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.