From: ~Ransalu_Senanayake1
comment: Brief Summary:

    The paper proposes a temporal recommendation system using RNNs that aims to incorporate two kinds of information that are not typically considered for this task: 1. Hierarchical representation of temporally long user histories, and 2) meta-data (contextual information)

General Comments:

    I like the idea of incorporating contextual information, in the form of meta-data, with temporal information as this is often not considered but should be a natural consideration if one is to make the most of available data.

    The generalization of the inputs to make the modelling invariant to network type is highly desirable (as opposed to Quadrana et al)

Strengths:

    The ablation studies as well as alternative network comparisons are appreciated

    The ability to easily incorporate meta-data is a useful property to have

Weaknesses:

    It is stated “To maintain performance even with missing values, we found that item features are better learned as substitutes rather than complements of the decoder weights. We achieve this by randomizing \lambda during training.” It is not clear how or why this is the case. What kind of impact does this difference make in actual terms? Could other architecture changes produce the same difference of effect and is it possible to show what you state is, in isolation, true? It is not clear why the \lambda need to be randomized to “substitute rather than complement” the decoder weights. An intuitive explanation would useful in understanding this.

    Table 2 appears not to have any associated units in the text; the column headings and their respective meanings are not clear. It is also not clear which model was used to produce this data (I am assuming it is the HRNN-meta).

Minor issues:

    L157 “cimilar” should be “similar”

Summary of Recommendation:

    Accept
title: From: NIPS 2018 Workshop Spatiotemporal Paper AnonReviewer1