A Visited Item Frequency Based Recommender System: Experimental Evaluation and Scenario Description
There has been a continuous development of new clustering and prediction techniques that help customers select products that meet their preferences and/or needs from an overwhelming amount of available choices. Because of the possible huge amount of available data, existing Recommender Systems showing good results might be difficult to implement and may require a lot of computational resources to perform in this scenario. In this paper, we present a more simple recommender system than the traditional ones, easy to implement, and requiring a reasonable amount of resources to perform. This system clusters users according to the frequency an item has been visited by users belonging to the same cluster, performing a collaborative filtering scheme. Experiments were conducted to evaluate the accuracy of this method using the Movielens dataset. Results obtained, as measured by the F-measure value, are comparable to other approaches found in the literature which are far more complex to implement. Following this, we explain the application of this system to an e-content site scenario for advertising. In this context, a filtering tool is shown which has been developed to filter and contextualize recommended items.
recommender system clustering collaborative filtering f-measure e-content advertising tf-idf