15 research outputs found
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
Recommendation systems are ubiquitous and impact many domains; they have the
potential to influence product consumption, individuals' perceptions of the
world, and life-altering decisions. These systems are often evaluated or
trained with data from users already exposed to algorithmic recommendations;
this creates a pernicious feedback loop. Using simulations, we demonstrate how
using data confounded in this way homogenizes user behavior without increasing
utility
Incentivizing Exploration with Selective Data Disclosure
We study the design of rating systems that incentivize (more) efficient
social learning among self-interested agents. Agents arrive sequentially and
are presented with a set of possible actions, each of which yields a positive
reward with an unknown probability. A disclosure policy sends messages about
the rewards of previously-chosen actions to arriving agents. These messages can
alter agents' incentives towards exploration, taking potentially sub-optimal
actions for the sake of learning more about their rewards. Prior work achieves
much progress with disclosure policies that merely recommend an action to each
user, but relies heavily on standard, yet very strong rationality assumptions.
We study a particular class of disclosure policies that use messages, called
unbiased subhistories, consisting of the actions and rewards from a subsequence
of past agents. Each subsequence is chosen ahead of time, according to a
predetermined partial order on the rounds. We posit a flexible model of
frequentist agent response, which we argue is plausible for this class of
"order-based" disclosure policies. We measure the success of a policy by its
regret, i.e., the difference, over all rounds, between the expected reward of
the best action and the reward induced by the policy. A disclosure policy that
reveals full history in each round risks inducing herding behavior among the
agents, and typically has regret linear in the time horizon . Our main
result is an order-based disclosure policy that obtains regret
. This regret is known to be optimal in the worst case
over reward distributions, even absent incentives. We also exhibit simpler
order-based policies with higher, but still sublinear, regret. These policies
can be interpreted as dividing a sublinear number of agents into constant-sized
focus groups, whose histories are then revealed to future agents
Breaking Feedback Loops in Recommender Systems with Causal Inference
Recommender systems play a key role in shaping modern web ecosystems. These
systems alternate between (1) making recommendations (2) collecting user
responses to these recommendations, and (3) retraining the recommendation
algorithm based on this feedback. During this process the recommender system
influences the user behavioral data that is subsequently used to update it,
thus creating a feedback loop. Recent work has shown that feedback loops may
compromise recommendation quality and homogenize user behavior, raising ethical
and performance concerns when deploying recommender systems. To address these
issues, we propose the Causal Adjustment for Feedback Loops (CAFL), an
algorithm that provably breaks feedback loops using causal inference and can be
applied to any recommendation algorithm that optimizes a training loss. Our
main observation is that a recommender system does not suffer from feedback
loops if it reasons about causal quantities, namely the intervention
distributions of recommendations on user ratings. Moreover, we can calculate
this intervention distribution from observational data by adjusting for the
recommender system's predictions of user preferences. Using simulated
environments, we demonstrate that CAFL improves recommendation quality when
compared to prior correction methods
DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops
Recommendation models trained on the user feedback collected from deployed
recommendation systems are commonly biased. User feedback is considerably
affected by the exposure mechanism, as users only provide feedback on the items
exposed to them and passively ignore the unexposed items, thus producing
numerous false negative samples. Inevitably, biases caused by such user
feedback are inherited by new models and amplified via feedback loops.
Moreover, the presence of false negative samples makes negative sampling
difficult and introduces spurious information in the user preference modeling
process of the model. Recent work has investigated the negative impact of
feedback loops and unknown exposure mechanisms on recommendation quality and
user experience, essentially treating them as independent factors and ignoring
their cross-effects. To address these issues, we deeply analyze the data
exposure mechanism from the perspective of data iteration and feedback loops
with the Missing Not At Random (\textbf{MNAR}) assumption, theoretically
demonstrating the existence of an available stabilization factor in the
transformation of the exposure mechanism under the feedback loops. We further
propose Dynamic Personalized Ranking (\textbf{DPR}), an unbiased algorithm that
uses dynamic re-weighting to mitigate the cross-effects of exposure mechanisms
and feedback loops without additional information. Furthermore, we design a
plugin named Universal Anti-False Negative (\textbf{UFN}) to mitigate the
negative impact of the false negative problem. We demonstrate theoretically
that our approach mitigates the negative effects of feedback loops and unknown
exposure mechanisms. Experimental results on real-world datasets demonstrate
that models using DPR can better handle bias accumulation and the universality
of UFN in mainstream loss methods
In the Eye of the Beholder: Robust Prediction with Causal User Modeling
Accurately predicting the relevance of items to users is crucial to the
success of many social platforms. Conventional approaches train models on
logged historical data; but recommendation systems, media services, and online
marketplaces all exhibit a constant influx of new content -- making relevancy a
moving target, to which standard predictive models are not robust. In this
paper, we propose a learning framework for relevance prediction that is robust
to changes in the data distribution. Our key observation is that robustness can
be obtained by accounting for how users causally perceive the environment. We
model users as boundedly-rational decision makers whose causal beliefs are
encoded by a causal graph, and show how minimal information regarding the graph
can be used to contend with distributional changes. Experiments in multiple
settings demonstrate the effectiveness of our approach.Comment: Accepted to NeurIPS 202