Recommender Systems on the Web: A Model-Driven Approach

Recommendation techniques have been increasingly incorporated in e-commerce applications, supporting clients in identifying those items that best fit their needs. Unfortunately, little effort has been made to integrate these techniques into methodological proposals of Web development, discouraging the adoption of engineering approaches to face the complexity of recommender systems. This paper introduces a proposal to develop Web-based recommender systems from a model-driven perspective, specifying the elements of recommendation algorithms from a high abstraction level. Adopting the item-to-item approach, this proposal adopts the conceptual models of an existing Web development process to represent the preferences of users for different items, the similarity between obtained from different algorithms, and the selection and ordering of the recommended items according to a predicted rating value. Along with systematizing the development of these systems, this approach permits to evaluate different algorithms with minor changes at conceptual level, simplifying their mapping to final implementations.