Recommendation System Based on Singular Value Decomposition and Multi-Objective Immune Optimization

Recommendation system plays a significant role in helping people to get effective information from mass data. Traditional recommendation systems focus on the recommendation accuracy, which is not sufficient. In this paper, we also consider the various needs of users to achieve more diverse recommendation. However, accuracy and diversity are two conflicting goals for recommendation system. Hence, we model the recommendation system as a multi-objective optimization problem, and aim to find tradeoff solutions between the two goals. Because the rating matrix is rather sparser in recommendation, we first use singular value decomposition to get the recommendation list, then multi-objective immune algorithm is used to optimize it. The experimental results illustrate that the proposed algorithm can get more diverse and accurate recommendation results.