There exist many research works that strive to answer the question “what news article is a user going to click next given his profile”. These works take into account the time dimension to reveal users’ preferences over time. However, few works exploit adequately the information that is hidden inside user sessions. User sessions include a list of user interactions with items within a short period of time such as 30 min, and can reveal her very last intentions. In this paper, we combine intra- with inter-session item transition probabilities to reveal the short- and long-term intentions of individuals. Thus, we are able to better capture the similarities among items that are co-selected inside a user session but also within any two consecutive sessions. We have evaluated experimentally our method and compare it against state-of-the-art algorithms on three real-life datasets. We demonstrate the superiority of our method over its competitors.

存在许多研究工作试图回答“根据他的个人资料，用户接下来会点击什么新闻文章”这个问题。这些作品考虑了时间维度，以随着时间的推移揭示用户的偏好。然而，很少有作品充分利用隐藏在用户会话中的信息。用户会话包括在短时间内（例如 30 分钟）内与项目的用户交互列表，并且可以揭示她最后的意图。在本文中，我们将会话内和会话间项目转换概率结合起来，以揭示个人的短期和长期意图。因此，我们能够更好地捕捉用户会话内以及任何两个连续会话内共同选择的项目之间的相似性。我们已经对我们的方法进行了实验评估，并将其与三个真实数据集上的最新算法进行了比较。我们展示了我们的方法优于其竞争对手的优势。