34 citations · 91 across the 23 of their papers we have counts for
13 papers · 1 filter
From Noise to Order: Learning to Rank via Denoising Diffusion
Sajad Ebrahimi, Bhaskar Mitra, Negar Arabzadeh +4
In information retrieval (IR), learning-to-rank (LTR) methods have traditionally limited themselves to discriminative machine learning approaches that model the probability of the…
Counterfactual Multi-player Bandits for Explainable Recommendation Diversification
Yansen Zhang, Bowei He, Xiaokun Zhang +3
Existing recommender systems tend to prioritize items closely aligned with users' historical interactions, inevitably trapping users in the dilemma of ``filter bubble''. Recent eff…
Diffusion-based Contrastive Learning for Sequential Recommendation
Ziqiang Cui, Haolun Wu, Bowei He +2
Contrastive learning has been effectively utilized to enhance the training of sequential recommendation models by leveraging informative self-supervised signals. Most existing appr…
Towards Group-aware Search Success
Haolun Wu, Bhaskar Mitra, Nick Craswell
Traditional measures of search success often overlook the varying information needs of different demographic groups. To address this gap, we introduce a novel metric, named Group-a…
Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval
Haolun Wu, Ofer Meshi, Masrour Zoghi +4
Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, l…
Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives
Fuyuan Lyu, Xing Tang, Dugang Liu +4
Representation learning has been a critical topic in machine learning. In Click-through Rate Prediction, most features are represented as embedding vectors and learned simultaneous…
Result Diversification in Search and Recommendation: A Survey
Haolun Wu, Yansen Zhang, Chen Ma +4
Diversifying return results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of provide…
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation
Haolun Wu, Yingxue Zhang, Chen Ma +4
To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations. Many SOTA methods f…
Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation
Chang Meng, Ziqi Zhao, Wei Guo +6
Multi-types of behaviors (e.g., clicking, adding to cart, purchasing, etc.) widely exist in most real-world recommendation scenarios, which are beneficial to learn users' multi-fac…
Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
Haolun Wu, Chen Ma, Yingxue Zhang +3
Implicit feedback is frequently used for developing personalized recommendation services due to its ubiquity and accessibility in real-world systems. In order to effectively utiliz…
Joint Multisided Exposure Fairness for Recommendation
Haolun Wu, Bhaskar Mitra, Chen Ma +2
Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of…
Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation
Haolun Wu, Chen Ma, Bhaskar Mitra +2
Nowadays, most online services are hosted on multi-stakeholder marketplaces, where consumers and producers may have different objectives. Conventional recommendation systems, howev…
Knowledge-Enhanced Top-K Recommendation in Poincaré Ball
Chen Ma, Liheng Ma, Yingxue Zhang +3
Personalized recommender systems are increasingly important as more content and services become available and users struggle to identify what might interest them. Thanks to the abi…