Internet Recommendation Systems

Ansari, Abdul Quaiyum; Essegaier, Skander; Kohli, Rajeev

doi:10.1509/jmkr.37.3.363.18779

Cited by 568 publications

(414 citation statements)

References 20 publications

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“…systems, web-based selling systems, bookmark web-sites, and so on. Motivated by the significance in economy and society, recently, the design of an efficient recommendation algorithm becomes a joint focus from marketing practice [45,46] to mathematical analysis [47], from engineering science [48,49,50] to physics community [51,52,53]. Basically, a recommendation system consists of users and objects, and each user has collected some objects.…”

Section: Personal Recommendationmentioning

confidence: 99%

Bipartite network projection and personal recommendation

et al. 2007

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The one-mode projecting is extensively used to compress the bipartite networks. Since the onemode projection is always less informative than the bipartite representation, a proper weighting method is required to better retain the original information. In this article, inspired by the networkbased resource-allocation dynamics, we raise a weighting method, which can be directly applied in extracting the hidden information of networks, with remarkably better performance than the widely used global ranking method as well as collaborative filtering. This work not only provides a creditable method in compressing bipartite networks, but also highlights a possible way for the better solution of a long-standing challenge in modern information science: How to do personal recommendation?

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Section: Personal Recommendationmentioning

confidence: 99%

Bipartite network projection and personal recommendation

et al. 2007

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“…This paper continues a small stream of work in that direction. Recommenders can have a positive effect on sales and web impressions (Ansari et al 2000;Das et al 2007;Bodapati 2008;De et al 2010). For example, De et al (2010) show that recommenders positively affect sales, and they expect this to be particularly important in industries with many SKUs.…”

Section: Prior Workmentioning

confidence: 99%

“…Such personalization is valuable in modern media markets, which can have millions of products to choose from. As a result, personalization has also become a major theme of research in Information Systems (e.g., Murthi and Sarkar 2003;Dellarocas 2003;Brynjolfsson et al 2006;Clemons et al 2006) and Marketing (e.g., Ansari et al 2000;Manchanda et al 2006;Shaffer and Zhang 1995;Rossi et al 1996), with its origins in targeted and customized marketing.…”

Section: Introductionmentioning

confidence: 99%

Will the Global Village Fracture Into Tribes? Recommender Systems and Their Effects on Consumer Fragmentation

et al. 2014

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Personalization is becoming ubiquitous on the World Wide Web. Such systems use statistical techniques to infer a customer's preferences and recommend content best suited to him (e.g., "Customers who liked this also liked…"). A debate has emerged as to whether personalization has drawbacks. By making the Web hyperspecific to our interests, does it fragment Internet users, reducing shared experiences and narrowing media consumption? We study whether personalization is in fact fragmenting the online population. Surprisingly, it does not appear to do so in our study. Personalization appears to be a tool that helps users widen their interests, which in turn creates commonality with others. This increase in commonality occurs for two reasons, which we term volume and product-mix effects. The volume effect is that consumers simply consume more after personalized recommendations, increasing the chance of having more items in common. The product-mix effect is that, conditional on volume, consumers buy a more similar mix of products after recommendations. AbstractPersonalization is becoming ubiquitous on the World Wide Web. Such systems use statistical techniques to infer a customer's preferences and recommend content best suited to him (e.g., "Customers who liked this also liked…"). A debate has emerged as to whether personalization has drawbacks. By making the web hyper-specific to our interests, does it fragment internet users, reducing shared experiences and narrowing media consumption? We study whether personalization is in fact fragmenting the online population. Surprisingly, it does not appear to do so in our study. Personalization appears to be a tool that helps users widen their interests, which in turn creates commonality with others. This increase in commonality occurs for two reasons, which we term volume and product mix effects. The volume effect is that consumers simply consume more after personalized recommendations, increasing the chance of having more items in common. The product mix effect is that, conditional on volume, consumers buy a more similar mix of products after recommendations.

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“…The recommendation system is largely fall into two categories: context based filtering [1,2] and collaboration filtering [3].…”

Section: Recommendation Systemmentioning

confidence: 99%

An Intelligent Matching System for the Products of Small Business/Manufactures with the Celebrities

Jeong

Son

et al. 2016

MATEC Web of Conferences

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Abstract. With the development of ICT, e-business have brought consumers opportunities to choose a wider range of products to purchase. However, these opportunities made it difficult for them to make a choice. Under this circumstance, product recommendation systems have arisen, helping consumers with purchase decision. In this paper, an intelligent matching system is proposed to connect small business/manufactures with celebrities. Previous matching/recommendation systems using narrowing approach based on limited information or using widening approach based on users' information. But these approaches have many restricts. The proposed matching system produces suitable celebrity candidate set to advertise target product of small business/manufactures based on relationship graph which can compare the similarity of each celebrity.

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Internet Recommendation Systems

Cited by 568 publications

References 20 publications

Bipartite network projection and personal recommendation

Bipartite network projection and personal recommendation

Will the Global Village Fracture Into Tribes? Recommender Systems and Their Effects on Consumer Fragmentation

An Intelligent Matching System for the Products of Small Business/Manufactures with the Celebrities

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