2,631 research outputs found
Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
This paper proposes a new neural architecture for collaborative ranking with
implicit feedback. Our model, LRML (\textit{Latent Relational Metric Learning})
is a novel metric learning approach for recommendation. More specifically,
instead of simple push-pull mechanisms between user and item pairs, we propose
to learn latent relations that describe each user item interaction. This helps
to alleviate the potential geometric inflexibility of existing metric learing
approaches. This enables not only better performance but also a greater extent
of modeling capability, allowing our model to scale to a larger number of
interactions. In order to do so, we employ a augmented memory module and learn
to attend over these memory blocks to construct latent relations. The
memory-based attention module is controlled by the user-item interaction,
making the learned relation vector specific to each user-item pair. Hence, this
can be interpreted as learning an exclusive and optimal relational translation
for each user-item interaction. The proposed architecture demonstrates the
state-of-the-art performance across multiple recommendation benchmarks. LRML
outperforms other metric learning models by in terms of Hits@10 and
nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover,
qualitative studies also demonstrate evidence that our proposed model is able
to infer and encode explicit sentiment, temporal and attribute information
despite being only trained on implicit feedback. As such, this ascertains the
ability of LRML to uncover hidden relational structure within implicit
datasets.Comment: WWW 201
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Message-Passing Inference on a Factor Graph for Collaborative Filtering
This paper introduces a novel message-passing (MP) framework for the
collaborative filtering (CF) problem associated with recommender systems. We
model the movie-rating prediction problem popularized by the Netflix Prize,
using a probabilistic factor graph model and study the model by deriving
generalization error bounds in terms of the training error. Based on the model,
we develop a new MP algorithm, termed IMP, for learning the model. To show
superiority of the IMP algorithm, we compare it with the closely related
expectation-maximization (EM) based algorithm and a number of other matrix
completion algorithms. Our simulation results on Netflix data show that, while
the methods perform similarly with large amounts of data, the IMP algorithm is
superior for small amounts of data. This improves the cold-start problem of the
CF systems in practice. Another advantage of the IMP algorithm is that it can
be analyzed using the technique of density evolution (DE) that was originally
developed for MP decoding of error-correcting codes
Embarrassingly Shallow Autoencoders for Sparse Data
Combining simple elements from the literature, we define a linear model that
is geared toward sparse data, in particular implicit feedback data for
recommender systems. We show that its training objective has a closed-form
solution, and discuss the resulting conceptual insights. Surprisingly, this
simple model achieves better ranking accuracy than various state-of-the-art
collaborative-filtering approaches, including deep non-linear models, on most
of the publicly available data-sets used in our experiments.Comment: In the proceedings of the Web Conference (WWW) 2019 (7 pages
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