Obtaining Calibrated Probabilities with Personalized Ranking Models

For personalized ranking models, the well-calibrated probability of an item being preferred by a user has great practical value. While existing work shows promising results in image classification, probability calibration has not been much explored for personalized ranking. In this paper, we aim to estimate the calibrated probability of how likely a user will prefer an item. We investigate various parametric distributions and propose two parametric calibration methods, namely Gaussian calibration and Gamma calibration. Each proposed method can be seen as a post-processing function that maps the ranking scores of pre-trained models to well-calibrated preference probabilities, without affecting the recommendation performance. We also design the unbiased empirical risk minimization framework that guides the calibration methods to learning of true preference probability from the biased user-item interaction dataset. Extensive evaluations with various personalized ranking models on real-world datasets show that both the proposed calibration methods and the unbiased empirical risk minimization significantly improve the calibration performance.Comment: AAAI 2022 Ora

MvFS: Multi-view Feature Selection for Recommender System

Feature selection, which is a technique to select key features in recommender systems, has received increasing research attention. Recently, Adaptive Feature Selection (AdaFS) has shown remarkable performance by adaptively selecting features for each data instance, considering that the importance of a given feature field can vary significantly across data. However, this method still has limitations in that its selection process could be easily biased to major features that frequently occur. To address these problems, we propose Multi-view Feature Selection (MvFS), which selects informative features for each instance more effectively. Most importantly, MvFS employs a multi-view network consisting of multiple sub-networks, each of which learns to measure the feature importance of a part of data with different feature patterns. By doing so, MvFS mitigates the bias problem towards dominant patterns and promotes a more balanced feature selection process. Moreover, MvFS adopts an effective importance score modeling strategy which is applied independently to each field without incurring dependency among features. Experimental results on real-world datasets demonstrate the effectiveness of MvFS compared to state-of-the-art baselines.Comment: CIKM 202

Learning Topology-Specific Experts for Molecular Property Prediction

Recently, graph neural networks (GNNs) have been successfully applied to predicting molecular properties, which is one of the most classical cheminformatics tasks with various applications. Despite their effectiveness, we empirically observe that training a single GNN model for diverse molecules with distinct structural patterns limits its prediction performance. In this paper, motivated by this observation, we propose TopExpert to leverage topology-specific prediction models (referred to as experts), each of which is responsible for each molecular group sharing similar topological semantics. That is, each expert learns topology-specific discriminative features while being trained with its corresponding topological group. To tackle the key challenge of grouping molecules by their topological patterns, we introduce a clustering-based gating module that assigns an input molecule into one of the clusters and further optimizes the gating module with two different types of self-supervision: topological semantics induced by GNNs and molecular scaffolds, respectively. Extensive experiments demonstrate that TopExpert has boosted the performance for molecular property prediction and also achieved better generalization for new molecules with unseen scaffolds than baselines. The code is available at https://github.com/kimsu55/ToxExpert.Comment: 11 pages with 8 figure

Plasmonic organic solar cell and its absorption enhancement analysis using cylindrical Ag nano-particle model based on finite difference time domain (FDTD)

We report the plasmon-assisted photocurrent enhancement in Ag nanoparticles (NPs)-embedded PEDOT:PSS/P3HT:PCBM organic solar cells, and theoretically investigate the causes of the improved optical absorption based on a cylindrical Ag-NPs model which is simulated with a finite difference time domain (FDTD) method. The proposed cylindrical Ag-NPs model is able to explain the optical absorption enhancement by the localized surface plasmon resonance (LSPR) modes, and to provide a further understanding of Ag-NPs shape parameters which play an important role to determine the broadband absorption phenomena in plasmonic organic solar cells

Multi-Modal Component Embedding for Fake News Detection

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Bidirectional Distillation for Top-K Recommender System

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Item-side ranking regularized distillation for recommender system

Recent recommender system (RS) have adopted large and sophisticated model architecture to better understand the complex user-item relationships, and accordingly, the size of the recommender is continuously increasing. To reduce the high inference costs of the large recommender, knowledge distillation (KD), which is a model compression technique from a large pre-trained model (teacher) to a small model (student), has been actively studied for RS. The state-of-the-art method is based on the ranking distillation approach, which makes the student preserve the ranking orders among items predicted by the teacher. In this work, we propose a new regularization method designed to maximize the effect of the ranking distillation in RS. We first point out an important limitation and a room for improvement of the state-of-the-art ranking distillation method based on our in-depth analysis.Then, we introduce the item-side ranking regularization, which can effectively prevent the student with limited capacity from being overfitted and enables the student to more accurately learn the teacher’s prediction results. We validate the superiority of the proposed method by extensive experiments on real-world datasets.11Nsciescopu

Deep Rating Elicitation for New Users in Collaborative Filtering

Recent recommender systems started to use rating elicitation, which asks new users to rate a small seed itemset for inferring their preferences, to improve the quality of initial recommendations. The key challenge of the rating elicitation is to choose the seed items which can best infer the new users' preference. This paper proposes a novel end-to-end Deep learning framework for Rating Elicitation (DRE), that chooses all the seed items at a time with consideration of the non-linear interactions. To this end, it first defines categorical distributions to sample seed items from the entire itemset, then it trains both the categorical distributions and a neural reconstruction network to infer users' preferences on the remaining items from CF information of the sampled seed items. Through the end-to-end training, the categorical distributions are learned to select the most representative seed items while reflecting the complex non-linear interactions. Experimental results show that DRE outperforms the state-of-the-art approaches in the recommendation quality by accurately inferring the new users' preferences and its seed itemset better represents the latent space than the seed itemset obtained by the other methods. © 2020 ACM.1

Topology Distillation for Recommender System

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