Click Prediction for Web Image Reranking Using Multimodal Sparse Coding
Abstract of Click Prediction for Web Image Reranking
However, a critical problem for click-based methods is the lack of click data, since only a small number of web images have actually been clicked on by users.
Therefore, we aim to solve this problem by predicting image clicks. We propose a multimodal hypergraph learning-based sparse coding method for image click prediction, and apply the obtained click data to the reranking of images.
Unlike a graph that has an edge between two vertices, a hyperedge in a hypergraph connects a set of vertices, and helps preserve the local smoothness of the constructed sparse codes.
An alternating optimization procedure is then performed, and the weights of different modalities and the sparse codes are simultaneously obtained.
Conclusion
In this paper we propose a new multimodal hypergraph learning based sparse coding method for the click prediction of images.
The obtained sparse codes can be used for image re-ranking by integrating them with a graph-based schema.
This helps preserve the local smoothness of the constructed sparse codes. Finally, a voting strategy is used to predict the click from the corresponding sparse code. Experimental results on real-world data sets have demonstrated that the proposed method is effective in determining click prediction. Additional experimental results on image re-ranking suggest that this method can improve the results returned by commercial search engines.
