19 research outputs found
Attentive Aspect Modeling for Review-aware Recommendation
In recent years, many studies extract aspects from user reviews and integrate
them with ratings for improving the recommendation performance. The common
aspects mentioned in a user's reviews and a product's reviews indicate indirect
connections between the user and product. However, these aspect-based methods
suffer from two problems. First, the common aspects are usually very sparse,
which is caused by the sparsity of user-product interactions and the diversity
of individual users' vocabularies. Second, a user's interests on aspects could
be different with respect to different products, which are usually assumed to
be static in existing methods. In this paper, we propose an Attentive
Aspect-based Recommendation Model (AARM) to tackle these challenges. For the
first problem, to enrich the aspect connections between user and product,
besides common aspects, AARM also models the interactions between synonymous
and similar aspects. For the second problem, a neural attention network which
simultaneously considers user, product and aspect information is constructed to
capture a user's attention towards aspects when examining different products.
Extensive quantitative and qualitative experiments show that AARM can
effectively alleviate the two aforementioned problems and significantly
outperforms several state-of-the-art recommendation methods on top-N
recommendation task.Comment: Camera-ready manuscript for TOI
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label Learning
In reality, learning from multi-view multi-label data inevitably confronts
three challenges: missing labels, incomplete views, and non-aligned views.
Existing methods mainly concern the first two and commonly need multiple
assumptions to attack them, making even state-of-the-arts involve at least two
explicit hyper-parameters such that model selection is quite difficult. More
roughly, they will fail in handling the third challenge, let alone addressing
the three jointly. In this paper, we aim at meeting these under the least
assumption by building a concise yet effective model with just one
hyper-parameter. To ease insufficiency of available labels, we exploit not only
the consensus of multiple views but also the global and local structures hidden
among multiple labels. Specifically, we introduce an indicator matrix to tackle
the first two challenges in a regression form while aligning the same
individual labels and all labels of different views in a common label space to
battle the third challenge. In aligning, we characterize the global and local
structures of multiple labels to be high-rank and low-rank, respectively.
Subsequently, an efficient algorithm with linear time complexity in the number
of samples is established. Finally, even without view-alignment, our method
substantially outperforms state-of-the-arts with view-alignment on five real
datasets.Comment: 15 pages, 7 figure
Relieving Triplet Ambiguity: Consensus Network for Language-Guided Image Retrieval
Language-guided image retrieval enables users to search for images and
interact with the retrieval system more naturally and expressively by using a
reference image and a relative caption as a query. Most existing studies mainly
focus on designing image-text composition architecture to extract
discriminative visual-linguistic relations. Despite great success, we identify
an inherent problem that obstructs the extraction of discriminative features
and considerably compromises model training: \textbf{triplet ambiguity}. This
problem stems from the annotation process wherein annotators view only one
triplet at a time. As a result, they often describe simple attributes, such as
color, while neglecting fine-grained details like location and style. This
leads to multiple false-negative candidates matching the same modification
text. We propose a novel Consensus Network (Css-Net) that self-adaptively
learns from noisy triplets to minimize the negative effects of triplet
ambiguity. Inspired by the psychological finding that groups perform better
than individuals, Css-Net comprises 1) a consensus module featuring four
distinct compositors that generate diverse fused image-text embeddings and 2) a
Kullback-Leibler divergence loss, which fosters learning among the compositors,
enabling them to reduce biases learned from noisy triplets and reach a
consensus. The decisions from four compositors are weighted during evaluation
to further achieve consensus. Comprehensive experiments on three datasets
demonstrate that Css-Net can alleviate triplet ambiguity, achieving competitive
performance on benchmarks, such as R@10 and R@50 on
FashionIQ.Comment: 11 page
User Diverse Preference Modeling by Multimodal Attentive Metric Learning
Most existing recommender systems represent a user's preference with a
feature vector, which is assumed to be fixed when predicting this user's
preferences for different items. However, the same vector cannot accurately
capture a user's varying preferences on all items, especially when considering
the diverse characteristics of various items. To tackle this problem, in this
paper, we propose a novel Multimodal Attentive Metric Learning (MAML) method to
model user diverse preferences for various items. In particular, for each
user-item pair, we propose an attention neural network, which exploits the
item's multimodal features to estimate the user's special attention to
different aspects of this item. The obtained attention is then integrated into
a metric-based learning method to predict the user preference on this item. The
advantage of metric learning is that it can naturally overcome the problem of
dot product similarity, which is adopted by matrix factorization (MF) based
recommendation models but does not satisfy the triangle inequality property. In
addition, it is worth mentioning that the attention mechanism cannot only help
model user's diverse preferences towards different items, but also overcome the
geometrically restrictive problem caused by collaborative metric learning.
Extensive experiments on large-scale real-world datasets show that our model
can substantially outperform the state-of-the-art baselines, demonstrating the
potential of modeling user diverse preference for recommendation.Comment: Accepted by ACM Multimedia 2019 as a full pape
Learning to Ask: Question-based Sequential Bayesian Product Search
Product search is generally recognized as the first and foremost stage of
online shopping and thus significant for users and retailers of e-commerce.
Most of the traditional retrieval methods use some similarity functions to
match the user's query and the document that describes a product, either
directly or in a latent vector space. However, user queries are often too
general to capture the minute details of the specific product that a user is
looking for. In this paper, we propose a novel interactive method to
effectively locate the best matching product. The method is based on the
assumption that there is a set of candidate questions for each product to be
asked. In this work, we instantiate this candidate set by making the hypothesis
that products can be discriminated by the entities that appear in the documents
associated with them. We propose a Question-based Sequential Bayesian Product
Search method, QSBPS, which directly queries users on the expected presence of
entities in the relevant product documents. The method learns the product
relevance as well as the reward of the potential questions to be asked to the
user by being trained on the search history and purchase behavior of a specific
user together with that of other users. The experimental results show that the
proposed method can greatly improve the performance of product search compared
to the state-of-the-art baselines.Comment: This paper is accepted by CIKM 201
A Zero Attention Model for Personalized Product Search
Product search is one of the most popular methods for people to discover and
purchase products on e-commerce websites. Because personal preferences often
have an important influence on the purchase decision of each customer, it is
intuitive that personalization should be beneficial for product search engines.
While synthetic experiments from previous studies show that purchase histories
are useful for identifying the individual intent of each product search
session, the effect of personalization on product search in practice, however,
remains mostly unknown. In this paper, we formulate the problem of personalized
product search and conduct large-scale experiments with search logs sampled
from a commercial e-commerce search engine. Results from our preliminary
analysis show that the potential of personalization depends on query
characteristics, interactions between queries, and user purchase histories.
Based on these observations, we propose a Zero Attention Model for product
search that automatically determines when and how to personalize a user-query
pair via a novel attention mechanism. Empirical results on commercial product
search logs show that the proposed model not only significantly outperforms
state-of-the-art personalized product retrieval models, but also provides
important information on the potential of personalization in each product
search session