Recommender systems are designed to help users in situations of information
overload. In recent years, we observed increased interest in session-based
recommendation scenarios, where the problem is to make item suggestions to
users based only on interactions observed in an ongoing session. However, in
cases where interactions from previous user sessions are available, the
recommendations can be personalized according to the users' long-term
preferences, a process called session-aware recommendation. Today, research in
this area is scattered and many existing works only compare session-aware with
session-based models. This makes it challenging to understand what represents
the state-of-the-art. To close this research gap, we benchmarked recent
session-aware algorithms against each other and against a number of
session-based recommendation algorithms and trivial extensions thereof. Our
comparison, to some surprise, revealed that (i) item simple techniques based on
nearest neighbors consistently outperform recent neural techniques and that
(ii) session-aware models were mostly not better than approaches that do not
use long-term preference information. Our work therefore not only points to
potential methodological issues where new methods are compared to weak
baselines, but also indicates that there remains a huge potential for more
sophisticated session-aware recommendation algorithms.

本篇论文针对会话推荐场景中的个性化推荐问题进行了研究，通过比较不同技术之间的性能，发现基于最近邻的简单技术比最近的神经技术表现更好，并且大多数情况下，考虑长期偏好信息的会话感知模型并不能优于不使用长期偏好信息的方法。