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

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2.5 E-Commerce Applications 29 Table 2.3: Weights determined using stepwise Poisson regression for the MP3 player catalog. Only selected attributes are shown Attribute Weight Brand 0.242 Memory Size 0.176 Audio Formats 0.131 Battery Type 0.106 Width 0.090 Operating System 0.086 Color 0.084 Storage Type 0.084 this prototype is shown in Figure 2.5. The GUI of the prototype mainly consists of a large focus map which gives a detailed view of a part of the complete product catalog map. In this map, a couple of products, in this case 12, are represented by a larger full color image, the other products are visualized as smaller monochrome images. By default, the monochrome images are colored according to the cluster they belong to. Alternatively, the user has the option to color them by product popularity (based on the clickstream data) or by attribute value. The small overview map at the top always shows the complete product catalog map. The user can navigate over the map in several ways. By selecting a rectangular subspace in the focus or overview map, the user can zoom in on a specific part of the map. Zooming is also possible using the mouse wheel. Finally, the user can move over the map using dragging. Since the map itself is static, that is, the coordinates of products are fixed, the computation of the map can be done offline once, decreasing online computation time. Of course, it is straightforward to add traditional search technology, such as performing crisp queries, to the map based interfaces. All remaining products satisfying the constraints can then be displayed using a map. This would require the map to adapt when the product set changes and in this case client computations may be necessary. The map of the prototype has been made using the attribute weight determination technique described in Section 2.4. These weights were determined using the data and product catalog described above. To be able to impute the missing values, we excluded attributes having more than 50% missing values and categories of (multi-valued) categorical attributes that were observed less than 10 times. The weights determined in this way are shown in Table 2.3. Note that attributes not mentioned in the table have a weight of zero and, hence, have no influence in the MDS procedure. According to our method, Brand and Memory Size are the most important attributes determining popularity of MP3 players and receive the highest weights. In Figure 2.6, we labeled the product points of both a map made by MDS using equal weights for all attributes and a map made using our weighting approach by their corresponding Brand. The second map was rotated using Procrustean transformation (Green, 1952) to best match the first map. As can be seen in Figure 2.6, the products having the same brand are more clustered in the second map where brand had been made more important. Figure 2.7a shows the second most important attribute Memory Size. This map shows that
30 Map Based Visualization of Product Catalogs Figure 2.5: Screenshot of the MDS based product catalog map prototype. the MP3 players are generally ordered from cheap at top left to expensive at the bottom right in the map. The most striking attribute absent in Table 2.3 and, thus, receiving a weight of zero, was Price. However, when we look at Figure 2.7b where we have made a map labeled by Price, there is a clear pattern in the map. Both effects exist, since Price highly correlates with other features of MP3 players, such as the Memory Size. These features are, hence, the features explaining most of the price that is given to a product. More generally, we can see that the MDS produces a kind of circular shaped map with some outliers that represent products very different from all other ones. Due to this, large parts of the map are unused. On the other hand, the map provides at first sight a good interpretation, where the important attributes show a clear pattern.
Page 1 and 2: MARTIJN KAGIE Advances in Online Sh
Page 4 and 5: Advances in Online Shopping Interfa
Page 6: Preface Many prefaces of doctoral d
Page 9 and 10: viii CONTENTS 3.2 Related Work . .
Page 12 and 13: Chapter 1 Introduction Over the pas
Page 14 and 15: 1.1 Product Catalog Maps 3 product
Page 16 and 17: 1.2 Recommender Systems 5 Table 1.2
Page 18 and 19: 1.2 Recommender Systems 7 Figure 1.
Page 20: 1.3 Overview of this Thesis 9 using
Page 24 and 25: Chapter 2 Map Based Visualization o
Page 26 and 27: 2.2 Methods for Map Based Visualiza
Page 32 and 33: 2.3 Product Catalog Maps 21 Figure
Page 34 and 35: 2.3 Product Catalog Maps 23 should
Page 36 and 37: 2.4 Determining Attribute Weights u
Page 38 and 39: 2.5 E-Commerce Applications 27 of c
Page 42 and 43: 2.5 E-Commerce Applications 31 Phil
Page 44 and 45: 2.5 E-Commerce Applications 33 The
Page 46 and 47: 2.6 Summary, Conclusions, and Outlo
Page 48 and 49: Chapter 3 A Graphical Shopping Inte
Page 50 and 51: 3.2 Related Work 39 • Content-bas
Page 52 and 53: 3.3 Methodology 41 implying that ob
Page 54 and 55: 3.5 Graphical Shopping Interface 43
Page 56 and 57: 3.5 Graphical Shopping Interface 45
Page 58 and 59: 3.6 A Prototype Application to MP3
Page 60 and 61: 3.6 A Prototype Application to MP3
Page 62 and 63: 3.7 Evaluation of the Graphical Sho
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
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4.6 User Experiment 79 Table 4.5: R
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4.7 Summary, Conclusions & Discussi
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4.B Derivation of PoissonTreeBoost
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Chapter 5 Including Item Characteri
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5.2 Recommender Systems 87 experime
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5.3 Models 89 u z y (a) v Figure 5.
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5.3 Models 91 This function is fair
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5.3 Models 93 we can estimate user
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5.3 Models 95 where xy is a vector
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5.4 Data 97 standard deviation v
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Table 5.1: Item Characteristics of
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5.5 Experiments & Results 101 Figur
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5.5 Experiments & Results 103 Figur
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5.5 Experiments & Results 105 Table
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5.6 Conclusions 107 data. In an exp
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Chapter 6 Multidimensional Scaling
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6.2 MDS by Iterative Majorization 1
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6.3 Sparse Data 115 procedure SMACO
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6.3 Sparse Data 117 When using this
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6.4 Large Scale MDS 119 Figure 6.4:
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6.5 Choosing Dissimilarity Measures
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6.5 Choosing Dissimilarity Measures
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6.6 Applications 125 6.6 Applicatio
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6.6 Applications 127 Figure 6.11: H
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6.7 Conclusions 129 Distance Dissim
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6.7 Conclusions 131 Figure 6.14: Pa
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Chapter 7 Discussion In this chapte
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7.2 Directions for Future Research
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7.2 Directions for Future Research
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140 Summary in Dutch beide winkelom
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Bibliography Adomavicius, G., & Tuz
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Bibliography 145 User evaluations o
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Bibliography 147 Gower, J. C. (1971
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Bibliography 149 95(1), 49-69. Koho
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Bibliography 151 recommendation. In
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Bibliography 153 Menlo Park, CA, US
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156 Author Index Friedman, J. H., 3
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158 Author Index Schwab, I., 62, 15
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Curriculum Vitae Martijn Kagie (198
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Bispo, A., Labour Market Segmentati
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Gertsen, H.F.M., Riding a Tiger wit
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Jong, C. de, Dealing with Derivativ
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Meer, J.R. van der, Operational Con
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Puvanasvari Ratnasingam, P., Intero
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Tims, B., Empirical Studies on Exch
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Erasmus Research Institute of Manag
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