Advances in Online Shopping Interfaces - RePub - Erasmus ...

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5.6 Conclusions 107 data. In an experiment, we showed that LCR-RS performs better in recommending relatively new items than collaborative filtering methods. We see some directions to future research based on the experiments with the pLSA-CF algorithm. We experienced that LCR-RS benefits from a good initialization. Therefore, we think performance of LCR-RS may be improved by a better initialization strategy, such as, initialization using pLSA-CF. Also, we expect that explanations of the LCR-RS might be improved using feature extraction or selection methods excluding correlated and insignificant characteristics. This can maybe done by integrating stepwise regression methods or lasso regression (Tibshirani, 1996). Furthermore, the LCR-RS model might be adapted to model other type of user evaluations such as a binary ‘like - don’t like’ evaluation. This can be done using generalized linear models (McCullagh & Nelder, 1989), such as logistic or probit regression, in combination with latent classes (Wedel & DeSarbo, 1995).
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MARTIJN KAGIE Advances in Online Sh
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Advances in Online Shopping Interfa
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Preface Many prefaces of doctoral d
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viii CONTENTS 3.2 Related Work . .
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Chapter 1 Introduction Over the pas
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1.1 Product Catalog Maps 3 product
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1.2 Recommender Systems 5 Table 1.2
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1.2 Recommender Systems 7 Figure 1.
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1.3 Overview of this Thesis 9 using
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Chapter 2 Map Based Visualization o
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2.2 Methods for Map Based Visualiza
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2.3 Product Catalog Maps 21 Figure
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2.3 Product Catalog Maps 23 should
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2.4 Determining Attribute Weights u
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2.5 E-Commerce Applications 27 of c
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2.5 E-Commerce Applications 29 Tabl
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2.5 E-Commerce Applications 31 Phil
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2.5 E-Commerce Applications 33 The
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2.6 Summary, Conclusions, and Outlo
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Chapter 3 A Graphical Shopping Inte
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3.2 Related Work 39 • Content-bas
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3.3 Methodology 41 implying that ob
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3.5 Graphical Shopping Interface 43
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3.5 Graphical Shopping Interface 45
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3.6 A Prototype Application to MP3
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3.6 A Prototype Application to MP3
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3.7 Evaluation of the Graphical Sho
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Page 68: 3.8 Conclusions and Discussion 57 t
Page 72 and 73: Chapter 4 Determination of Attribut
Page 74 and 75: 4.2 The Use of Dissimilarities in R
Page 76 and 77: 4.3 Determining Attribute Weights 6
Page 82 and 83: 4.4 Determining Weights for Four Re
Page 84 and 85: 4.5 Clickstream Evaluation 73 Table
Page 86 and 87: 4.5 Clickstream Evaluation 75 compu
Page 88 and 89: 4.6 User Experiment 77 Table 4.4: p
Page 90 and 91: 4.6 User Experiment 79 Table 4.5: R
Page 92 and 93: 4.7 Summary, Conclusions & Discussi
Page 94 and 95: 4.B Derivation of PoissonTreeBoost
Page 96 and 97: Chapter 5 Including Item Characteri
Page 98 and 99: 5.2 Recommender Systems 87 experime
Page 100 and 101: 5.3 Models 89 u z y (a) v Figure 5.
Page 102 and 103: 5.3 Models 91 This function is fair
Page 104 and 105: 5.3 Models 93 we can estimate user
Page 106 and 107: 5.3 Models 95 where xy is a vector
Page 108 and 109: 5.4 Data 97 standard deviation v
Page 110 and 111: Table 5.1: Item Characteristics of
Page 112 and 113: 5.5 Experiments & Results 101 Figur
Page 114 and 115: 5.5 Experiments & Results 103 Figur
Page 116 and 117: 5.5 Experiments & Results 105 Table
Page 120: Part III Visualization of Large Dat
Page 123 and 124: 112 Multidimensional Scaling for La
Page 145 and 146: 134 Discussion in Stress in the GRS
Page 147 and 148: 136 Discussion an active pool of us
Page 150 and 151: Nederlandse Samenvatting (Summary i
Page 152: Nederlandse Samenvatting 141 kan wo
Page 155 and 156: 144 Bibliography Learning Research,
Page 157 and 158: 146 Bibliography Dempster, A. P., L
Page 159 and 160: 148 Bibliography 1.1-27, available
Page 161 and 162: 150 Bibliography Ong, T.-H., Chen,
Page 163 and 164: 152 Bibliography Silva, V. de, & Te
Page 166 and 167: Author Index Adomavicius, G., 7, 38
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Author Index 157 Logan, B. F., 123,
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Author Index 159 Zielman, B., 126,
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ERASMUS RESEARCH INSTITUTE OF MANAG
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Chen, Y., Labour Flexibility in Chi
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Hessels, S.J.A., International Entr
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Le Anh, T., Intelligent Control of
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Nieuwenboer, N.A. den, Seeing the S
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Schweizer, T.S., An Individual Psyc
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Wall, R.S., Netscape: Cities and Gl
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