Building Recommender Systems using a Knowledge Base of Product Semantics

Online retailers have access to large amounts of transactional data but current recommender systems tend to be short-sighted in nature and usually focus on the narrow problem of pushing a set of closely related products that try to satisfy the user’s current need. Most ecommerce recommender systems analyze a large amount of transactional data without actually having any idea of what the items in the transactions mean or what they say about the customers who purchased or browsed those items. In this paper, we present a case study of a system that recommends items based on a custom-built knowledge base that consists of products and associated semantic attributes. Our system first extracts semantic features that characterize the domain of interest, apparel products in our case, using text learning techniques and populates a knowledge base with these products and features. The recommender system analyzes descriptions of products that the user browses or buys and automatically infers these semantic attributes to build a model of the user. This abstraction allows us to not only recommend other items in the same class of products that ”match” the user model but also gives us the ability to understand the customer’s ”tastes” and recommend items across categories for which traditional collaborative filtering and contentbased systems are unsuitable. Our approach also allows us to ”explain” the recommendations in terms of qualitative features which, we believe, enhances the user experience and helps build the user’s confidence in the recommendations.