D-CARS: A Declarative Context-Aware Recommender System

Abstract

Context-aware recommendation has emerged as perhaps the most popular service over online sites, and has seen applications to domains as diverse as entertainment, e-business, e-health and government services. There has been recent significant progress on the quality and scalability of recommender systems. However, we believe that different target users concern different contexts when they select an online item, which can greatly affect the quality of recommendation, and have not been investigated yet. In this paper, we propose a new type of recommender system, Declarative Context-Aware Recommender System (D-CARS), which enables the personalization of the contexts exploited for each target user by automatically analysing the viewing history of users. First, we propose a novel User-Window Non-negative Matrix Factorization topic model (UW-NMF) that adaptively identifies the significant contexts of users and constructs user profiles in a personalized manner. Then, we design a novel declarative context-aware recommendation algorithm that exploits the user context preference to identify a group of item candidates and its context distribution, based on a Subspace Ensemble Tree Model (SETM), which is constructed in the identified context subspace for item recommendation. Finally, we propose an algorithm that incrementally maintains our SETM model. Extensive experiments are conducted to prove the high effectiveness and efficiency of our D-CARS system.