but this gives rise to major privacy concerns. To combat the threat of privacy invasion, individuals are proactively adopting privacy enhancing technologies (PETs) to protect their personal information. Consumers’ adoption of PETs may hamper firms’ big data analytics capabilities and performance but our knowledge of how PETs impact firms’ data analytics is limited. This study proposes an inductively derived framework which qualitatively shows that end-user PETs induce measurement error and/or missing values with regards to attributes, entities and relationships in firms’ customer databases, but the impacts of specific end-user PETs may vary by analytics use case. Our simulation experiments in the context of product recommendations quantitively find that consumers’ adoption characteristics (adoption rate and pattern) and PETs characteristics (protection mechanism and intensity) significantly affect the performance of recommender systems. Collapse

Semantic filters: collaborative filtering data quality

Topics: privacy missing data mobile application market recommender system digital distribution

Methods: simulation computational algorithm theory development experiment literature study
(How) Does Data-based Music Discovery Work?

2015 | European Conference On Information Systems | Citations: 0
Authors: Charoenpanich, Akarapat; Aaltonen, Aleksi

Abstract: This paper analyses a new type of business operations that mediate the productio ... Expand

Abstract: This paper analyses a new type of business operations that mediate the production and consumption of music. Online environment has largely abolished constraints on the variety of music that can be economically distributed, but, at the same time, it reveals another problem. How do people learn what music items do they want to listen to? In the music industry, the product space consists of thousands of artists, songs and albums, and is expanding rapidly. More effective forms of music discovery could therefore create considerable new value by allowing people to listen to music that better matches their taste. We analyse data from Last.fm music discovery service that deploys a collaborative filtering recommender system and social media features to aid music discovery. The analysis finds evidence that the new form of music discovery is valuable to consumers, yet it is relatively less important than an opportunity to listen to music for free. The findings lead us to discuss how the nature of analytical problem and product space, consumer taste, and social media features shape the potential value of created by big data. Collapse

Semantic filters: collaborative filtering data quality

Topics: social media big data recommender system analytical information system innovation management

Methods: ordinary least square path analysis longitudinal research logarithmic transformation theory development
An Automated Recommendation Approach to Selection in Personnel Recruitment

2003 | Americas Conference on Information Systems | Citations: 0
Authors: Faerber, Frank; Weitzel, Tim; Keim, Tobias

Abstract: Many online recruitment platforms suffer from the inappropriateness of Boolean s ... Expand

Abstract: Many online recruitment platforms suffer from the inappropriateness of Boolean search methods for matching candidates with job requirements. While such platforms have so far been a successful means for decreasing personnel advertising cost, the huge amount of electronic candidate profiles has not yet been exploited to optimize search quality. Collapse

Semantic filters: collaborative filtering data quality

Topics: collaborative filtering advertising management healthcare data content-based filtering data quality

Methods: survey computational algorithm latent semantic analysis case study qualitative interview

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Found 6 articles

The Decoy Effect and Recommendation Systems

Something’s Missing? A Procedure for Extending Item Content Data Sets in the Context of Recommender Systems

Data modalities, consumer attributes and recommendation performance in the fashion industry

Impact of End-User Privacy Enhancing Technologies (PETs) on Firms’ Analytics Performance

(How) Does Data-based Music Discovery Work?

An Automated Recommendation Approach to Selection in Personnel Recruitment