A Lasso-based Collaborative Filtering Recommendation Model

This paper proposes a new approach to solve the problem of lack of information in rating data due to new users or new items, or there is too little rating data of the user for items of the collaborative filtering recommendation models (CFR models). In this approach, we consider the similarity between users or items based on the lasso regression to build the CFR models. In the commonly used CFR models, the recommendation results are built only based on the feedback matrix of users. The results of our model are predicted based on two similarity calculated values: (1) the similarity calculated value based on the rating matrix; (2) the similarity calculated value based on the prediction results of the Lasso regression. The experimental results of the proposed models on two popular datasets have been processed and integrated into the recommenderlab package showed that the suggested models have higher accuracy than the commonly used CFR models. This result confirms that Lasso regression helps to deal with the lack of information in the rating data problem of the CFR models.