Empirical Analysis of the Impact of Recommender Systems on Sales

Pathak, Bhavik; Garfinkel, Robert; Gopal, Ram D.; Venkatesan, Rajkumar; Yin, Fang

doi:10.2753/mis0742-1222270205

Cited by 271 publications

(155 citation statements)

References 45 publications

Supporting

Mentioning

146

Contrasting

Unclassified

Order By: Relevance

“…RS are widely applied in applications such as online shops, news portals, or media platforms and have been shown to have strong commercial implications, e.g., by increasing the number of sales [32]. A wide range of recommender techniques have been developed, both in academia and industry, that are mostly based either on users' ratings of items (provided explicitly by the user or implicitly based on interaction behavior or purchases) which is known as collaborative filtering, or on properties of the items themselves (contentbased filtering).…”

Section: Introductionmentioning

confidence: 99%

Negotiation and Reconciliation of Preferences in a Group Recommender System

Márquez

Ziegler

2018

Journal of Information Processing

View full text Add to dashboard Cite

This article presents an approach to group recommender systems that focuses its attention on the group's social interaction during the formulation, discussion and negotiation of the features the item to be jointly selected should possess. Current group recommender techniques are mainly based on aggregating existing user profiles or on a profile of the group as a whole. Our method supports collaborative preference elicitation and negotiation process where desired features have to be chosen individually, but group consensus is needed for them to become active in the item filtering process. Users provide feedback on the selected preferences and change their significance, bringing up new recommendations each time individual settings are modified. The last stage in the decision process is also supported, when users collectively select the final item from the recommendation set. We explored the possible benefits of our approach through the development of three prototypes, each based on a different variant of the approach with a different emphasis on private and group-wide preference spaces. They were evaluated with user groups of different size, addressing questions regarding the effectiveness of different information sharing methods and the repercussion of group size in the recommendation process. We compare the different methods and consolidate the findings in an initial model of recommending for group.

show abstract

Section: Introductionmentioning

confidence: 99%

Negotiation and Reconciliation of Preferences in a Group Recommender System

Márquez

Ziegler

2018

Journal of Information Processing

View full text Add to dashboard Cite

show abstract

“…When the web bloggers post more information, it resulted in had greater value for readers. Studies concerning the impact of customer reviews such as Chevalier and Mayzlin (2006), Pathak et al (2010), and Zhu and Zhang (2010) all revealed that the number of online reviews positively affect its business performance. In other studies by Duan et al, (2008aDuan et al, ( , 2008b ;Liu, (2006) and Ye et al (2011) found that the volume of online reviews, separated from the ratings, emerged as the primary influence on sales and there were positive relationship between the number of reviews and the number of bookings for hotels.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Accommodation Types Difference by Age Groups

Suvachart¹

2017

Proceedings of the 30th International Academic Conference, Venice

View full text Add to dashboard Cite

show abstract

“…Pathak et al [17] study the cross effects between sales, pricing and recommendations on Amazon books. They show that recommendation systems increase sales and cause price changes.…”

Section: Related Workmentioning

confidence: 99%

“…However, most businesses do not provide the specification of their recommender system (treating it as a "secret sauce"), which diminishes this concern. Furthermore, several companies (including Netflix, Walmart and Amazon) admitted human intervention in their recommender system [17], so it may well be that different companies are already tweaking their recommender systems for their own good. In this sense, an important lesson to take away from this work is "users beware".…”

Section: Introductionmentioning

confidence: 99%

Movie recommender system for profit maximization

Azaria

Hassidim

Kraus

et al. 2013

Proceedings of the 7th ACM Conference on Recommender Systems

View full text Add to dashboard Cite

Traditional recommender systems minimize prediction error with respect to users' choices. Recent studies have shown that recommender systems have a positive effect on the provider's revenue.In this paper we show that by providing a set of recommendations different than the one perceived best according to user acceptance rate, the recommendation system can further increase the business' utility (e.g. revenue), without any significant drop in user satisfaction. Indeed, the recommendation system designer should have in mind both the user, whose taste we need to reveal, and the business, which wants to promote specific content.We performed a large body of experiments comparing a commercial state-of-the-art recommendation engine with a modified recommendation list, which takes into account the utility (or revenue) which the business obtains from each suggestion that is accepted by the user. We show that the modified recommendation list is more desirable for the business, as the end result gives the business a higher utility (or revenue). To study possible reduce in satisfaction by providing the user worse suggestions, we asked the users how they perceive the list of recommendation that they received. Differences in user satisfaction between the lists is negligible, and not statistically significant.We also uncover a phenomenon where movie consumers prefer watching and even paying for movies that they have already seen in the past than movies that are new to them.

show abstract

Empirical Analysis of the Impact of Recommender Systems on Sales

Cited by 271 publications

References 45 publications

Negotiation and Reconciliation of Preferences in a Group Recommender System

Negotiation and Reconciliation of Preferences in a Group Recommender System

Exploring the Accommodation Types Difference by Age Groups

Movie recommender system for profit maximization

Contact Info

Product

Resources

About