Online Shopping Using a Two Dimensional Product Map - CiteSeerX

Online Shopping Using a Two Dimensional Product Map Martijn Kagie, Michiel van Wezel, and Patrick J.F. Groenen Econometric Institute, Erasmus University Rotterdam, The Netherlands. {kagie,mvanwezel,groenen}@few.eur.nl

Abstract. In this paper, we propose a user interface for online shopping that uses a two dimensional product map to present products. This map is created using multidimensional scaling (MDS). Dissimilarities between products are computed using an adapted version of Gower’s coefficient of similarity based on the attributes of the product. The user can zoom in and out by drawing rectangles. We show an application of this user interface to MP3 players and give an interpretation of the product map.

1

Introduction

In most electronic commerce stores, customers can choose from an enormous number of different products within a product category. Although it is expected that increased choice is better for customer satisfaction, this is not the case [1]. This phenomenon is known as the paradox of choice. When the amount of choice options increases, customers often end up choosing an option that is further away from the product they prefer most. One reason for this phenomenon is that it is very hard to get an overview of all the products that are available. In many product categories, such as real estate and electronics, a consumer has to choose from a heterogenous range of products with a large amount of product attributes. Often, the customer first has to make a selection based on a (limited) number of constraints on product attributes, before a subset of products satisfying these constraints is shown to her. These products are usually shown in a list. A disadvantage of this approach is that customers can find these constraints too strict. In addition, product attributes can substitute each other, that is, a higher value on one attribute can compensate for a lower value on another. In this way, selection on pairs of attributes may not allow for attribute combinations that are preferred by a consumer. For example, a consumer who wants to buy an MP3 player can be equally satisfied with a cheaper MP3 player with less memory as with a more expensive MP3 player that has also more memory. Sometimes, lists of products are constructed using some measure of similarity to a query. In that case all products are shown to the user, but they are ordered by relevance. A disadvantage of the usual approach of presenting the products in a (ordered) list is that no information is given on how similar products are to each other. For example, two products that have almost the same similarity to a

2

Martijn Kagie, Michiel van Wezel, and Patrick J.F. Groenen

query can differ from this query on a completely different set of attributes and thus differ a lot from each other. Also, the systems only show or a subset of the products available or a complete list of products. In this way, it is impossible for customers to get a good overview of the complete range of products. In this paper, we propose a graphical user interface for online shopping that is based on a two dimensional map of the product space. This product map is created using multidimensional scaling (MDS) [2]. The distances in this map correspond to the dissimilaties between products: Distant products in the map are very dissimilar whereas closely products are similar. These dissimilarities between products that are the input for MDS are based on the attributes of the products. Since the product map would become very unclear, when we represent every product by a thumbnail picture, we choose to represent only a limited number of prototypical products by such an image. A clustering algorithm is used to determine these products. Furthermore, the GUI facilitates a method to zoomin on specific parts of the map. Our interface has some similarity with earlier work. Graphical applications using two dimensional maps, so-called inspiration interfaces, are used in the field of industrial design engineering [3,4,5]. These applications are used to explore databases in an interactive way. At first, a small set of items is shown in a 2D space. Then, the user can click in any point in space and a new item that is closest to that point is added to the space. In Kagie, Van Wezel, and Groenen [6], a recommender systems using 2D spaces (called graphical shopping interface) was proposed, that can be used to navigate through the product space. A small set of products is shown to the user each time. By clicking on one of the products, a new set of products more similar to this product is shown together with the selected product to the user in the 2D space. Both the inspiration interfaces and the graphical shopping interface only create maps based on subsets of products making it difficult to the user to get an overview of the complete product space. In contrast, the method we propose only uses a single product map of the complete set of products. Also in somewhat related fields like news [7,8], the web [9], music playlists [10,11], and image browsing [12] GUI’s based on 2D visualizations have been created only using different visualization techniques like self-organizing maps, classical scaling, Sammon mapping, and treemap. The remainder of this paper is organized as follows. In Sect. 2, we give a description of the methodology used to implement the user interface. In Sect. 3 we introduce the user interface and in Sect. 4 an application of the our approach on MP3 Players is given. Finally, we give conclusions and recommendations.

2

Methodology

The MDS method we use to create the product map visualizes a dissimilarity matrix, in our case dissimilarities between products, in a low dimensional space. Therefore, we first need to determine a measure of dissimilarity between prod-

Online Shopping Using a Two Dimensional Product Map

3

ucts. To this end, we introduce some notation. Consider a data set D, which contains products {xi }n1 having K attributes xi = (xi1 , xi2 . . . xiK ). For each product, we also have a binary vector mi = (mi1 , mi2 . . . miK ), containing values of 1 for nonmissing attribute values. In most applications, these attributes have mixed types, that is, the attributes can be numerical, binary, or categorical. The most often used (dis)similarity measures, like the Euclidean distance, Pearson’s correlation coefficient, and Jaccard’s similarity measure, are only suited to handle one of these attribute types. Also, these measures cannot cope with missing values in a natural way. Therefore, we use a dissimilarity measure which is based on the general coefficient of similarity proposed by Gower [13], which was introduced by Kagie et al. [6]. Although we will use this dissimilarity measure during this paper, since it has some specific advantages concerning the data set we use, the product map approach can be applied to any dissimilarity measure. This implies that this approach can also be used when no explicit attribute information is available, but only, for instance, co-purchases or item-item rating correlations. The dissimilarity δij between products i and j is defined as the square root of the average of nonmissing dissimilarity scores δijk on the K attributes. When we let C be the set of categorical attributes and N be the set of numerical attributes, we can write the dissimilarity as v  u P P C N u k∈C mik mjk δijk + k∈N mik mjk δijk t . δij = PK k=1 mik mjk

(1)

The computation of the dissimilarity score is dependent on the type of the attribute. For numerical attributes the dissimilarity score is the normalized absolute distance |xik − xjk | . −1 P m m m m |x − x | ik jk ik jk ik jk i