论文标题
超越下一个项目建议：推荐和评估序列列表
Beyond Next Item Recommendation: Recommending and Evaluating List of Sequences
论文作者
论文摘要
推荐系统（RS）建议基于用户估计偏好的项目。最新的RS方法利用向量空间嵌入和深度学习方法来提出有效的建议。但是，这些方法中的大多数都忽略了顺序性特征，并考虑每个相互作用，例如签入，彼此独立。提出的方法考虑了用户与项目的交互的顺序，并使用它们来提出多项目序列列表的建议。所提出的方法使用FastText \ cite {Bojanowski2016enriching}，一种众所周知的自然语言处理技术（NLP），对序列亚基之间的关系进行建模，例如序列，轨道，播放列表，并利用训练有素的代表作为传统建议方法的输入。推荐的多项目序列列表由Rouge \ cite {lin2003automatic，lin2004Rouge}公制评估，该公制也通常在NLP文献中使用。当前的实验结果表明，除传统的下一个项目建议外，还可以推荐多项目序列的列表。此外，使用输入序列的子单元的FastText的使用有助于克服冷启动的用户问题。
Recommender systems (RS) suggest items-based on the estimated preferences of users. Recent RS methods utilise vector space embeddings and deep learning methods to make efficient recommendations. However, most of these methods overlook the sequentiality feature and consider each interaction, e.g., check-in, independent from each other. The proposed method considers the sequentiality of the interactions of users with items and uses them to make recommendations of a list of multi-item sequences. The proposed method uses FastText \cite{bojanowski2016enriching}, a well-known technique in natural language processing (NLP), to model the relationship among the subunits of sequences, e.g., tracks, playlists, and utilises the trained representation as an input to a traditional recommendation method. The recommended lists of multi-item sequences are evaluated by the ROUGE \cite{lin2003automatic,lin2004rouge} metric, which is also commonly used in the NLP literature. The current experimental results reveal that it is possible to recommend a list of multi-item sequences, in addition to the traditional next item recommendation. Also, the usage of FastText, which utilise sub-units of the input sequences, helps to overcome cold-start user problem.
