@article{Gu_Song_Jiang_Wang_Liu_2020, title={Enhancing Personalized Trip Recommendation with Attractive Routes}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/5407}, DOI={10.1609/aaai.v34i01.5407}, abstractNote={<p>Personalized trip recommendation tries to recommend a sequence of point of interests (POIs) for a user. Most of existing studies search POIs only according to the popularity of POIs themselves. In fact, the routes among the POIs also have attractions to visitors, and some of these routes have high popularity. We term this kind of route as <em>Attractive Route</em> (AR), which brings extra user experience. In this paper, we study the attractive routes to improve personalized trip recommendation. To deal with the challenges of discovery and evaluation of ARs, we propose a personalized <span style="text-decoration: underline;">T</span>rip <span style="text-decoration: underline;">R</span>ecommender with POIs and <span style="text-decoration: underline;">A</span>ttractive <span style="text-decoration: underline;">R</span>oute (TRAR). It discovers the attractive routes based on the popularity and the Gini coefficient of POIs, then it utilizes a gravity model in a category space to estimate the rating scores and preferences of the attractive routes. Based on that, TRAR recommends a trip with ARs to maximize user experience and leverage the tradeoff between the time cost and the user experience. The experimental results show the superiority of TRAR compared with other state-of-the-art methods.</p>}, number={01}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Gu, Jiqing and Song, Chao and Jiang, Wenjun and Wang, Xiaomin and Liu, Ming}, year={2020}, month={Apr.}, pages={662-669} }