| 摘要 |
A method and apparatus are disclosed for recommending items of interest to a user, such as television program recommendations, before a viewing history or purchase history of the user is available. A third party viewing or purchase history is processed to generate stereotype profiles that reflect the typical patterns of items selected by representative viewers. A user can select the most relevant stereotype(s) from the generated stereotype profiles and thereby initialize his or her profile with the items that are closest to his or her own interests. A clustering routine partitions the third party viewing or purchase history (the data set) into clusters using a k-means clustering algorithm, such that points (e.g., television programs) in one cluster are closer to the mean of that cluster than any other cluster. A mean computation routine computes the symbolic mean of a cluster. For an item -based mean computation, the distance computation between two items is performed on the item level and the resultant cluster mean is made up of the feature values of the selected mean item. Thus, the one or more items that exhibit the minimum variance are selected as the mean of that cluster.
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