In the last decade, recommendation systems have gradually become the most important service for online business, which serve as sales assistants for e-commerce business increasing their profits. However, the conventional recommendation systems are usually confronted with two challenges. First, in online shopping contexts, users often browse products that they do not go on to order. The majority of action sequences are browsing-browsing rather than browsing-order. As a result, user actions are not a direct reflection of user preferences. Second, the popularity of sold products creates a skewed distribution that results in the problem of cold-start product for recommendation. In this paper, we present our research on developing a twostage framework of hybrid recommendation system to tackle these two challenges for tourism product recommendation. In order to extract knowledge from users’ implicit feedback, we develop the neighborhood structure of users and products in the multi-behavior interaction network that simultaneously incorporates the browsing and order behaviors. To ensure the coverage of cold products, we considered the metadata associated with products and extracted more features from the textual content to form a product-content knowledge graph. By embedding the multi-behavior network and productcontent knowledge graph within the recommendation system, we were able to capture user preferences from implicit feedback and the relationships among products. To evaluate the proposed model, we conducted experiments on a real-world dataset. Experimental results indicate that the proposed approach outperforms several widely-used recommendation systems.