Online reviews provide viewpoints on the strengths and shortcomings of products/services, influencing potential customers' purchasing decisions. However, the proliferation of non-credible reviews -- either fake (promoting/ demoting an item), incompetent (involving irrelevant aspects), or biased -- entails the problem o…
On-device research index
arXiv research
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.
168,742 papers · 148 categories
Trend · papers per month
48 results for “Item strengths”
A method for dynamic ranking using BTL model and nearest neighbor rank centrality.
problem Aggregating evolving pairwise comparisons to recover item strengths over time.
method Adapting Rank Centrality method to handle dynamic pairwise outcomes.
result Consistency of the method in estimating latent strengths over time.
In general, recommendation can be viewed as a matching problem, i.e., match proper items for proper users. However, due to the huge semantic gap between users and items, it's almost impossible to directly match users and items in their initial representation spaces. To solve this problem, many methods have been studied…
The paper studies dynamic ranking and translation synchronization from evolving pairwise comparison graphs.
problem Dynamic pairwise comparison graphs in evolving environments.
method Proposes estimators based on smoothness-penalized least squares and projection onto low frequency eigenspace.
result Finite sample bounds for the estimation error, proving consistency of the proposed methods.
The success of recommender systems in modern online platforms is inseparable from the accurate capture of users' personal tastes. In everyday life, large amounts of user feedback data are created along with user-item online interactions in a variety of ways, such as browsing, purchasing, and sharing. These multiple typ…
A new recommender system uses slates and Thompson Sampling to improve diversity and click rates.
problem Recommending relevant content to users in a dynamic environment.
method Variational Bayesian Recurrent Neural Net, Thompson Sampling, hierarchical prior, in-slate Thompson Sampling.
result Improved click rates and diversity in recommended slates.
Novel method diagnoses large language models' reasoning abilities.
problem Fine-grained evaluation of large language models' reasoning abilities.
method Adapting cognitive diagnosis models to LLMs, estimating mastery profiles and Q-matrix, incorporating textual information.
result Accurate parameter recovery and insights into LLMs' capabilities.
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.
Improved IFA with Generative Adversarial Networks for high-dimensional latent variables.
problem Limited expressiveness of traditional VAEs in high-dimensional latent variable modeling.
method Introducing Adversarial Variational Bayes (AVB) and Importance-weighted Adversarial Variational Bayes (IWAVB) algorithms.
result IWAVB demonstrated superior expressiveness and higher likelihood compared to IWAE.
Optimizes decision-making in dueling bandits with contextual features.
problem Identifying the best arm in dueling bandits with contextual features.
method Develops algorithms for minimizing regret in stochastic contextual dueling bandits.
result Proves optimal regret bounds for contextual dueling bandits.
In this paper, we study the problem of safe online learning to re-rank, where user feedback is used to improve the quality of displayed lists. Learning to rank has traditionally been studied in two settings. In the offline setting, rankers are typically learned from relevance labels created by judges. This approach has…
Bayesian method improves adaptive testing item selection, ensuring full item exposure.
problem Adaptive testing selects items to estimate ability, but must also ensure diverse item exposure.
method Formulated as Bayesian model averaging, deriving optimal item sampling probabilities.
result Stochastic method achieves full item bank exposure without sacrificing accuracy.
The item cold-start problem seriously limits the recommendation performance of Collaborative Filtering (CF) methods when new items have either none or very little interactions. To solve this issue, many modern Internet applications propose to predict a new item's interaction from the possessing contents. However, it is…
Recommending new items to existing users has remained a challenging problem due to absence of user's past preferences for these items. The user personalized non-collaborative methods based on item features can be used to address this item cold-start problem. These methods rely on similarities between the target item an…
In recommender systems, the user-item interaction data is usually sparse and not sufficient for learning comprehensive user/item representations for recommendation. To address this problem, we propose a novel dual-bridging recommendation model (DBRec). DBRec performs latent user/item group discovery simultaneously with…
Optimal recommendation system using user and item clustering.
problem Maximizing recommendation accuracy with limited feedback.
method Latent variable model with user and item clustering, exploiting i.i.d. structure.
result Near-optimal algorithm that combines item and user structures.
Active learning improves ordering of items with contextual attributes.
problem Learning accurate item orderings from pairwise comparisons, especially when exhaustive comparisons are impractical.
method Proposes an active learning strategy that samples items to minimize expected ordering error, accounting for uncertainty in comparisons.
result Superior sample efficiency and generalization compared to non-contextual ranking approaches and active preference learning baselines.
We propose a novel learning framework to answer questions such as "if a user is purchasing a shirt, what other items will (s)he need with the shirt?" Our framework learns distributed representations for items from available textual data, with the learned representations representing items in a latent space expressing f…
Measuring Item Similarity in Introductory Programming: Python and Robot Programming Case Studiescs.CY
A personalized learning system needs a large pool of items for learners to solve. When working with a large pool of items, it is useful to measure the similarity of items. We outline a general approach to measuring the similarity of items and discuss specific measures for items used in introductory programming. Evaluat…
In recommender systems, cold-start issues are situations where no previous events, e.g. ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new ite…
This paper optimizes the number of comparisons needed to find the best k items from pairwise comparisons.
problem Finding the best k items from pairwise comparisons with limited comparisons.
method Developed algorithms for finding probably approximately correct and exact best k items under stochastic conditions.
result Upper and lower bounds on the number of comparisons for finding the best k items, with matching upper bounds for PAC best k items.
Two methods improve 10-K item segmentation using large language models.
problem Challenges in extracting specific items from 10-K reports due to variations in document formats and item presentation.
method Two advanced item segmentation methods: GPT4ItemSeg and BERT4ItemSeg.
result BERT4ItemSeg achieves a macro-F1 of 0.9825, surpassing other methods.
A new algorithm estimates item parameters in item response theory models.
problem Estimating item parameters in item response theory models.
method Computation of the stationary distribution of a Markov chain defined on an item-item graph.
result Our algorithm is consistent and enjoys favorable optimality properties.
Much of the data being created on the web contains interactions between users and items. Stochastic blockmodels, and other methods for community detection and clustering of bipartite graphs, can infer latent user communities and latent item clusters from this interaction data. These methods, however, typically ignore t…
We consider an online model for recommendation systems, with each user being recommended an item at each time-step and providing 'like' or 'dislike' feedback. Each user may be recommended a given item at most once. A latent variable model specifies the user preferences: both users and items are clustered into types. Al…
Next basket recommendation, which aims to predict the next a few items that a user most probably purchases given his historical transactions, plays a vital role in market basket analysis. From the viewpoint of item, an item could be purchased by different users together with different items, for different reasons. Ther…
Algorithm clusters items by sequentially selecting features, minimizing observations.
problem Clustering items based on bandit feedback with many features.
method Sequential Halving algorithm for feature selection.
result Accurate recovery of item partition with minimal observations.
System segments Form 10-K documents into Item sections for financial analysis.
problem Segmenting Form 10-K documents into Item sections for efficient financial analysis.
method Developed an automatic Form 10-K Itemization system using NLP techniques.
result System achieves a retrieval rate of 93% for segmenting Item sections.
New model improves recommendation systems by analyzing user-item interactions.
problem Improving recommendation systems for better user-item interactions.
method Sliced Anti-symmetric Decomposition (SAD) model using tensor decomposition.
result SAD produces the most consistent personalized preferences compared to SOTA models.
In this paper we consider an online recommendation setting, where a platform recommends a sequence of items to its users at every time period. The users respond by selecting one of the items recommended or abandon the platform due to fatigue from seeing less useful items. Assuming a parametric stochastic model of user …
Recommenders personalize the web content by typically using collaborative filtering to relate users (or items) based on explicit feedback, e.g., ratings. The difficulty of collecting this feedback has recently motivated to consider implicit feedback (e.g., item consumption along with the corresponding time). In this pa…
Algorithm identifies best item from subsets with random utility model feedback.
problem PAC learning the best item from subsets with random utility model feedback.
method Pairwise relative counts and hierarchical elimination for learning algorithm.
result Near-optimal PAC sample complexity guarantee for identifying ε-optimal item.
Adaptive cascade submodular maximization tackles sequential selection under uncertainty.
problem Maximizing expected utility from a set of items with unknown states and continuation probabilities.
method Proposed adaptive cascade submodular functions and a 0.12 approximation algorithm.
result Identified a class of functions (adaptive cascade submodular) that many practical applications satisfy.
In this paper, we consider decentralized sequential decision making in distributed online recommender systems, where items are recommended to users based on their search query as well as their specific background including history of bought items, gender and age, all of which comprise the context information of the use…
A new scoring function improves ordinal regression by combining binary predictions.
problem Improving ordinal regression prediction accuracy.
method Introduces a cumulative sum scoring function to combine binary predictions.
result Simplified formulation and two online learning algorithms converge under rank separability condition.
Collaborative filtering is used to recommend items to a user without requiring a knowledge of the item itself and tends to outperform other techniques. However, collaborative filtering suffers from the cold-start problem, which occurs when an item has not yet been rated or a user has not rated any items. Incorporating …
New algorithms for fair item allocation with limited copies.
problem Fair division of numerous items with few copies.
method Modeling as a contextual bandit problem with sub-linear regret guarantees.
result Proposed algorithms achieve sub-linear regret in fair item allocation.
We consider the problem of learning soft assignments of items to categories given two sources of information: an item-category similarity matrix, which encourages items to be assigned to categories they are similar to (and to not be assigned to categories they are dissimilar to), and an item-item similarity mat…
New estimators improve Rasch model item parameter estimation for sparse data.
problem Estimating item parameters in sparse Rasch model data.
method Random pairing maximum likelihood estimator (RP-MLE) and its bootstrapped variant (MRP-MLE).
result RP-MLE and MRP-MLE are minimax optimal and provide precise item parameter estimates.
Study ranking in generalized linear bandits with position and item dependencies.
problem Complex reward function due to position and item dependencies in recommendation systems.
method Model position and item dependencies, design UCB and Thompson Sampling algorithms.
result Generalizes existing studies in position dependencies and graph theory.
Most recommender systems recommend a list of items. The user examines the list, from the first item to the last, and often chooses the first attractive item and does not examine the rest. This type of user behavior can be modeled by the cascade model. In this work, we study cascading bandits, an online learning variant…
Recommender System research suffers currently from a disconnect between the size of academic data sets and the scale of industrial production systems. In order to bridge that gap we propose to generate more massive user/item interaction data sets by expanding pre-existing public data sets. User/item incidence matrices …
Gradient optimization improves preference elicitation for large item spaces.
problem Computational infeasibility of EVOI for large item spaces in recommender systems.
method Continuous formulation of EVOI as a differentiable network, optimized using gradient methods.
result Gradient-based EVOI optimization achieves state-of-the-art performance and scalability.
Most of the existing recommender systems use the ratings provided by users on individual items. An additional source of preference information is to use the ratings that users provide on sets of items. The advantages of using preferences on sets are two-fold. First, a rating provided on a set conveys some preference in…
Improved item recommendations for repeat interactions using sequence analysis.
problem Limited effectiveness of traditional recommender systems in handling repeated user-item interactions.
method Designed a recommender system that considers sequences of item interactions for each user.
result Empirically shown to give highly accurate predictions and increase sales by 5%.
There is much empirical evidence that item-item collaborative filtering works well in practice. Motivated to understand this, we provide a framework to design and analyze various recommendation algorithms. The setup amounts to online binary matrix completion, where at each time a random user requests a recommendation a…
A new dataset tracks user interactions and click responses in online marketplaces.
problem Lack of exposure data in recommender systems datasets.
method Proposes a novel dataset including slates and click responses, allowing more accurate likelihood models.
result Models using exposure data show more natural likelihood, reducing bias towards previously exposed items.
Industry-scale recommendation systems have become a cornerstone of the e-commerce shopping experience. For Etsy, an online marketplace with over 50 million handmade and vintage items, users come to rely on personalized recommendations to surface relevant items from its massive inventory. One hallmark of Etsy's shopping…