Deviation-based learning improves recommender systems by abstaining from recommending choices users might follow.
problem Recommender systems learn from user choices but can stall if users blindly follow recommendations.
method The recommender learns user knowledge by observing choices, abstaining from recommending a choice when multiple alternatives produce similar payoffs.
result Learning rate and social welfare improve when the recommender abstains from recommending certain choices.
A learner selects subsets of choices for a user who then picks from them, aiming to minimize regret.
problem Optimizing subset selection for user choices in a stochastic setting.
method Introduces a new problem and defines regret, then proposes algorithms with matching upper and lower bounds.
result Upper and lower bounds on expected regret match up to a logarithmic term, demonstrating algorithm efficiency.
While a user's preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learning recommender models. In particular, existing collaborative filtering (CF) approaches take into account only the binary events of user actions …
Platform learns user preferences to avoid abandonment due to marketing fatigue.
problem Maximizing platform's cumulative payoff while avoiding user abandonment.
method Dynamic sequential choice model to balance exploration and exploitation.
result Proposed algorithm achieves optimal regret bound for online learning.
Algorithm maximizes revenue from user choices with contextual information.
problem Maximizing revenue from user choices with contextual preference information.
method Proposes an algorithm that learns from user feedback and achieves a revenue regret of order \( \widetilde{O}(d \sqrt{K T} / L_0 ) \).
result Achieves a revenue regret of order \( \widetilde{O}(d \sqrt{K T} / L_0 ) \) and a lower bound of order \( \Omega(d \sqrt{T}/ L_0) \).
New model improves website ranking by considering user choices as a whole.
problem Optimizing content ordering for user clicks in website design.
method Introduced multinomial logit (MNL) choice model to LTR framework, proposing UCB algorithms.
result Proved theoretical bounds on regret for UCB algorithms in both known and unknown position parameter settings.
Study personalizes user experience to maximize rewards with patience budget.
problem Maximizing rewards for a platform while respecting user patience.
method Proposes bandit algorithms for sequential choice with feedback models.
result Upper and lower bounds on regret of order O(N2/3) and Ω(N2/3). Examines how algorithms affect user autonomy and information choice.
problem Impact of algorithmic recommendations on user autonomy and free choice.
method Double dichotomy analysis of user intentions and actions, prior and posterior information rearrangement.
result Algorithms can expand or limit user cognitive and social horizons.
Paper extends top-k Mallows model for better user preference analysis.
problem Capturing real-world user preferences focusing on a limited set of items.
method Generalized top-k Mallows model, novel sampling scheme, efficient algorithm, active learning.
result New tools for analysis and prediction in decision-making scenarios.
A recommendation framework helps users choose healthcare interventions.
problem Choice overload in online healthcare communities.
method Multi-Armed Bandit (MAB) approach with innovative model components.
result Our recommendation design outperforms state-of-the-art systems.
Two algorithms optimize assortment selection for user choices in unknown MNL models.
problem Sequential assortment selection with unknown multinomial logit parameters.
method Upper confidence bound algorithms for MNL contextual bandits.
result Optimal regret bounds for assortment selection problems.
When tracking user-specific online activities, each user's preference is revealed in the form of choices and comparisons. For example, a user's purchase history is a record of her choices, i.e. which item was chosen among a subset of offerings. A user's preferences can be observed either explicitly as in movie ratings …
Recent progress in portfolio choice has made a wide class of problems involving transaction costs tractable. We review the basic approach to these problems, and outline some directions for future research.
We study platforms in the sharing economy and discuss the need for incentivizing users to explore options that otherwise would not be chosen. For instance, rental platforms such as Airbnb typically rely on customer reviews to provide users with relevant information about different options. Yet, often a large fraction o…
We study the problem of collaborative filtering where ranking information is available. Focusing on the core of the collaborative ranking process, the user and their community, we propose new models for representation of the underlying permutations and prediction of ranks. The first approach is based on the assumption …
This work tackles slate-based recommender systems using RL, optimizing long-term user engagement.
problem Optimizing long-term user engagement in slate-based recommender systems.
method Developed SLATEQ, a decomposition of RL methods for slate-based recommendations, and outlined a practical methodology.
result SLATEQ decomposes long-term value of a slate into component item-wise long-term values under mild assumptions.
Automates decision-making for human operators managing multiple robots.
problem Limited human operator attention when controlling multiple robots.
method Learned model of user preferences from easy settings to automatically identify the most critical robot.
result Automated decision-making can assist human operators in managing more robots than their attention allows.
This paper analyzes consumer choices over lunchtime restaurants using data from a sample of several thousand anonymous mobile phone users in the San Francisco Bay Area. The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants. We build a model where res…
Bayesian model eliminates feedback loops in personalization systems.
problem Feedback loops in user choice systems based on limited exposure.
method Bayesian choice model based on Luce axioms, fair and efficient.
result Low regret in learning to present, accurate preference estimates with minimal interactions.
RecSim creates customizable simulation environments for RSs.
problem Creating realistic simulation environments for RSs.
method Configurable platform for authoring simulation environments.
result Enables pushing the limits of RL and RS techniques.
This survey reviews portfolio choice in settings where investment opportunities are stochastic due to, e.g., stochastic volatility or return predictability. It is explained how to heuristically compute candidate optimal portfolios using tools from stochastic control, and how to rigorously verify their optimality by mea…
New framework predicts choice with set-related invariances.
problem Accurately predicting choice behavior in large-scale data.
method A learning framework that captures set-related invariances, derived from economics.
result Demonstrated utility on three large choice datasets.
Tutorial on recursive models for predicting path choices.
problem Modeling path choice behavior of network users.
method Recursive discrete choice models.
result Advantages of recursive models over path-based models.
Response time improves alignment with diverse human preferences.
problem Standard aggregation of feedback ignores heterogeneity and anonymity.
method Augmenting feedback with response time data and modeling decisions with DDM.
result Estimator of heterogeneous preferences converges to true average preference.
Modeling incentives for content creators on algorithm-curated platforms.
problem Maximizing exposure for content creators on algorithmic platforms.
method Formalized exposure game model, proving effects of algorithmic choices on equilibria, proposing tools for finding equilibria.
result Algorithmic choices significantly affect content exposure and creator behavior.
We analyze the problem of learning a single user's preferences in an active learning setting, sequentially and adaptively querying the user over a finite time horizon. Learning is conducted via choice-based queries, where the user selects her preferred option among a small subset of offered alternatives. These queries …
FPL allows users to control their data in federated top-N recommendation.
problem Data privacy in recommendation services.
method Federated Learning with learning to rank optimization.
result Users can control the amount of sensitive data shared with a central server.
We develop a method for user-controllable semantic image inpainting: Given an arbitrary set of observed pixels, the unobserved pixels can be imputed in a user-controllable range of possibilities, each of which is semantically coherent and locally consistent with the observed pixels. We achieve this using a deep generat…
Proposes a privacy-preserving recommendation system using matrix factorization and differential privacy.
problem Privacy leakage in recommendation systems when anonymizing user data is not sufficient.
method Uses matrix factorization and differential privacy via the Gaussian mechanism.
result Demonstrates excellent utility for privacy-preserving recommendation systems.
This paper uses Bayesian ARD to automatically determine utility functions for discrete choice models.
problem Challenging and time-consuming task in identifying optimal utility function specifications.
method Bayesian framework and automatic relevance determination (ARD) for data-driven utility function specification.
result The proposed DCM-ARD model accurately recovers true utility function specifications and outperforms previous methods.
Home entertainment systems feature in a variety of usage scenarios with one or more simultaneous users, for whom the complexity of choosing media to consume has increased rapidly over the last decade. Users' decision processes are complex and highly influenced by contextual settings, but data supporting the development…
In this article we provide a formulation of empirical bayes described by Atchade (2011) to tune the hyperparameters of priors used in bayesian set up of collaborative filter. We implement the same in MovieLens small dataset. We see that it can be used to get a good initial choice for the parameters. It can also be used…
FSPO optimizes synthetic preferences for LLM personalization.
problem Personalizing large language models for diverse users.
method FSPO reframes reward modeling as a meta-learning problem, using few labeled preferences and synthetic data.
result FSPO achieves high winrates in personalized responses, both synthetic and real.
A deep learning strategy improves recommendation accuracy by leveraging trust and distrust relationships.
problem Data scarcity and cold-start problem in recommender systems.
method Social deep pairwise learning with a ranking loss function and social negative sampling.
result The proposed model achieves an 11.49% improvement over state-of-the-art methods.
Seeker allows real-time feedback to refine search results.
problem Users struggle to accurately describe desired items in words.
method Interactive refinement of search results through user feedback.
result Seeker improves search quality through user feedback.
Proposes a new framework for resource-limited recommendation.
problem Resource constraints affect user choices in recommendation tasks.
method Interest-behavior multiplicative network with MRRNNs and resource-limited branch.
result Framework effectively predicts user interactions considering resource limitations.
Spectral risk measures (SRMs) are risk measures that take account of user riskaversion, but to date there has been little guidance on the choice of utility function underlying them. This paper addresses this issue by examining alternative approaches based on exponential and power utility functions. A number of problems…
The paper connects discrete choice models to multi-armed bandit algorithms with sublinear regret bounds.
problem Optimizing user choices in a multi-armed bandit setting.
method Establishes connections between discrete choice models and multi-armed bandit algorithms, providing sublinear regret bounds and novel algorithms.
result Sublinear regret bounds for a family of algorithms, including the Exp3 algorithm.
Protects user privacy in models using optional personal data.
problem Ensuring fairness for users who opt-out of data sharing.
method Formalizes protection requirements, introduces Protected User Consent (PUC), devises data augmentation strategy.
result PUC-compliant models can improve performance without disadvantaging opt-out users.
We propose some axioms for hierarchical clustering of probability measures and investigate their ramifications. The basic idea is to let the user stipulate the clusters for some elementary measures. This is done without the need of any notion of metric, similarity or dissimilarity. Our main results then show that for e…
Bayesian optimization agent learns user preferences from pairwise comparisons.
problem Learning user preferences from unknown and infinite choices.
method Sequential Bayesian optimization with pairwise comparisons.
result Optimal agent strategy minimizes remaining system uncertainty.
Model predicts human food choices based on demographics.
problem Predicting human food choices from demographic data.
method Non-deterministic model based on NHANES dataset and behavioral studies.
result Generates synthetic data similar to original dataset.
Representation mixing combines character and phoneme inputs for flexible TTS synthesis.
problem Limited control over pronunciation in character or phoneme-based TTS systems.
method Representation mixing combines multiple linguistic inputs in a single encoder.
result Flexibility in choosing between character, phoneme, or mixed representations during inference.
Professional-grade software applications are powerful but complicated−expert users can achieve impressive results, but novices often struggle to complete even basic tasks. Photo editing is a prime example: after loading a photo, the user is confronted with an array of cryptic sliders like "clarity", "temp", and "high…
GPs' decisions can vary significantly with different kernels, even if kernels are qualitatively similar.
problem Robustness of GP decisions to kernel choice.
method Solved a constrained optimization problem over a finite-dimensional space to identify changes in GP decisions.
result Decisions made with a GP can be non-robust to kernel choice, even with qualitatively similar kernels.
Spectral risk measures are attractive risk measures as they allow the user to obtain risk measures that reflect their risk-aversion functions. To date there has been very little guidance on the choice of risk-aversion functions underlying spectral risk measures. This paper addresses this issue by examining two popular …
The study estimates how changing words in sentences affects audience perception.
problem Estimating the causal effect of lexical choice on audience perception.
method Two classes of methods: quasi-experimental designs and classification problems.
result Algorithmic estimates align with randomized-control trials and can be transferred across domains.
A new algorithm for conversational recommendation systems using dueling bandits in GLMs.
problem Limited user feedback in existing conversational bandit methods.
method Integrates dueling bandits with relative feedback in generalized linear models.
result Theoretical and empirical validation of ConDuel's efficacy.