20,293 research outputs found
Knowledge-aware Complementary Product Representation Learning
Learning product representations that reflect complementary relationship
plays a central role in e-commerce recommender system. In the absence of the
product relationships graph, which existing methods rely on, there is a need to
detect the complementary relationships directly from noisy and sparse customer
purchase activities. Furthermore, unlike simple relationships such as
similarity, complementariness is asymmetric and non-transitive. Standard usage
of representation learning emphasizes on only one set of embedding, which is
problematic for modelling such properties of complementariness. We propose
using knowledge-aware learning with dual product embedding to solve the above
challenges. We encode contextual knowledge into product representation by
multi-task learning, to alleviate the sparsity issue. By explicitly modelling
with user bias terms, we separate the noise of customer-specific preferences
from the complementariness. Furthermore, we adopt the dual embedding framework
to capture the intrinsic properties of complementariness and provide geometric
interpretation motivated by the classic separating hyperplane theory. Finally,
we propose a Bayesian network structure that unifies all the components, which
also concludes several popular models as special cases. The proposed method
compares favourably to state-of-art methods, in downstream classification and
recommendation tasks. We also develop an implementation that scales efficiently
to a dataset with millions of items and customers
ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation
A user can be represented as what he/she does along the history. A common way
to deal with the user modeling problem is to manually extract all kinds of
aggregated features over the heterogeneous behaviors, which may fail to fully
represent the data itself due to limited human instinct. Recent works usually
use RNN-based methods to give an overall embedding of a behavior sequence,
which then could be exploited by the downstream applications. However, this can
only preserve very limited information, or aggregated memories of a person.
When a downstream application requires to facilitate the modeled user features,
it may lose the integrity of the specific highly correlated behavior of the
user, and introduce noises derived from unrelated behaviors. This paper
proposes an attention based user behavior modeling framework called ATRank,
which we mainly use for recommendation tasks. Heterogeneous user behaviors are
considered in our model that we project all types of behaviors into multiple
latent semantic spaces, where influence can be made among the behaviors via
self-attention. Downstream applications then can use the user behavior vectors
via vanilla attention. Experiments show that ATRank can achieve better
performance and faster training process. We further explore ATRank to use one
unified model to predict different types of user behaviors at the same time,
showing a comparable performance with the highly optimized individual models.Comment: AAAI 201
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