由于渔业科学数据资源分散、数据关系混乱、数据与用户之间联系不够紧密，使得传统推荐算法结果准确性不高，难以为用户推荐合适渔业科学数据，针对这一问题，提出一种基于地理位置的推荐算法。在传统的用户—物品领域之外引入地理位置信息，结合位置—物品领域，形成跨领域的推荐算法。通过计算IP与用户浏览记录之间的关系，将地理位置信息融入到推荐算法中，并构建一套完整的推荐系统，为用户进行个性化推荐。结果显示：在推荐数据为10条时，相较于传统的协同过滤推荐算法，基于地理位置的跨领域推荐算法在预测准确率、平均击中率上分别提高了9.7%和3.9%；弥补了传统推荐算法没有考虑渔业科学数据本身蕴含的地理位置信息的缺陷，提高了推荐的效果。

To solve the problem that dispersion of data relations in fishery science data, and the lack of close contact between data and users lead to inaccuracy of the results calculated by traditional recommendation algorithm and make it difficult to recommend suitable fishery science data for users, a recommendation algorithm based on geographic location is proposed. This method introduces geographic location information and forms a cross-domain recommendation algorithm based on the traditional user - article domain. It integrates the geographic location information into recommendation algorithm by calculating the relations between IP and users’ records, and build a complete set of personalized recommendation system for users. The results showed that the recommendation accuracy and average hit rates increased by 9.7% and 3.9% compared to the traditional collaborative recommendation when there were 10 pieces of recommendation data. It makes up the shortcoming that traditional recommendation algorithm does not consider the geographical location information contained in the fishery science data, thus improving the efficiency of recommendation.