18 research outputs found
OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization
Exploring the potential of GANs for unsupervised disentanglement learning,
this paper proposes a novel GAN-based disentanglement framework with One-Hot
Sampling and Orthogonal Regularization (OOGAN). While previous works mostly
attempt to tackle disentanglement learning through VAE and seek to implicitly
minimize the Total Correlation (TC) objective with various sorts of
approximation methods, we show that GANs have a natural advantage in
disentangling with an alternating latent variable (noise) sampling method that
is straightforward and robust. Furthermore, we provide a brand-new perspective
on designing the structure of the generator and discriminator, demonstrating
that a minor structural change and an orthogonal regularization on model
weights entails an improved disentanglement. Instead of experimenting on simple
toy datasets, we conduct experiments on higher-resolution images and show that
OOGAN greatly pushes the boundary of unsupervised disentanglement.Comment: AAAI 202
VIP5: Towards Multimodal Foundation Models for Recommendation
Computer Vision (CV), Natural Language Processing (NLP), and Recommender
Systems (RecSys) are three prominent AI applications that have traditionally
developed independently, resulting in disparate modeling and engineering
methodologies. This has impeded the ability for these fields to directly
benefit from each other's advancements. With the recent development of
foundation models, large language models have emerged as a potential
general-purpose interface for unifying different modalities and problem
formulations. In light of this, we propose the development of a multimodal
foundation model (MFM) considering visual, textual, and personalization
modalities under the P5 recommendation paradigm, thus named VIP5 (Visual P5),
to unify various modalities and recommendation tasks. This will enable the
processing of multiple modalities in a shared architecture for improved
recommendations. To achieve this, we introduce multimodal personalized prompts
to accommodate multiple modalities under a shared format. Additionally, we
propose a parameter-efficient training method for foundation models, which
involves freezing the P5 backbone and fine-tuning lightweight adapters,
resulting in improved recommendation performance and increased efficiency in
terms of training time and memory usage. Code and data of VIP5 are available at
https://github.com/jeykigung/VIP5.Comment: Accepted by EMNLP 202
Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
Recent advances in personalized recommendation have sparked great interest in
the exploitation of rich structured information provided by knowledge graphs.
Unlike most existing approaches that only focus on leveraging knowledge graphs
for more accurate recommendation, we perform explicit reasoning with knowledge
for decision making so that the recommendations are generated and supported by
an interpretable causal inference procedure. To this end, we propose a method
called Policy-Guided Path Reasoning (PGPR), which couples recommendation and
interpretability by providing actual paths in a knowledge graph. Our
contributions include four aspects. We first highlight the significance of
incorporating knowledge graphs into recommendation to formally define and
interpret the reasoning process. Second, we propose a reinforcement learning
(RL) approach featuring an innovative soft reward strategy, user-conditional
action pruning and a multi-hop scoring function. Third, we design a
policy-guided graph search algorithm to efficiently and effectively sample
reasoning paths for recommendation. Finally, we extensively evaluate our method
on several large-scale real-world benchmark datasets, obtaining favorable
results compared with state-of-the-art methods.Comment: Accepted in SIGIR 201
Causal Collaborative Filtering
Recommender systems are important and valuable tools for many personalized
services. Collaborative Filtering (CF) algorithms -- among others -- are
fundamental algorithms driving the underlying mechanism of personalized
recommendation. Many of the traditional CF algorithms are designed based on the
fundamental idea of mining or learning correlative patterns from data for
matching, including memory-based methods such as user/item-based CF as well as
learning-based methods such as matrix factorization and deep learning models.
However, advancing from correlative learning to causal learning is an important
problem, because causal/counterfactual modeling can help us to think outside of
the observational data for user modeling and personalization. In this paper, we
propose Causal Collaborative Filtering (CCF) -- a general framework for
modeling causality in collaborative filtering and recommendation. We first
provide a unified causal view of CF and mathematically show that many of the
traditional CF algorithms are actually special cases of CCF under simplified
causal graphs. We then propose a conditional intervention approach for
-calculus so that we can estimate the causal relations based on
observational data. Finally, we further propose a general counterfactual
constrained learning framework for estimating the user-item preferences.
Experiments are conducted on two types of real-world datasets -- traditional
and randomized trial data -- and results show that our framework can improve
the recommendation performance of many CF algorithms.Comment: 14 pages, 5 figures, 3 table
Learning Personalized Risk Preferences for Recommendation
The rapid growth of e-commerce has made people accustomed to shopping online.
Before making purchases on e-commerce websites, most consumers tend to rely on
rating scores and review information to make purchase decisions. With this
information, they can infer the quality of products to reduce the risk of
purchase. Specifically, items with high rating scores and good reviews tend to
be less risky, while items with low rating scores and bad reviews might be
risky to purchase. On the other hand, the purchase behaviors will also be
influenced by consumers' tolerance of risks, known as the risk attitudes.
Economists have studied risk attitudes for decades. These studies reveal that
people are not always rational enough when making decisions, and their risk
attitudes may vary in different circumstances.
Most existing works over recommendation systems do not consider users' risk
attitudes in modeling, which may lead to inappropriate recommendations to
users. For example, suggesting a risky item to a risk-averse person or a
conservative item to a risk-seeking person may result in the reduction of user
experience. In this paper, we propose a novel risk-aware recommendation
framework that integrates machine learning and behavioral economics to uncover
the risk mechanism behind users' purchasing behaviors. Concretely, we first
develop statistical methods to estimate the risk distribution of each item and
then draw the Nobel-award winning Prospect Theory into our model to learn how
users choose from probabilistic alternatives that involve risks, where the
probabilities of the outcomes are uncertain. Experiments on several e-commerce
datasets demonstrate that our approach can achieve better performance than many
classical recommendation approaches, and further analyses also verify the
advantages of risk-aware recommendation beyond accuracy