Abstract: Aiming at the problem of high ranking of commodity views and lagging sales in online shopping, based on purchasing intention of users, we proposed a collaborative filtering recommendation algorithm merged with enhanced rating matrix. Firstly, the penalty factor was used as the evaluation weight of the enhanced matrix, and the commodity portrait representing the user’s purchasing intention was weighted to obtain the prediction score of the enhanced matrix. Secondly, the basic rating matrix was obtained by combining the item-based collaborative filtering recommendation to establish the item similarity matrix between the potentially interested commodities. Finally, TOP-N result was used to recommend the top ranked commodities to target users with strong purchasing intention. The experimental results show that, compared with the traditional item-based collaborative filtering recommendation algorithm, the recommendation accuracy of the enhanced rating matrix collaborative filtering recommendation algorithm is improved by 2.48%, the recall rate is improved by 4.31%, and the comprehensive value F₁ is improved by 3.19%, which effectively solves the problem that the commodities of interest to users are ranked low and are not purchased or purchased less times, so as to achieve recommendation purpose of more accurate target users with strong purchasing intention, and then improve the recommendation accuracy.

Key words: similarity, penalty factor, recommendation accuracy, collaborative filtering, recommendation algorithm