Abstract: Agricultural information has strong timeliness and periodicity. Traditional behavior-based recommendation algorithms can mine farmers’ interests but cannot reflect the information needs of farmers at different time periods. At the same time, farmers generally use an anonymous webpage to browse agricultural news directly. Explicit feedback data is very scarce. Traditional collaborative filtering recommendation algorithms need to face cold start problems. This paper proposes a collaborative filtering recommendation algorithm based on user behavior and news timeliness, which integrates the multi-dimensional factors such as user’s implicit and explicit feedback data, and considers the classification characteristics and periodicity of agricultural information. It improves the pertinence and timeliness of agricultural news recommendation according to the periodic attention change of different agricultural classification information and the heat coefficient. The experimental results show that the proposed algorithm can effectively improve the accuracy of agricultural information recommendation.

Key words: agricultural information, collaborative filtering, implicit feedback, cold start, classification characteristics, periodicity