2,167 research outputs found
Leveraging Long and Short-term Information in Content-aware Movie Recommendation
Movie recommendation systems provide users with ranked lists of movies based
on individual's preferences and constraints. Two types of models are commonly
used to generate ranking results: long-term models and session-based models.
While long-term models represent the interactions between users and movies that
are supposed to change slowly across time, session-based models encode the
information of users' interests and changing dynamics of movies' attributes in
short terms. In this paper, we propose an LSIC model, leveraging Long and
Short-term Information in Content-aware movie recommendation using adversarial
training. In the adversarial process, we train a generator as an agent of
reinforcement learning which recommends the next movie to a user sequentially.
We also train a discriminator which attempts to distinguish the generated list
of movies from the real records. The poster information of movies is integrated
to further improve the performance of movie recommendation, which is
specifically essential when few ratings are available. The experiments
demonstrate that the proposed model has robust superiority over competitors and
sets the state-of-the-art. We will release the source code of this work after
publication
Mixture-of-tastes Models for Representing Users with Diverse Interests
Most existing recommendation approaches implicitly treat user tastes as
unimodal, resulting in an average-of-tastes representations when multiple
distinct interests are present. We show that appropriately modelling the
multi-faceted nature of user tastes through a mixture-of-tastes model leads to
large increases in recommendation quality. Our result holds both for deep
sequence-based and traditional factorization models, and is robust to careful
selection and tuning of baseline models. In sequence-based models, this
improvement is achieved at a very modest cost in model complexity, making
mixture-of-tastes models a straightforward improvement on existing baselines
Metric Factorization: Recommendation beyond Matrix Factorization
In the past decade, matrix factorization has been extensively researched and
has become one of the most popular techniques for personalized recommendations.
Nevertheless, the dot product adopted in matrix factorization based recommender
models does not satisfy the inequality property, which may limit their
expressiveness and lead to sub-optimal solutions. To overcome this problem, we
propose a novel recommender technique dubbed as {\em Metric Factorization}. We
assume that users and items can be placed in a low dimensional space and their
explicit closeness can be measured using Euclidean distance which satisfies the
inequality property. To demonstrate its effectiveness, we further designed two
variants of metric factorization with one for rating estimation and the other
for personalized item ranking. Extensive experiments on a number of real-world
datasets show that our approach outperforms existing state-of-the-art by a
large margin on both rating prediction and item ranking tasks.Comment: 12 page
Variational Autoencoders for Collaborative Filtering
We extend variational autoencoders (VAEs) to collaborative filtering for
implicit feedback. This non-linear probabilistic model enables us to go beyond
the limited modeling capacity of linear factor models which still largely
dominate collaborative filtering research.We introduce a generative model with
multinomial likelihood and use Bayesian inference for parameter estimation.
Despite widespread use in language modeling and economics, the multinomial
likelihood receives less attention in the recommender systems literature. We
introduce a different regularization parameter for the learning objective,
which proves to be crucial for achieving competitive performance. Remarkably,
there is an efficient way to tune the parameter using annealing. The resulting
model and learning algorithm has information-theoretic connections to maximum
entropy discrimination and the information bottleneck principle. Empirically,
we show that the proposed approach significantly outperforms several
state-of-the-art baselines, including two recently-proposed neural network
approaches, on several real-world datasets. We also provide extended
experiments comparing the multinomial likelihood with other commonly used
likelihood functions in the latent factor collaborative filtering literature
and show favorable results. Finally, we identify the pros and cons of employing
a principled Bayesian inference approach and characterize settings where it
provides the most significant improvements.Comment: 10 pages, 3 figures. WWW 201
Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback
Recommender systems (RSs) provide an effective way of alleviating the
information overload problem by selecting personalized items for different
users. Latent factors based collaborative filtering (CF) has become the popular
approaches for RSs due to its accuracy and scalability. Recently, online social
networks and user-generated content provide diverse sources for recommendation
beyond ratings. Although {\em social matrix factorization} (Social MF) and {\em
topic matrix factorization} (Topic MF) successfully exploit social relations
and item reviews, respectively, both of them ignore some useful information. In
this paper, we investigate the effective data fusion by combining the
aforementioned approaches. First, we propose a novel model {\em \mbox{MR3}} to
jointly model three sources of information (i.e., ratings, item reviews, and
social relations) effectively for rating prediction by aligning the latent
factors and hidden topics. Second, we incorporate the implicit feedback from
ratings into the proposed model to enhance its capability and to demonstrate
its flexibility. We achieve more accurate rating prediction on real-life
datasets over various state-of-the-art methods. Furthermore, we measure the
contribution from each of the three data sources and the impact of implicit
feedback from ratings, followed by the sensitivity analysis of hyperparameters.
Empirical studies demonstrate the effectiveness and efficacy of our proposed
model and its extension.Comment: 27 pages, 11 figures, 6 tables, ACM TKDD 201
Addressing Class-Imbalance Problem in Personalized Ranking
Pairwise ranking models have been widely used to address recommendation
problems. The basic idea is to learn the rank of users' preferred items through
separating items into \emph{positive} samples if user-item interactions exist,
and \emph{negative} samples otherwise. Due to the limited number of observable
interactions, pairwise ranking models face serious \emph{class-imbalance}
issues. Our theoretical analysis shows that current sampling-based methods
cause the vertex-level imbalance problem, which makes the norm of learned item
embeddings towards infinite after a certain training iterations, and
consequently results in vanishing gradient and affects the model inference
results. We thus propose an efficient \emph{\underline{Vi}tal
\underline{N}egative \underline{S}ampler} (VINS) to alleviate the
class-imbalance issue for pairwise ranking model, in particular for deep
learning models optimized by gradient methods. The core of VINS is a bias
sampler with reject probability that will tend to accept a negative candidate
with a larger degree weight than the given positive item. Evaluation results on
several real datasets demonstrate that the proposed sampling method speeds up
the training procedure 30\% to 50\% for ranking models ranging from shallow to
deep, while maintaining and even improving the quality of ranking results in
top-N item recommendation.Comment: Preprin
Collaborative Filtering with Stability
Collaborative filtering (CF) is a popular technique in today's recommender
systems, and matrix approximation-based CF methods have achieved great success
in both rating prediction and top-N recommendation tasks. However, real-world
user-item rating matrices are typically sparse, incomplete and noisy, which
introduce challenges to the algorithm stability of matrix approximation, i.e.,
small changes in the training data may significantly change the models. As a
result, existing matrix approximation solutions yield low generalization
performance, exhibiting high error variance on the training data, and
minimizing the training error may not guarantee error reduction on the test
data. This paper investigates the algorithm stability problem of matrix
approximation methods and how to achieve stable collaborative filtering via
stable matrix approximation. We present a new algorithm design framework, which
(1) introduces new optimization objectives to guide stable matrix approximation
algorithm design, and (2) solves the optimization problem to obtain stable
approximation solutions with good generalization performance. Experimental
results on real-world datasets demonstrate that the proposed method can achieve
better accuracy compared with state-of-the-art matrix approximation methods and
ensemble methods in both rating prediction and top-N recommendation tasks
TransRev: Modeling Reviews as Translations from Users to Items
The text of a review expresses the sentiment a customer has towards a
particular product. This is exploited in sentiment analysis where machine
learning models are used to predict the review score from the text of the
review. Furthermore, the products costumers have purchased in the past are
indicative of the products they will purchase in the future. This is what
recommender systems exploit by learning models from purchase information to
predict the items a customer might be interested in. We propose TransRev, an
approach to the product recommendation problem that integrates ideas from
recommender systems, sentiment analysis, and multi-relational learning into a
joint learning objective. TransRev learns vector representations for users,
items, and reviews. The embedding of a review is learned such that (a) it
performs well as input feature of a regression model for sentiment prediction;
and (b) it always translates the reviewer embedding to the embedding of the
reviewed items. This allows TransRev to approximate a review embedding at test
time as the difference of the embedding of each item and the user embedding.
The approximated review embedding is then used with the regression model to
predict the review score for each item. TransRev outperforms state of the art
recommender systems on a large number of benchmark data sets. Moreover, it is
able to retrieve, for each user and item, the review text from the training set
whose embedding is most similar to the approximated review embedding
fastFM: A Library for Factorization Machines
Factorization Machines (FM) are only used in a narrow range of applications
and are not part of the standard toolbox of machine learning models. This is a
pity, because even though FMs are recognized as being very successful for
recommender system type applications they are a general model to deal with
sparse and high dimensional features. Our Factorization Machine implementation
provides easy access to many solvers and supports regression, classification
and ranking tasks. Such an implementation simplifies the use of FM's for a wide
field of applications. This implementation has the potential to improve our
understanding of the FM model and drive new development.Comment: Source Code is available at https://github.com/ibayer/fastF
Adversarial Collaborative Auto-encoder for Top-N Recommendation
During the past decade, model-based recommendation methods have evolved from
latent factor models to neural network-based models. Most of these techniques
mainly focus on improving the overall performance, such as the root mean square
error for rating predictions and hit ratio for top-N recommendation, where the
users' feedback is considered as the ground-truth. However, in real-world
applications, the users' feedback is possibly contaminated by imperfect user
behaviours, namely, careless preference selection. Such data contamination
poses challenges on the design of robust recommendation methods. In this work,
to address the above issue, we propose a general adversial training framework
for neural network-based recommendation models, which improves both the model
robustness and the overall performance. We point out the tradeoffs between
performance and robustness enhancement with detailed instructions on how to
strike a balance. Specifically, we implement our approach on the collaborative
auto-encoder, followed by experiments on three public available datasets:
MovieLens-1M, Ciao, and FilmTrust. We show that our approach outperforms highly
competitive state-of-the-art recommendation methods. In addition, we carry out
a thorough analysis on the noise impacts, as well as the complex interactions
between model nonlinearity and noise levels. Through simple modifications, our
adversarial training framework can be applied to a host of neural network-based
models whose robustness and performance are expected to be both enhanced
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