Temporal Database Queries for Recommender System using Temporal Logic

Recommender systems (RS) use a database about user preferences to predict additional topics or products a new user might like. These conventional recommender systems make their recommendations based on the user x item information and don't take into consideration a temporal data that may be crucial in many applications. The distinguishing feature of the temporal database is time itself When user preferences change over time these may not be sufficient to simply recommend between users and items; the recommender systems must need to take the temporal data into consideration when recommending a new product. In this paper, we present a recommender system based on the Temporal data during a specific time that make predictions about which movie a user should like given a partial list of that user's tastes. Moreover we also present the temporal database queries and recommendations using the first order temporal logic operators.