Articoli correlati a Optimized Ranking-Based Techniques for Improving Aggregate...
Sinossi
Master's Thesis from the year 2013 in the subject Computer Science - Miscellaneous, grade: 1, , course: ME computer science, language: English, abstract: This paper investigates how demand-side factors contribute to the Internet's "Long Tail" phenomenon. It first models how a reduction in search costs will affect the concentration in product sales. Then, by analyzing data collected from a multi-channel retailing company, it provides empirical evidence that the Internet channel exhibits a significantly less concentrated sales distribution, when compared with traditional channels. The difference in the sales distribution is highly significant, even after controlling for consumer differences. Furthermore, the effect is particularly strong for individuals with more prior experience using the Internet channel. We find evidence that Internet purchases made by consumers with prior Internet experience are more skewed toward obscure products, compared with consumers who have no such experience. We observe the opposite outcome when comparing purchases by the same consumers through the catalog channel. If the relationships we uncover persist, the underlying trends in technology and search costs portend an ongoing shift in the distribution of product sales. Singular Value Decomposition (SVD), together with the Expectation-Maximization (EM) procedure, can be used to find a low-dimension model that maximizes the log likelihood of observed ratings in recommendation systems. However, the computational cost of this approach is a major concern, since each iteration of the EM algorithm requires a new SVD computation. We present a novel algorithm that incorporates SVD approximation into the EM procedure to reduce the overall computational cost while maintaining accurate predictions. Furthermore, we propose a new framework for collaborating filtering in distributed recommendation systems that allows users to maintain their own rating profiles for privacy. We conduct offline and online t
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
- EditoreGRIN Verlag
- Data di pubblicazione2013
- ISBN 10 3656563241
- ISBN 13 9783656563242
- RilegaturaCopertina flessibile
- LinguaInglese
- Numero edizione1
- Numero di pagine20
Compra nuovo
Visualizza questo articolo
EUR 29,80
EUR 3,57 per la spedizione in U.S.A.
Destinazione, tempi e costiRisultati della ricerca per Optimized Ranking-Based Techniques for Improving Aggregate...
Foto dell'editore
Optimized Ranking-Based Techniques for Improving Aggregate Recommendation Diversity
Da: Books Puddle, New York, NY, U.S.A.
Valutazione del venditore 4 su 5 stelle
Condizione: New. pp. 20. Codice articolo 26127602453
Quantità: 4 disponibili
Foto dell'editore
Optimized Ranking-Based Techniques for Improving Aggregate Recommendation Diversity
Da: California Books, Miami, FL, U.S.A.
Valutazione del venditore 5 su 5 stelle
Condizione: New. Codice articolo I-9783656563242
Quantità: Più di 20 disponibili
Foto dell'editore
Optimized Ranking-Based Techniques for Improving Aggregate Recommendation Diversity
Print on DemandDa: Majestic Books, Hounslow, Regno Unito
Valutazione del venditore 5 su 5 stelle
Condizione: New. Print on Demand pp. 20. Codice articolo 132952266
Quantità: 4 disponibili
Immagini fornite dal venditore
Optimized Ranking-Based Techniques for Improving Aggregate Recommendation Diversity
Print on DemandDa: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Valutazione del venditore 5 su 5 stelle
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Master's Thesis from the year 2013 in the subject Computer Science - Miscellaneous, grade: 1, , course: ME computer science, language: English, abstract: This paper investigates how demand-side factors contribute to the Internet's 'Long Tail' phenomenon. It first models how a reduction in search costs will affect the concentration in product sales. Then, by analyzing data collected from a multi-channel retailing company, it provides empirical evidence that the Internet channel exhibits a significantly less concentrated sales distribution, when compared with traditional channels. The difference in the sales distribution is highly significant, even after controlling for consumer differences. Furthermore, the effect is particularly strong for individuals with more prior experience using the Internet channel. We find evidence that Internet purchases made by consumers with prior Internet experience are more skewed toward obscure products, compared with consumers who have no such experience. We observe the opposite outcome when comparing purchases by the same consumers through the catalog channel. If the relationships we uncover persist, the underlying trends in technology and search costs portend an ongoing shift in the distribution of product sales. Singular Value Decomposition (SVD), together with the Expectation-Maximization (EM) procedure, can be used to find a low-dimension model that maximizes the log likelihood of observed ratings in recommendation systems. However, the computational cost of this approach is a major concern, since each iteration of the EM algorithm requires a new SVD computation. We present a novel algorithm that incorporates SVD approximation into the EM procedure to reduce the overall computational cost while maintaining accurate predictions. Furthermore, we propose a new framework for collaborating filtering in distributed recommendation systems that allows users to maintain their own rating profiles for privacy. We conduct offline and online tests of our ranking algorithm. We use Yahoo! Search queries that resulted in a click on a Yahoo! Movies or Internet Movie Database (IMDB) movie URL. Our online test involved 44 Yahoo! Employees providing subjective assessments of results quality. In both tests, our ranking methods show significantly better recall and quality than IMDB search and Yahoo! Movies current search. Reduced rank approximation of matrices has hitherto been possible only by un-weighted least squares. This paper presents iterative techniques for obtaining such approximations when weights are introduced. 20 pp. Englisch. Codice articolo 9783656563242
Quantità: 2 disponibili
Foto dell'editore
Optimized Ranking-Based Techniques for Improving Aggregate Recommendation Diversity
Da: Biblios, Frankfurt am main, HESSE, Germania
Valutazione del venditore 5 su 5 stelle
Condizione: New. Codice articolo 18127602463
Quantità: 4 disponibili
Immagini fornite dal venditore
Optimized Ranking-Based Techniques for Improving Aggregate Recommendation Diversity
Da: AHA-BUCH GmbH, Einbeck, Germania
Valutazione del venditore 5 su 5 stelle
Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Master's Thesis from the year 2013 in the subject Computer Science - Miscellaneous, grade: 1, , course: ME computer science, language: English, abstract: This paper investigates how demand-side factors contribute to the Internet's 'Long Tail' phenomenon. It first models how a reduction in search costs will affect the concentration in product sales. Then, by analyzing data collected from a multi-channel retailing company, it provides empirical evidence that the Internet channel exhibits a significantly less concentrated sales distribution, when compared with traditional channels. The difference in the sales distribution is highly significant, even after controlling for consumer differences. Furthermore, the effect is particularly strong for individuals with more prior experience using the Internet channel. We find evidence that Internet purchases made by consumers with prior Internet experience are more skewed toward obscure products, compared with consumers who have no such experience. We observe the opposite outcome when comparing purchases by the same consumers through the catalog channel. If the relationships we uncover persist, the underlying trends in technology and search costs portend an ongoing shift in the distribution of product sales. Singular Value Decomposition (SVD), together with the Expectation-Maximization (EM) procedure, can be used to find a low-dimension model that maximizes the log likelihood of observed ratings in recommendation systems. However, the computational cost of this approach is a major concern, since each iteration of the EM algorithm requires a new SVD computation. We present a novel algorithm that incorporates SVD approximation into the EM procedure to reduce the overall computational cost while maintaining accurate predictions. Furthermore, we propose a new framework for collaborating filtering in distributed recommendation systems that allows users to maintain their own rating profiles for privacy. We conduct offline and online tests of our ranking algorithm. We use Yahoo! Search queries that resulted in a click on a Yahoo! Movies or Internet Movie Database (IMDB) movie URL. Our online test involved 44 Yahoo! Employees providing subjective assessments of results quality. In both tests, our ranking methods show significantly better recall and quality than IMDB search and Yahoo! Movies current search. Reduced rank approximation of matrices has hitherto been possible only by un-weighted least squares. This paper presents iterative techniques for obtaining such approximations when weights are introduced. Codice articolo 9783656563242
Quantità: 1 disponibili
Immagini fornite dal venditore
Optimized Ranking-Based Techniques for Improving Aggregate Recommendation Diversity
Print on DemandDa: preigu, Osnabrück, Germania
Valutazione del venditore 5 su 5 stelle
Taschenbuch. Condizione: Neu. Optimized Ranking-Based Techniques for Improving Aggregate Recommendation Diversity | Saravana Kumar (u. a.) | Taschenbuch | 20 S. | Englisch | 2013 | GRIN Verlag | EAN 9783656563242 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand. Codice articolo 105503185
Quantità: 5 disponibili