USING SEMANTIC CUES FOR CONTEXTUAL RECOMMENDATION

Recommender systems help users overcome the information overload problem and have been widely used in an ever-increasing number of e-commerce websites. However, most existing recommender systems use simplistic user model. In this paper, we describe how context can be brought to bear on recommender systems. We propose a new approach to integrating user rating vectors with a contextual retrieval cue to generate recommendations. We use a user model consisting of short and long term memory with context playing the role of retrieval and we use semantic information extracted from the domain knowledge as the key cue for distinguishing user context. An evaluation of our recommendation algorithm was carried out using Movielens data by extending standard collaborating filtering algorithm using semantic cues.