22,285 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
Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation
Venue recommendation aims to assist users by making personalised
suggestions of venues to visit, building upon data available from
location-based social networks (LBSNs) such as Foursquare. A
particular challenge for this task is context-aware venue recommendation
(CAVR), which additionally takes the surrounding context of
the user (e.g. the user’s location and the time of day) into account
in order to provide more relevant venue suggestions. To address the
challenges of CAVR, we describe two approaches that exploit word
embedding techniques to infer the vector-space representations of
venues, users’ existing preferences, and users’ contextual preferences.
Our evaluation upon the test collection of the TREC 2015
Contextual Suggestion track demonstrates that we can significantly
enhance the effectiveness of a state-of-the-art venue recommendation
approach, as well as produce context-aware recommendations
that are at least as effective as the top TREC 2015 systems
- …