A personalised recommendation method of online and offline mixed teaching resources based on user preference behaviour

For online and offline hybrid teaching resources, due to the low recommendation accuracy and long recommendation time of traditional personalised recommendation methods, a personalised recommendation method based on user preference behaviour is proposed. First, we collect the teaching resource data through the crawler technology, then clean the obtained data, and then build the teaching resource model. Finally, we build the user model, calculate the interest preference behaviour group category that the user belongs to, determine the user preference behaviour, and use cosine similarity to measure the similarity between users, so as to predict the user score, and recommend the resource with the highest score to the user. The experimental results show that the proposed method has higher accuracy and shorter recommendation time. Copyright © 2024 Inderscience Enterprises Ltd.