Cross-domain collaborative filtering (CDCF) is an evolving research topic in the modern recommender systems. Its main objective is to alleviate the data sparsity problem in individual domains by mixing and transferring the knowledge among the related domains. But there is also an issue of user interest drift over time because user’s taste keeps on changing over time. We should consider various temporal domains to overcome user interest drift over time problem to predict more accurately as per the user’s current interest. This paper discusses how to achieve effective ratings recommendations using contextual parameters in temporal domain in this research line. It calculates the contextual parameters as per user’s current timestamp. This will enhance the recommendations more in line with the current temporal domain. It also deals with cross domain recommendations for both movies and novels based on their categories and similarities.
Title = "Enhanced Cross Domain Recommender System using Contextual parameters in Temporal Domain",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "6",
Pages ="349 - 409",
Year = "2017",
Authors ="Swapna Joshi , Prof. Manisha Patil"}
The paper proposes effective recommender system using cross domain approach
Various contextual parameters are applied as events for current user context
Movies, books and Wiki are the three 3 related cross domains are used
Performance evaluation is calculated based on RMSE and MAE parameters.