Contextual recommendation system

The traditional recommendation system usually ignored the contextual information and simply focused on the past preferences of customers. However, there are usually various factors influencing customer´s decision on what product to buy and what information to rely on in reality. Besides, customer´s demand might change with context as well. Therefore, this work proposes a contextual recommendation framework to improve this problem and provide more suitable recommendation results which could more consistent with customer´s requirement. Recommender systems are efficient tools that overcome the information overload problem by providing users with the most relevant contents. This is generally done through user´s preferences/ratings acquired from log files of his former sessions. Besides these preferences, taking into account the interaction context of the user will improve the relevancy of recommendation process. In this paper, we propose a contextual recommender system based on both user profile and context.