A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New estimators improve Rasch model item parameter estimation for sparse data.
Bayesian method improves adaptive testing item selection, ensuring full item exposure.
A new algorithm estimates item parameters in item response theory models.
Item-item collaborative filtering (CF) models are a well known and studied family of recommender systems, however current literature does not provide any theoretical explanation of the conditions under which item-based recommendations will succeed or fail. We investigate the existence of an ideal item-based CF method a…
Deep learning based knowledge tracing model has been shown to outperform traditional knowledge tracing model without the need for human-engineered features, yet its parameters and representations have long been criticized for not being explainable. In this paper, we propose Deep-IRT which is a synthesis of the item res…
New algorithms for fair item allocation with limited copies.
Given an incomplete ratings data over a set of users and items, the preference completion problem aims to estimate a personalized total preference order over a subset of the items. In practical settings, a ranked list of top- items from the estimated preference order is recommended to the end user in the decreasing …
New model improves recommendation systems by analyzing user-item interactions.
The paper develops a method to estimate consumer preferences from observed rankings.
FedIRT enables privacy-preserving psychometric estimation without centralizing data.
The paper ranks items based on top choices in multiway comparisons.
This paper improves collaborative filtering by integrating user and item embeddings with attention.
This paper studies the problem of inferring a global preference based on the partial rankings provided by many users over different subsets of items according to the Plackett-Luce model. A question of particular interest is how to optimally assign items to users for ranking and how many item assignments are needed to a…
GPIRT uses Gaussian processes to estimate latent traits and IRFs from binary responses.
The paper models and predicts co-occurrence counts using Gamma regression.
Modeling preference rankings with salient features to explain irrational choices.
This paper explores the preference-based top- rank aggregation problem. Suppose that a collection of items is repeatedly compared in pairs, and one wishes to recover a consistent ordering that emphasizes the top- ranked items, based on partially revealed preferences. We focus on the Bradley-Terry-Luce (BTL) model…
Proposes -IRT, a new IRT model with enhanced discrimination estimation.
New Bayesian method for sparse multidimensional item response theory.
Deep learning speeds up IFA estimation for large datasets.
Mechanism design for large item sets using topic models.
DINOSAUR improves retrieval by accounting for embedding uncertainty in recommender systems.
Recommender systems play a central role in providing individualized access to information and services. This paper focuses on collaborative filtering, an approach that exploits the shared structure among mind-liked users and similar items. In particular, we focus on a formal probabilistic framework known as Markov rand…
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 expos…
Model analyzes cooccurrence data for recommender systems and item relevance.
We propose new positive definite kernels for permutations. First we introduce a weighted version of the Kendall kernel, which allows to weight unequally the contributions of different item pairs in the permutations depending on their ranks. Like the Kendall kernel, we show that the weighted version is invariant to rela…
A new framework evaluates large language models efficiently and accurately.
A new estimator reduces bias and variance in ranking policy evaluation.
Pairwise comparison data arises in many domains, including tournament rankings, web search, and preference elicitation. Given noisy comparisons of a fixed subset of pairs of items, we study the problem of estimating the underlying comparison probabilities under the assumption of strong stochastic transitivity (SST). We…
Recommender systems can be formulated as a matrix completion problem, predicting ratings from user and item parameter vectors. Optimizing these parameters by subsampling data becomes difficult as the number of users and items grows. We develop a novel approach to generate all latent variables on demand from the ratings…
A common problem in machine learning is to rank a set of n items based on pairwise comparisons. Here ranking refers to partitioning the items into sets of pre-specified sizes according to their scores, which includes identification of the top-k items as the most prominent special case. The score of a given item is defi…
Traditional recommendation systems rely on past usage data in order to generate new recommendations. Those approaches fail to generate sensible recommendations for new users and items into the system due to missing information about their past interactions. In this paper, we propose a solution for successfully addressi…
Suppose that we wish to estimate a user's preference vector from paired comparisons of the form "does user prefer item or item ?," where both the user and items are embedded in a low-dimensional Euclidean space with distances that reflect user and item similarities. Such observations arise in numerous se…
Item Response Theory (IRT) aims to assess latent abilities of respondents based on the correctness of their answers in aptitude test items with different difficulty levels. In this paper, we propose the -IRT model, which models continuous responses and can generate a much enriched family of Item Characteristic Cur…
Novel method diagnoses large language models' reasoning abilities.
The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user ve…
We consider the problem of learning the qualities of a collection of items by performing noisy comparisons among them. Following the standard paradigm, we assume there is a fixed "comparison graph" and every neighboring pair of items in this graph is compared times according to the Bradley-Terry-Luce model (where t…
Paper proposes a method to estimate true positive proportion without knowing it.
Researchers study how teachers' advising relationships influence their perceptions of satisfaction and students, not policy influence.
Paper proposes unbiased learning for recommendation causal effects.
Paper improves recommendation systems by optimizing sequence of items for clicks.
Recommending items to users is a challenging task due to the large amount of missing information. In many cases, the data solely consist of ratings or tags voluntarily contributed by each user on a very limited subset of the available items, so that most of the data of potential interest is actually missing. Current ap…
A new learning-to-rank approach ensures fairness for item providers in dynamic ranking systems.
The Rasch model is widely used for item response analysis in applications ranging from recommender systems to psychology, education, and finance. While a number of estimators have been proposed for the Rasch model over the last decades, the available analytical performance guarantees are mostly asymptotic. This paper p…
In academic literature, recommender systems are often evaluated on the task of next-item prediction. The procedure aims to give an answer to the question: "Given the natural sequence of user-item interactions up to time t, can we predict which item the user will interact with at time t+1?". Evaluation results obtained …
BLOB combines organic and bandit signals for better user interest estimation.
Unified approach for conversational recommendation by integrating attributes and items.
Latent factor models for Recommender Systems with implicit feedback typically treat unobserved user-item interactions (i.e. missing information) as negative feedback. This is frequently done either through negative sampling (point--wise loss) or with a ranking loss function (pair-- or list--wise estimation). Since a ze…