Improving Collaborative Filtering-Based Image Recommendation through Use of Eye Gaze Tracking

Due to the overwhelming variety of products and services currently available on electronic commerce sites, the consumer finds it difficult to encounter products of preference. It is common that product preference be influenced by the visual appearance of the image associated with the product. In this context, Recommendation Systems for products that are associated with Images (IRS) become vitally important in aiding consumers to find those products considered as pleasing or useful. In general, these IRS use the Collaborative Filtering technique that is based on the behaviour passed on by users. One of the principal challenges found with this technique is the need for the user to supply information concerning their preference. Therefore, methods for obtaining implicit information are desirable. In this work, the author proposes an investigation to discover to which extent information concerning user visual attention can aid in producing a more precise IRS. This work proposes therefore a new approach, which combines the preferences passed on from the user, by means of ratings and visual attention data. The experimental results show that our approach exceeds that of the state of the art.