Martijn Kagie

A graphical shopping interface based on product attributes

Most recommender systems present recommended products in lists to the user. By doing so, much information is lost about the mutual similarity between recommended products. We propose to represent the mutual similarities of the recommended products in a two dimensional map, where similar products are located close to each other and dissimilar products far apart. As a dissimilarity measure we use an adaptation of Gowers similarity coefficient based on the attributes of a product. Two recommender systems are developed that use this approach. The first, the graphical recommender system, uses a description given by the user in terms of product attributes of an ideal product. The second system, the graphical shopping interface, allows the user to navigate towards the product she wants. We show a prototype application of both systems to MP3-players.

Including item characteristics in the probabilistic latent semantic analysis model for collaborative filtering

We propose a new hybrid recommender system that combines some advantages of collaborative and content-based recommender systems. While it uses ratings data of all users, as do collaborative recommender systems, it is also able to recommend new items and provide an explanation of its recommendations, as do content-based systems. Our approach is based on the idea that there are communities of users that find the same characteristics important to like or dislike a product. This model is an extension of the probabilistic latent semantic model for collaborative filtering with ideas based on clusterwise linear regression. On a movie data set, we show that the model is competitive to other recommenders and can be used to explain the recommendations to the users.

Choosing attribute weights for item dissimilarity using clikstream data with an application to a product catalog map

In content- and knowledge-based recommender systems often a measure of (dis)similarity between items is used. Frequently, this measure is based on the attributes of the items. However, which attributes are important for the users of the system remains an important question to answer. In this paper, we present an approach to determine attribute weights in a dissimilarity measure using clickstream data of an e-commerce website. Counted is how many times products are sold and based on this a Poisson regression model is estimated. Estimates of this model are then used to determine the attribute weights in the dissimilarity measure. We show an application of this approach on a product catalog of MP3 players provided by Compare Group, owner of the Dutch price comparison site http://www.vergelijk.nl, and show how the dissimilarity measure can be used to improve 2D product catalog visualizations.

Online shopping using a two dimensional product map

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 Gowers 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.

A Graphical Shopping Interface Based on Product Characteristics

Most recommender systems present recommended products in lists to the user. By doing so, much information is lost about the mutual similarity between recommended products. We propose a graphical shopping interface, which represents the mutual similarities of the recommended products in a two dimensional space, where similar products are located close to each other and dissimilar products far apart. The graphical shopping interface can be used to navigate through the complete product space in a number of steps. We will show a prototype application of the system for MP3-players.