2 research outputs found
Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network
Personalized review generation (PRG) aims to automatically produce review
text reflecting user preference, which is a challenging natural language
generation task. Most of previous studies do not explicitly model factual
description of products, tending to generate uninformative content. Moreover,
they mainly focus on word-level generation, but cannot accurately reflect more
abstractive user preference in multiple aspects. To address the above issues,
we propose a novel knowledge-enhanced PRG model based on capsule graph neural
network~(Caps-GNN). We first construct a heterogeneous knowledge graph (HKG)
for utilizing rich item attributes. We adopt Caps-GNN to learn graph capsules
for encoding underlying characteristics from the HKG. Our generation process
contains two major steps, namely aspect sequence generation and sentence
generation. First, based on graph capsules, we adaptively learn aspect capsules
for inferring the aspect sequence. Then, conditioned on the inferred aspect
label, we design a graph-based copy mechanism to generate sentences by
incorporating related entities or words from HKG. To our knowledge, we are the
first to utilize knowledge graph for the PRG task. The incorporated KG
information is able to enhance user preference at both aspect and word levels.
Extensive experiments on three real-world datasets have demonstrated the
effectiveness of our model on the PRG task.Comment: Accepted by CIKM 2020 (Long Paper
CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
Recent research explores incorporating knowledge graphs (KG) into e-commerce
recommender systems, not only to achieve better recommendation performance, but
more importantly to generate explanations of why particular decisions are made.
This can be achieved by explicit KG reasoning, where a model starts from a user
node, sequentially determines the next step, and walks towards an item node of
potential interest to the user. However, this is challenging due to the huge
search space, unknown destination, and sparse signals over the KG, so
informative and effective guidance is needed to achieve a satisfactory
recommendation quality. To this end, we propose a CoArse-to-FinE neural
symbolic reasoning approach (CAFE). It first generates user profiles as coarse
sketches of user behaviors, which subsequently guide a path-finding process to
derive reasoning paths for recommendations as fine-grained predictions. User
profiles can capture prominent user behaviors from the history, and provide
valuable signals about which kinds of path patterns are more likely to lead to
potential items of interest for the user. To better exploit the user profiles,
an improved path-finding algorithm called Profile-guided Path Reasoning (PPR)
is also developed, which leverages an inventory of neural symbolic reasoning
modules to effectively and efficiently find a batch of paths over a large-scale
KG. We extensively experiment on four real-world benchmarks and observe
substantial gains in the recommendation performance compared with
state-of-the-art methods.Comment: Accepted in CIKM 202