Modeling user preferences (long-term history) and user dynamics (short-term history) is of greatest importance to build efficient sequential recommender systems. The challenge lies in the successful combination of the whole user’s history and his recent actions (sequential dynamics) to provide personalized recommendations. Existing methods capture the sequential dynamics of a user using fixed-order Markov chains (usually first order chains) regardless of the user, which limits both the impact of the past of the user on the recommendation and the ability to adapt its length to the user profile. In this article, we propose to use frequent sequences to identify the most relevant part of the user history for the recommendation. The most salient items are then used in a unified metric model that embeds items based on user preferences and sequential dynamics. Extensive experiments demonstrate that our method outperforms state-of-the-art, especially on sparse datasets. We show that considering sequences of varying lengths improves the recommendations and we also emphasize that these sequences provide explanations on the recommendation.

对用户偏好（长期历史记录）和用户动态（短期历史记录）进行建模对于构建高效的顺序推荐系统至关重要。挑战在于将整个用户的历史和他最近的动作（顺序动态）成功地结合起来以提供个性化推荐。现有方法使用固定顺序的马尔可夫链（通常是一阶链）来捕获用户的顺序动态，而与用户无关，这既限制了用户过去对推荐的影响，也限制了其长度适应用户的能力。轮廓。在本文中，我们建议使用频繁序列来识别推荐中用户历史记录中最相关的部分。然后，将最突出的项目用于统一的度量标准模型，该模型根据用户的喜好和顺序动态来嵌入项目。大量实验表明，我们的方法优于最新技术，尤其是在稀疏数据集上。我们表明考虑不同长度的序列可以改善建议，并且我们还强调这些序列为建议提供了解释。