  Traditional approaches to next item and next basket recommendation typically
extract users' interests based on their past interactions and associated static
contextual information (e.g. a user id or item category). However, extracted
interests can be inaccurate and become obsolete. Dynamic attributes, such as
user income changes, item price changes (etc.), change over time. Such dynamics
can intrinsically reflect the evolution of users' interests. We argue that
modeling such dynamic attributes can boost recommendation performance. However,
properly integrating them into user interest models is challenging since
attribute dynamics can be diverse such as time-interval aware, periodic
patterns (etc.), and they represent users' behaviors from different
perspectives, which can happen asynchronously with interactions. Besides
dynamic attributes, items in each basket contain complex interdependencies
which might be beneficial but nontrivial to effectively capture. To address
these challenges, we propose a novel Attentive network to model Dynamic
attributes (named AnDa). AnDa separately encodes dynamic attributes and basket
item sequences. We design a periodic aware encoder to allow the model to
capture various temporal patterns from dynamic attributes. To effectively learn
useful item relationships, intra-basket attention module is proposed.
Experimental results on three real-world datasets demonstrate that our method
consistently outperforms the state-of-the-art.
