1,529 research outputs found
Sequential Recommendation with Self-Attentive Multi-Adversarial Network
Recently, deep learning has made significant progress in the task of
sequential recommendation. Existing neural sequential recommenders typically
adopt a generative way trained with Maximum Likelihood Estimation (MLE). When
context information (called factor) is involved, it is difficult to analyze
when and how each individual factor would affect the final recommendation
performance. For this purpose, we take a new perspective and introduce
adversarial learning to sequential recommendation. In this paper, we present a
Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the
effect of context information on sequential recommendation. Specifically, our
proposed MFGAN has two kinds of modules: a Transformer-based generator taking
user behavior sequences as input to recommend the possible next items, and
multiple factor-specific discriminators to evaluate the generated sub-sequence
from the perspectives of different factors. To learn the parameters, we adopt
the classic policy gradient method, and utilize the reward signal of
discriminators for guiding the learning of the generator. Our framework is
flexible to incorporate multiple kinds of factor information, and is able to
trace how each factor contributes to the recommendation decision over time.
Extensive experiments conducted on three real-world datasets demonstrate the
superiority of our proposed model over the state-of-the-art methods, in terms
of effectiveness and interpretability
Quaternion-Based Self-Attentive Long Short-Term User Preference Encoding for Recommendation
Quaternion space has brought several benefits over the traditional Euclidean
space: Quaternions (i) consist of a real and three imaginary components,
encouraging richer representations; (ii) utilize Hamilton product which better
encodes the inter-latent interactions across multiple Quaternion components;
and (iii) result in a model with smaller degrees of freedom and less prone to
overfitting. Unfortunately, most of the current recommender systems rely on
real-valued representations in Euclidean space to model either user's long-term
or short-term interests. In this paper, we fully utilize Quaternion space to
model both user's long-term and short-term preferences. We first propose a
QUaternion-based self-Attentive Long term user Encoding (QUALE) to study the
user's long-term intents. Then, we propose a QUaternion-based self-Attentive
Short term user Encoding (QUASE) to learn the user's short-term interests. To
enhance our models' capability, we propose to fuse QUALE and QUASE into one
model, namely QUALSE, by using a Quaternion-based gating mechanism. We further
develop Quaternion-based Adversarial learning along with the Bayesian
Personalized Ranking (QABPR) to improve our model's robustness. Extensive
experiments on six real-world datasets show that our fused QUALSE model
outperformed 11 state-of-the-art baselines, improving 8.43% at HIT@1 and 10.27%
at NDCG@1 on average compared with the best baseline
Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders
While sequential recommender systems achieve significant improvements on
capturing user dynamics, we argue that sequential recommenders are vulnerable
against substitution-based profile pollution attacks. To demonstrate our
hypothesis, we propose a substitution-based adversarial attack algorithm, which
modifies the input sequence by selecting certain vulnerable elements and
substituting them with adversarial items. In both untargeted and targeted
attack scenarios, we observe significant performance deterioration using the
proposed profile pollution algorithm. Motivated by such observations, we design
an efficient adversarial defense method called Dirichlet neighborhood sampling.
Specifically, we sample item embeddings from a convex hull constructed by
multi-hop neighbors to replace the original items in input sequences. During
sampling, a Dirichlet distribution is used to approximate the probability
distribution in the neighborhood such that the recommender learns to combat
local perturbations. Additionally, we design an adversarial training method
tailored for sequential recommender systems. In particular, we represent
selected items with one-hot encodings and perform gradient ascent on the
encodings to search for the worst case linear combination of item embeddings in
training. As such, the embedding function learns robust item representations
and the trained recommender is resistant to test-time adversarial examples.
Extensive experiments show the effectiveness of both our attack and defense
methods, which consistently outperform baselines by a significant margin across
model architectures and datasets.Comment: Accepted to RecSys 202
Learning transferrable parameters for long-tailed sequential user behavior modeling
National Research Foundation (NRF) Singapore under its AI Singapore Programm
DiffuRec: A Diffusion Model for Sequential Recommendation
Mainstream solutions to Sequential Recommendation (SR) represent items with
fixed vectors. These vectors have limited capability in capturing items' latent
aspects and users' diverse preferences. As a new generative paradigm, Diffusion
models have achieved excellent performance in areas like computer vision and
natural language processing. To our understanding, its unique merit in
representation generation well fits the problem setting of sequential
recommendation. In this paper, we make the very first attempt to adapt
Diffusion model to SR and propose DiffuRec, for item representation
construction and uncertainty injection. Rather than modeling item
representations as fixed vectors, we represent them as distributions in
DiffuRec, which reflect user's multiple interests and item's various aspects
adaptively. In diffusion phase, DiffuRec corrupts the target item embedding
into a Gaussian distribution via noise adding, which is further applied for
sequential item distribution representation generation and uncertainty
injection. Afterwards, the item representation is fed into an Approximator for
target item representation reconstruction. In reversion phase, based on user's
historical interaction behaviors, we reverse a Gaussian noise into the target
item representation, then apply rounding operation for target item prediction.
Experiments over four datasets show that DiffuRec outperforms strong baselines
by a large margin
NAIS: Neural Attentive Item Similarity Model for Recommendation
Item-to-item collaborative filtering (aka. item-based CF) has been long used
for building recommender systems in industrial settings, owing to its
interpretability and efficiency in real-time personalization. It builds a
user's profile as her historically interacted items, recommending new items
that are similar to the user's profile. As such, the key to an item-based CF
method is in the estimation of item similarities. Early approaches use
statistical measures such as cosine similarity and Pearson coefficient to
estimate item similarities, which are less accurate since they lack tailored
optimization for the recommendation task. In recent years, several works
attempt to learn item similarities from data, by expressing the similarity as
an underlying model and estimating model parameters by optimizing a
recommendation-aware objective function. While extensive efforts have been made
to use shallow linear models for learning item similarities, there has been
relatively less work exploring nonlinear neural network models for item-based
CF.
In this work, we propose a neural network model named Neural Attentive Item
Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an
attention network, which is capable of distinguishing which historical items in
a user profile are more important for a prediction. Compared to the
state-of-the-art item-based CF method Factored Item Similarity Model (FISM),
our NAIS has stronger representation power with only a few additional
parameters brought by the attention network. Extensive experiments on two
public benchmarks demonstrate the effectiveness of NAIS. This work is the first
attempt that designs neural network models for item-based CF, opening up new
research possibilities for future developments of neural recommender systems
- …