An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering

• An automated and effective approach to combine results from RS-techniques.
• The cost reduction for recommender systems design.
• A flexible method for different application scenarios.
• A customizable method able to combine many different RS-techniques’ results.

Recommender systems (RS) are often used as guides, helping users to discover products of their interest. Many techniques and approaches to generate an effective recommendation are available for the system designers. On the one hand, this is interesting because different application’s scenarios could have a fittest solution but on the other it can also cause some complexity to select the best technique to address at each state of the database. Thus, choose the best technique for each new state becomes too difficult and frequent for manually select. One of big challenges on RS is turn the techniques more useful for real-world scenarios. Therefore, automate or help the design decision is an important task to improve the usability of RS and reduce its cost. Although many works aims to improve the performance of RS for some scenarios, just a few of them try to help the designers on selection or combination of the techniques through applications’ state changes. Therefore, this work proposes an evolutionary approach, called Invenire, to automate the choice of techniques used by combining results of different recommendation techniques. This is a new approach that uses a search algorithm to optimize the techniques combination, and can inspire hybrid methods and expert systems on how automate them. To evaluate the proposal, experiments were performed with a dataset from MovieLens and different collaborative filtering approaches. The results obtained show that the Invenire outperforms all collaborative filtering approach separately in all contexts addressed. The improvement achieved varies from 3.6% to 118.99% depending on the combination encountered and the experiment executed. Thus, the proposal was able to increase the accuracy on the generated recommendations and automate the combinations of techniques.