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Searching, browsing, and recommendations are common ways in which the "choiceoverload" faced by users in the online marketplace can be mitigated. In thispaper we propose the use of hierarchical item categories, obtained fromimplicit feedback data, to enable efficient browsing and recommendations. Wepresent a method of creating hierarchical item categories from implicitfeedback 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 findinginteresting and relevant items while they are browsing through the hierarchy.We also show that this item hierarchy can be useful in making category basedrecommendations, which makes the recommendations more explainable and increasesthe diversity of the recommendations without compromising much on the accuracy.Item hierarchy can also be useful in the creation of an automatic item taxonomyskeleton by bypassing manual labeling and annotation. This can especially beuseful for small vendors. Our data-driven hierarchical categories are based onhierarchical latent tree analysis (HLTA) which has been previously used fortext analysis. We present a scaled up learning algorithm \emph{HLTA-Forest} sothat HLTA can be applied to implicit feedback data.

Learning Hierarchical Item Categories from Implicit Feedback Data for Efficient Recommendations and Browsing