69 research outputs found
Persona-Aware Tips Generation
Tips, as a compacted and concise form of reviews, were paid less attention by
researchers. In this paper, we investigate the task of tips generation by
considering the `persona' information which captures the intrinsic language
style of the users or the different characteristics of the product items. In
order to exploit the persona information, we propose a framework based on
adversarial variational auto-encoders (aVAE) for persona modeling from the
historical tips and reviews of users and items. The latent variables from aVAE
are regarded as persona embeddings. Besides representing persona using the
latent embeddings, we design a persona memory for storing the persona related
words for users and items. Pointer Network is used to retrieve persona wordings
from the memory when generating tips. Moreover, the persona embeddings are used
as latent factors by a rating prediction component to predict the sentiment of
a user over an item. Finally, the persona embeddings and the sentiment
information are incorporated into a recurrent neural networks based tips
generation component. Extensive experimental results are reported and discussed
to elaborate the peculiarities of our framework.Comment: Accepted to WWW'2019, 11 page
LRMM: Learning to Recommend with Missing Modalities
Multimodal learning has shown promising performance in content-based
recommendation due to the auxiliary user and item information of multiple
modalities such as text and images. However, the problem of incomplete and
missing modality is rarely explored and most existing methods fail in learning
a recommendation model with missing or corrupted modalities. In this paper, we
propose LRMM, a novel framework that mitigates not only the problem of missing
modalities but also more generally the cold-start problem of recommender
systems. We propose modality dropout (m-drop) and a multimodal sequential
autoencoder (m-auto) to learn multimodal representations for complementing and
imputing missing modalities. Extensive experiments on real-world Amazon data
show that LRMM achieves state-of-the-art performance on rating prediction
tasks. More importantly, LRMM is more robust to previous methods in alleviating
data-sparsity and the cold-start problem.Comment: 11 pages, EMNLP 201
Abstractive Opinion Tagging
In e-commerce, opinion tags refer to a ranked list of tags provided by the
e-commerce platform that reflect characteristics of reviews of an item. To
assist consumers to quickly grasp a large number of reviews about an item,
opinion tags are increasingly being applied by e-commerce platforms. Current
mechanisms for generating opinion tags rely on either manual labelling or
heuristic methods, which is time-consuming and ineffective. In this paper, we
propose the abstractive opinion tagging task, where systems have to
automatically generate a ranked list of opinion tags that are based on, but
need not occur in, a given set of user-generated reviews.
The abstractive opinion tagging task comes with three main challenges: (1)
the noisy nature of reviews; (2) the formal nature of opinion tags vs. the
colloquial language usage in reviews; and (3) the need to distinguish between
different items with very similar aspects. To address these challenges, we
propose an abstractive opinion tagging framework, named AOT-Net, to generate a
ranked list of opinion tags given a large number of reviews. First, a
sentence-level salience estimation component estimates each review's salience
score. Next, a review clustering and ranking component ranks reviews in two
steps: first, reviews are grouped into clusters and ranked by cluster size;
then, reviews within each cluster are ranked by their distance to the cluster
center. Finally, given the ranked reviews, a rank-aware opinion tagging
component incorporates an alignment feature and alignment loss to generate a
ranked list of opinion tags. To facilitate the study of this task, we create
and release a large-scale dataset, called eComTag, crawled from real-world
e-commerce websites. Extensive experiments conducted on the eComTag dataset
verify the effectiveness of the proposed AOT-Net in terms of various evaluation
metrics.Comment: Accepted by WSDM 202
A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss
Acquiring accurate summarization and sentiment from user reviews is an
essential component of modern e-commerce platforms. Review summarization aims
at generating a concise summary that describes the key opinions and sentiment
of a review, while sentiment classification aims to predict a sentiment label
indicating the sentiment attitude of a review. To effectively leverage the
shared sentiment information in both review summarization and sentiment
classification tasks, we propose a novel dual-view model that jointly improves
the performance of these two tasks. In our model, an encoder first learns a
context representation for the review, then a summary decoder generates a
review summary word by word. After that, a source-view sentiment classifier
uses the encoded context representation to predict a sentiment label for the
review, while a summary-view sentiment classifier uses the decoder hidden
states to predict a sentiment label for the generated summary. During training,
we introduce an inconsistency loss to penalize the disagreement between these
two classifiers. It helps the decoder to generate a summary to have a
consistent sentiment tendency with the review and also helps the two sentiment
classifiers learn from each other. Experiment results on four real-world
datasets from different domains demonstrate the effectiveness of our model.Comment: Accepted by SIGIR 2020. Updated the results of balanced accuracy
scores in Table 3 since we found a bug in our source code. Nevertheless, our
model still achieves higher balanced accuracy scores than the baselines after
we fixed this bu
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