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In this work, we apply a clustering technique to integrate the contents of items into the item-based collaborative filtering framework. The group rating information that is obtained from the clustering result provides a way to introduce content information into collaborative recommendation and solves the cold start problem. Extensive experiments have been conducted on MovieLens data to analyze the characteristics of our technique. The results show that our approach contributes to the improvement of prediction quality of the item-based collaborative filtering, especially for the cold start problem.

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We investigated the relationship between mood and color according to musical preference. Volunteers were requested to select the mood evoked by music and the color associated with a mood word, and the color distributions and mood distributions were then analyzed with two‐way ANOVA using Minitab. The results of the analysis indicated that mood and color were differently distributed, depending on the music genre presented and the musical preference of the listener. © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 413–423, 2014

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