Nowadays, we have large amounts of online items in various web-based applications, which makes it an important task to build effective personalized recommender systems so as to save users' efforts in information seeking. One of the most extensively and successfully used methods for personalized recommendation is the Collaborative Filtering (CF) technique, which makes recommendation based on users' historical choices as well as those of the others'. The most popular CF method, like Latent Factor Model (LFM), is to model how users evaluate items by understanding the hidden dimension or factors of their opinions. How to model these hidden factors is key to improve the performance of recommender system. In this work, we consider the problem of hotel recommendation for travel planning services by integrating the location information and the user's preference for recommendation. The intuition is that user preferences may change dynamically over different locations, thus treating the historical decisions of a user as static or universally applicable can be infeasible in real-world applications. For example, users may prefer chain brand hotels with standard configurations when traveling for business, while they may prefer unique local hotels when traveling for entertainment. In this paper, we aim to provide trip-level personalization for users in recommendation.
翻译:目前,我们在各种基于网络的应用程序中有大量在线项目,这使得建立有效的个性化推荐系统成为一项重要任务,以节省用户在信息搜索方面的努力。对于个性化建议,最广泛和成功使用的方法之一是合作过滤技术(CF),根据用户的历史选择以及其他人的历史选择提出建议。最受欢迎的CF方法,如Litetent Point 模型(LFM),是模拟用户如何通过了解其意见的隐藏层面或因素来评估项目。如何模拟这些隐性因素是改进推荐者系统绩效的关键。在这项工作中,我们考虑旅馆旅行规划服务建议的问题,办法是将地点信息与用户对建议的偏好结合起来。直觉是,用户的偏好可能会在不同的地点发生动态变化,从而将用户的历史决定视为静态或普遍适用,在现实世界应用中是行不通的。例如,用户在为商业目的旅行时可能更喜欢使用标准配置的品牌旅馆,而他们可能更喜欢在旅行娱乐时选择独特的当地旅馆。在本文中,我们打算为用户提供绊脚的个性个人化建议。