User Profiling from Reviews for Accurate Time-Based Recommendations

Recommender systems are a valuable way to engage users in a system, increase participation and show them resources they may not have found otherwise. One significant challenge is that user interests may change over time and certain items have an inherently temporal aspect. As a result, a recommender system should try and take into account the time-dependant user-item relationships. However, temporal aspects of a user profile may not always be explicitly available and so we may need to infer this information from available resources. Product reviews on sites, such as Amazon, represent a valuable data source to understand why someone bought an item and potentially who the item is for. This information can then be used to construct a dynamic user profile. In this paper, we demonstrate utilising reviews to extract temporal information to infer the \textit{age category preference} of users, and leverage this feature to generate time-dependent recommendations. Given the predictable and yet shifting nature of age and time, we show that, recommendations generated using this dynamic aspect lead to higher accuracy compared with techniques from state of art. Mining temporally related content in reviews can enable the recommender to go beyond finding similar items or users to potentially predict a future need of a user.