33 research outputs found
Causal Inference in Microbiomes Using Intervention Calculus
Inferring causal effects is critically important in biomedical research as it allows us to move from the typical paradigm of associational studies to causal inference, and can impact treatments and therapeutics. Association patterns can be coincidental and may lead to wrong inferences in complex systems. Microbiomes are highly complex, diverse, and dynamic environments. Microbes are key players in health and diseases. Hence knowledge of genuine causal relationships among the entities in a microbiome, and the impact of internal and external factors on microbial abundance and interactions are essential for understanding disease mechanisms and making treatment recommendations. In this paper, we investigate fundamental causal inference techniques to measure the causal effects of various entities in a microbiome. In particular, we show how to use these techniques on microbiome datasets to study the rise and impact of antibiotic-resistance in microbiomes. Our main contributions include the following. We introduce a novel pipeline for microbiome studies, new ideas for experimental design under weaker assumptions, and data augmentation by context embedding. Our pipeline is robust, different from traditional approaches, and able to predict interventional effects without any controlled experiments. Our work shows the advantages of causal inference in identifying potential pathogenic, beneficial, and antibiotic-resistant bacteria. We validate our results using results that were previously published
Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback
Recommender systems widely use implicit feedback such as click data because
of its general availability. Although the presence of clicks signals the users'
preference to some extent, the lack of such clicks does not necessarily
indicate a negative response from the users, as it is possible that the users
were not exposed to the items (positive-unlabeled problem). This leads to a
difficulty in predicting the users' preferences from implicit feedback.
Previous studies addressed the positive-unlabeled problem by uniformly
upweighting the loss for the positive feedback data or estimating the
confidence of each data having relevance information via the EM-algorithm.
However, these methods failed to address the missing-not-at-random problem in
which popular or frequently recommended items are more likely to be clicked
than other items even if a user does not have a considerable interest in them.
To overcome these limitations, we first define an ideal loss function to be
optimized to realize recommendations that maximize the relevance and propose an
unbiased estimator for the ideal loss. Subsequently, we analyze the variance of
the proposed unbiased estimator and further propose a clipped estimator that
includes the unbiased estimator as a special case. We demonstrate that the
clipped estimator is expected to improve the performance of the recommender
system, by considering the bias-variance trade-off. We conduct semi-synthetic
and real-world experiments and demonstrate that the proposed method largely
outperforms the baselines. In particular, the proposed method works better for
rare items that are less frequently observed in the training data. The findings
indicate that the proposed method can better achieve the objective of
recommending items with the highest relevance.Comment: accepted at WSDM'2
Debiasing Recommendation with Personal Popularity
Global popularity (GP) bias is the phenomenon that popular items are
recommended much more frequently than they should be, which goes against the
goal of providing personalized recommendations and harms user experience and
recommendation accuracy. Many methods have been proposed to reduce GP bias but
they fail to notice the fundamental problem of GP, i.e., it considers
popularity from a \textit{global} perspective of \textit{all users} and uses a
single set of popular items, and thus cannot capture the interests of
individual users. As such, we propose a user-aware version of item popularity
named \textit{personal popularity} (PP), which identifies different popular
items for each user by considering the users that share similar interests. As
PP models the preferences of individual users, it naturally helps to produce
personalized recommendations and mitigate GP bias. To integrate PP into
recommendation, we design a general \textit{personal popularity aware
counterfactual} (PPAC) framework, which adapts easily to existing
recommendation models. In particular, PPAC recognizes that PP and GP have both
direct and indirect effects on recommendations and controls direct effects with
counterfactual inference techniques for unbiased recommendations. All codes and
datasets are available at \url{https://github.com/Stevenn9981/PPAC}.Comment: Accepted by WWW'24 as a research full pape
Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective
Recommender systems learn from historical user-item interactions to identify
preferred items for target users. These observed interactions are usually
unbalanced following a long-tailed distribution. Such long-tailed data lead to
popularity bias to recommend popular but not personalized items to users. We
present a gradient perspective to understand two negative impacts of popularity
bias in recommendation model optimization: (i) the gradient direction of
popular item embeddings is closer to that of positive interactions, and (ii)
the magnitude of positive gradient for popular items are much greater than that
of unpopular items. To address these issues, we propose a simple yet efficient
framework to mitigate popularity bias from a gradient perspective.
Specifically, we first normalize each user embedding and record accumulated
gradients of users and items via popularity bias measures in model training. To
address the popularity bias issues, we develop a gradient-based embedding
adjustment approach used in model testing. This strategy is generic,
model-agnostic, and can be seamlessly integrated into most existing recommender
systems. Our extensive experiments on two classic recommendation models and
four real-world datasets demonstrate the effectiveness of our method over
state-of-the-art debiasing baselines.Comment: Recommendation System, Popularity Bia