Abstract: Travel products recommendation has become one of emerging issues in the realm of recommendation systems. The widely-used collaborative filtering algorithms are usually difficult to be used for recommending travel products due to a number of reasons, including: 1) the content of travel products is very complex, 2) the user-item matrix is extremely sparse, and 3) the cold-start users are widely existing. To tackle these issues, we try to exploit Web server logs for generating recommenda-tion, and present a novel recommendation engine (SECT for short) for travel products based on topic sequential patterns. In detail, we first extract topics from semantic description of every Web page. Then, we mine topic frequent sequential patterns and their target products to form click patterns library. At last, we propose a Markov n-gram model for matching the real-time click-stream of users with the click patterns library and thus computing recommendation scores. To enhance the efficiency of online computing, we design a new multi-branch tree data structures called PSC-tree to store the historical click patterns library and integrate with online computing module seamlessly. Experimental results on a real-world travel dataset demonstrate that the SECT prevails over the state-of-art baseline algorithms. In particular, SECT shows merits in improving both the coverage and accuracy for recommending products to cold-start users. Also, SECT is effective to recommend long tail items and outperform baseline algorithms.