PerRank: Personalized Rank Retrieval with Categorical and Numerical Attributes

Abstract

Ranking has been popularly used for intelligent data retrieval in both database and machine learning communities. Recently, there were studies on integrating these two approaches to support soft queries, based on a user's sense of relevance and preference, for ranking with numerical attributes. However, in real life, it is desirable to use categorical attributes together with numerical ones in ranking. For example, when buying a car, categorical attributes, such as make, model, color, and equipments, are considered as significant factors as numerical attributes, such as price and year. Meanwhile, users often do not have sufficient domain knowledge at formulating an effective selection query over categories, whereas rank formulation is even more challenging as categories have no inherent ordering. In this paper, we propose a framework PerRank (Personalized Ranking with Categorical and Numerical Attributes) to support personalized ranking with both categorical and numerical attributes for soft queries. For an efficient computation, we developed an algorithm CAC (Clustering-based Attribute Construction) which makes use of a clustering method. Extensive experiments show CAC is effective and efficient at supporting ranking with both categorical and numerical attributes for soft queries.