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This article describes an approach to learn feature weights for content-based image retrieval (CBIR) from user interaction log files. These usage log files are analyzed for images marked together in the same query step. The problem is somewhat similar to one of the traditional data mining problems, the market basket analysis problem, where items bought together in a supermarket are analyzed. This paper outlines similarities and differences between the two fields and explains how to use the interaction data for deriving a better feature weighting. Experiments with existing log files are done and a significant improvement in performance is reached with a feature weighting calculated from the information contained in the log files. Even with several steps of relevance feedback the results remain much better than without the learning, which means that not only information from feedback is taken into account earlier, but a better quality of retrieval is reached in all steps.