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Evaluation of session-based recommendation algorithms

机译：评估基于会话的推荐算法

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摘要

Recommender systems help users find relevant items of interest, for example on e-commerce or media streaming sites. Most academic research is concerned with approaches that personalize the recommendations according to long-term user profiles. In many real-world applications, however, such long-term profiles often do not exist and recommendations therefore have to be made solely based on the observed behavior of a user during an ongoing session. Given the high practical relevance of the problem, an increased interest in this problem can be observed in recent years, leading to a number of proposals for session-based recommendation algorithms that typically aim to predict the user’s immediate next actions. In this work, we present the results of an in-depth performance comparison of a number of such algorithms, using a variety of datasets and evaluation measures. Our comparison includes the most recent approaches based on recurrent neural networks like gru4rec , factorized Markov model approaches such as fism or fossil , as well as simpler methods based, e.g., on nearest neighbor schemes. Our experiments reveal that algorithms of this latter class, despite their sometimes almost trivial nature, often perform equally well or significantly better than today’s more complex approaches based on deep neural networks. Our results therefore suggest that there is substantial room for improvement regarding the development of more sophisticated session-based recommendation algorithms.
机译：推荐系统可帮助用户找到感兴趣的相关项目，例如在电子商务或媒体流网站上。大多数学术研究都关注根据长期用户概况个性化建议的方法。但是，在许多实际应用中，此类长期配置文件通常不存在，因此必须仅根据正在进行的会话期间观察到的用户行为来提出建议。考虑到该问题的高度实际相关性，近年来人们对该问题的兴趣日益增加，从而引发了许多基于会话的推荐算法的提案，这些提案通常旨在预测用户的立即下一步操作。在这项工作中，我们展示了使用各种数据集和评估方法对许多此类算法进行深入性能比较的结果。我们的比较包括基于递归神经网络的最新方法（如gru4rec），因式马尔可夫模型方法（如拳头或化石）以及基于例如最近邻居方案的更简单方法。我们的实验表明，尽管后者的算法有时几乎是微不足道的，但它们的性能通常比当今基于深度神经网络的更为复杂的方法具有相同或更好的性能。因此，我们的结果表明，在开发更复杂的基于会话的推荐算法方面，还有很大的改进空间。

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