Human-Computer Interaction: Communication, Cooperation, and Application Design

By Hans-Jörg Bullinger; Jürgen Ziegler | Go to book overview
Other users' comments on items are presented with the commentators' tastes visualized as the color patterns of their ratings.
Figure 1 is a simplified illustration of a map, which contains two major clusters of nodes with high rating, A and B, and an outlying node P. The clusters indicate that most of the user's interests fall into two categories. If the user is interested in one of these categories more than the other, he or she can browse the recommended items around the cluster. If the user wants something different from the recommendation, the area around the node P can be a starting point for exploration. In a statistical viewpoint, P is regarded as an outlier to be excluded. In the user's viewpoint, however, the fact that this node lies far from the clusters can be a clue to a new interesting category.
an item with high rating a recommended item a less recommendable item Figure 1. Color pattern of a map

4 Highlighting by Similarity
The layout of the nodes gives only approximated information on the actual semantic distances. To represent more detailed relationship between the nodes, we introduced a dynamic filter that highlights nodes similar to specified nodes. The user can browse details of the similarity space by selecting nodes and controlling the threshold of similarity:
By selecting a known item (i.e., items with the user's rating), the user can dynamically extract similar items from the map.

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