Journal of Digital Information Management Jing YI, Liang ZHANG, Phelan, C.A 14 2 2016 Collaborative filtering (CF) is the most successful and widely utilized recommendation technology. CF-based recommenders perform well in terms of accuracy, but they lack the capability to find fresh and novel items. To improve the novel recommendation of userbased CF, the definition of a novel item was established, and an appropriate strategy of novel recommendation was determined. First, a novel item containing the three aspects of likability, unknown, and dissimilarity was defined based on the meaning of the term novel. Second, metrics that measure the novelty of the recommendation system were designed based on the timeliness of novelty. Finally, for the comparison of different strategies of novel recommendation, unknown and dissimilarity were integrated into the process and output of traditional algorithms. Results show that the novelty of the recommendation system is significantly improved when unknown and dissimilarity are integrated into the recommendation results of the traditional algorithm to recalculate the novelty of the item and set the accuracy threshold. Output integration strategy can improve the novelty of the recommended results and can be utilized for any algorithm.