A recommender system based on invasive weed optimization algorithm

Recommender systems intend to help users find their interested items from among a large number of items. We continue our previous work that emphasizes on "prioritized user-profile" approach as an effective approach to increase the quality of the recommendations. Prioritized user-profile is an approach that tries to implement more personalized recommendation by assigning different priority importance to each of the features of the user-profile for different users. In order to find the optimal priorities for each user an optimization algorithm is needed. In this paper, we employ a new optimization algorithm namely Invasive Weed Optimization (IWO) for this purpose. IWO is a relatively new and simple algorithm inspired from the invasive habits of growth of weeds in nature. Experimental results showed that IWO achieved the best accuracy in predicting users' interests compared to two other prioritized approaches which were based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) and to standard user-based Pearson algorithm on a movie dataset.