Abstract
Items adopted by a user over time are indicative of
the underlying preferences. We are concerned with
learning such preferences from observed sequences
of adoptions for recommendation. As multiple
items are commonly adopted concurrently, e.g., a
basket of grocery items or a sitting of media consumption, we deal with a sequence of baskets as
input, and seek to recommend the next basket. Intuitively, a basket tends to contain groups of related
items that support particular needs. Instead of recommending items independently for the next basket, we hypothesize that incorporating information
on pairwise correlations among items would help to
arrive at more coherent basket recommendations.
Towards this objective, we develop a hierarchical
network architecture codenamed Beacon to model
basket sequences. Each basket is encoded taking
into account the relative importance of items and
correlations among item pairs. This encoding is
utilized to infer sequential associations along the
basket sequence. Extensive experiments on three
public real-life datasets showcase the effectiveness
of our approach for the next-basket recommendation problem