Most of the published approaches to collaborative filtering and recommender systems concentrate on mathematical approaches for identifying user / item preferences. This paper demonstrates that by considering the psychological decision making processes that are being undertaken by the users of the system it is possible to achieve a significant improvement in results. This approach is applied to the Netflix dataset and it is demonstrated that it is possible to achieve a score better than the Cinematch score set at the beginning of the Netflix competition without even considering individual preferences for individual movies. The result has important implications for both the design and the analysis of the data from collaborative filtering systems.
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