Automatic recommendation of products using latent semantic indexing of content

Abstract

Techniques for using latent semantic structure of textual content ascribed to the items to provide automatic recommendations to the user. A user inputs a selected item and, in turn, a latent semantic algorithm is applied to the user selection and the textual content of the items in a database to generate a conceptual similarity between the selection and the items. A set of nearest items to the selected item is provided as a recommendation to the user of other items that may be of particular interest or relevance to the user's original selection based upon the conceptual similarity measure.

Description

BACKGROUND OF THE DISCLOSURE[0001] 1. Field of the Invention[0002] This invention relates generally to a procedure for selecting a product by a customer and, more particularly, to methodologies and concomitant circuitry for using latent semantic structure of content ascribed to the products to provide automatic recommendations to the customer.[0003] 2. Description of the Background[0004] There are two threads of pertinent subject matter which serve as points of departure for the present invention, namely: (1) work in manipulating personal preferences for recommendations of items; and (2) work in using relevance feedback in information retrieval tasks for items.[0005] The current state of the art with respect to item (1) above is composed of two techniques for providing recommendations. The first is to use a domain expert to handcraft recommendations for a specific item. In this technique, an expert proceeds through a series of items, and notates for each item which additional items ...

AI Technical Summary

Problems solved by technology

This technique is laborious and is not automatic; for instance, when a new item is introduced, the expert must be consulted again to generate recommendations for the new item.

Also, in situations with large sets of items, it becomes less likely that any expert would be familiar with all the items, and so would be less likely to produce a correctly tailored list for all the items that need recommendations.

While this is possible to consider in the case of a small number of sets, an expert will be quickly overwhelmed in any attempt to provide a comprehensive set of recommendations given the large number of possible combinations of items.

There are a number of situations in which using preference data does not generate reasonable recommendations.

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