542 research outputs found
Expectile Matrix Factorization for Skewed Data Analysis
Matrix factorization is a popular approach to solving matrix estimation
problems based on partial observations. Existing matrix factorization is based
on least squares and aims to yield a low-rank matrix to interpret the
conditional sample means given the observations. However, in many real
applications with skewed and extreme data, least squares cannot explain their
central tendency or tail distributions, yielding undesired estimates. In this
paper, we propose \emph{expectile matrix factorization} by introducing
asymmetric least squares, a key concept in expectile regression analysis, into
the matrix factorization framework. We propose an efficient algorithm to solve
the new problem based on alternating minimization and quadratic programming. We
prove that our algorithm converges to a global optimum and exactly recovers the
true underlying low-rank matrices when noise is zero. For synthetic data with
skewed noise and a real-world dataset containing web service response times,
the proposed scheme achieves lower recovery errors than the existing matrix
factorization method based on least squares in a wide range of settings.Comment: 8 page main text with 5 page supplementary documents, published in
AAAI 201
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
Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline
Recommender systems constitute the core engine of most social network
platforms nowadays, aiming to maximize user satisfaction along with other key
business objectives. Twitter is no exception. Despite the fact that Twitter
data has been extensively used to understand socioeconomic and political
phenomena and user behaviour, the implicit feedback provided by users on Tweets
through their engagements on the Home Timeline has only been explored to a
limited extent. At the same time, there is a lack of large-scale public social
network datasets that would enable the scientific community to both benchmark
and build more powerful and comprehensive models that tailor content to user
interests. By releasing an original dataset of 160 million Tweets along with
engagement information, Twitter aims to address exactly that. During this
release, special attention is drawn on maintaining compliance with existing
privacy laws. Apart from user privacy, this paper touches on the key challenges
faced by researchers and professionals striving to predict user engagements. It
further describes the key aspects of the RecSys 2020 Challenge that was
organized by ACM RecSys in partnership with Twitter using this dataset.Comment: 16 pages, 2 table
Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
The rapid proliferation of new users and items on the social web has
aggravated the gray-sheep user/long-tail item challenge in recommender systems.
Historically, cross-domain co-clustering methods have successfully leveraged
shared users and items across dense and sparse domains to improve inference
quality. However, they rely on shared rating data and cannot scale to multiple
sparse target domains (i.e., the one-to-many transfer setting). This, combined
with the increasing adoption of neural recommender architectures, motivates us
to develop scalable neural layer-transfer approaches for cross-domain learning.
Our key intuition is to guide neural collaborative filtering with
domain-invariant components shared across the dense and sparse domains,
improving the user and item representations learned in the sparse domains. We
leverage contextual invariances across domains to develop these shared modules,
and demonstrate that with user-item interaction context, we can learn-to-learn
informative representation spaces even with sparse interaction data. We show
the effectiveness and scalability of our approach on two public datasets and a
massive transaction dataset from Visa, a global payments technology company
(19% Item Recall, 3x faster vs. training separate models for each domain). Our
approach is applicable to both implicit and explicit feedback settings.Comment: SIGIR 202
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