Abstract: Customer interactions with one or more pages associated with an online marketplace may be aggregated into historical navigational patterns, which may be associated with one or more items available for purchase at the online marketplace. Where a series or sequence of customer interactions is subsequently received from a customer, such interactions may also be aggregated into a pattern, which may be compared to one or more of the historical navigational patterns. An item recommendation may be identified for the customer based at least in part on the comparison of the received navigational pattern to one or more of the historical navigational patterns. The item recommendation may include an item previously purchased following one of the historical navigational patterns, or an item related to one or more such items.

Abstract: A method of categorizing data points is described which, when combined with a clustering algorithm, provides groupings of data points that have an improved confidence interval. The method can be used to find an optimal number of groupings for a dataset, which in turn allows a user to categorize a group of data points for processing. In some examples, a dataset containing a number of data points may be accessed. Additionally, in some aspects, groupings of data points within the dataset may be grouped based at least in part on similarities between the data. Further, a number of groupings of data points may be adjusted so that the distance between the data points within one or more groupings of data points may fit within a confidence level.

Abstract: Customer interactions with one or more pages associated with an online marketplace may be aggregated into historical navigational patterns, which may be associated with one or more items available for purchase at the online marketplace. Where a series or sequence of customer interactions is subsequently received from a customer, such interactions may also be aggregated into a pattern, which may be compared to one or more of the historical navigational patterns. An item recommendation may be identified for the customer based at least in part on the comparison of the received navigational pattern to one or more of the historical navigational patterns. The item recommendation may include an item previously purchased following one of the historical navigational patterns, or an item related to one or more such items.

Abstract: A method of categorizing data points is described which, when combined with a clustering algorithm, provides groupings of data points that have an improved confidence interval. The method can be used to find an optimal number of groupings for a dataset, which in turn allows a user to categorize a group of data points for processing. In some examples, a dataset containing a number of data points may be accessed. Additionally, in some aspects, groupings of data points within the dataset may be grouped based at least in part on similarities between the data. Further, a number of groupings of data points may be adjusted so that the distance between the data points within one or more groupings of data points may fit within a confidence level.