Searching, browsing, and recommendations are common ways in which the "choice
overload" faced by users in the online marketplace can be mitigated. In this
paper we propose the use of hierarchical item categories, obtained from
implicit feedback data, to enable efficient browsing and recommendations. We
present a method of creating hierarchical item categories from implicit
feedback data only i.e., without any other information on the items like name,
genre etc. Categories created in this fashion are based on users'
co-consumption of items. Thus, they can be more useful for users in finding
interesting and relevant items while they are browsing through the hierarchy.
We also show that this item hierarchy can be useful in making category based
recommendations, which makes the recommendations more explainable and increases
the diversity of the recommendations without compromising much on the accuracy.
Item hierarchy can also be useful in the creation of an automatic item taxonomy
skeleton by bypassing manual labeling and annotation. This can especially be
useful for small vendors. Our data-driven hierarchical categories are based on
hierarchical latent tree analysis (HLTA) which has been previously used for
text analysis. We present a scaled up learning algorithm \emph{HLTA-Forest} so
that HLTA can be applied to implicit feedback data.

使用来自隐式反馈数据的分层项目类别，以实现在线市场中有效的浏览和推荐。通过基于用户的协同消费行为创建类别，可以使用户更容易找到有趣和相关的商品。 HLTA-Forest 是一个用于隐式反馈数据的缩放学习算法，可用于创建基于类别的推荐，并且还可以用于自动创建商品分类骨架。