OpenRaiser/Intern-Atlas huggingface dataset:
Graph-based alignment and uniformity for recommendation
Authors: Liangwei Yang, Zhiwei Liu, Chen Wang, Mingdai Yang, Xiaolong Liu, Jing Ma, Philip S. Yu
Venue: Conference CIKM 2023
Year: 2023

Collaborative filtering-based recommender systems (RecSys) rely on learning representations for users and items to predict preferences accurately. Representation learning on the hypersphere is a promising approach due to its desirable properties, such as alignment and uniformity. However, the sparsity issue arises when it encounters RecSys. To address this issue, we propose a novel approach, graph-based alignment and uniformity (GraphAU), that explicitly considers high-order connectivities in the user-item bipartite graph. GraphAU aligns the user/item embedding to the dense vector representations of high-order neighbors using a neighborhood aggregator, eliminating the need to compute the burdensome alignment to high-order neighborhoods individually. To address the discrepancy in alignment losses, GraphAU includes a layer-wise alignment pooling module to integrate alignment losses layer-wise. Experiments on four datasets show that GraphAU significantly alleviates the sparsity issue and achieves state-of-the-art performance. We open-source GraphAU at https://github.com/YangLiangwei/GraphAU.
Contextual self-attentive temporal point process for physical decommissioning prediction of cloud assets
Authors: Fangkai Yang, Jue Zhang, Lu Wang 0029, Bo Qiao 0001, Di Weng, Xiaoting Qin, Gregory Weber, Durgesh Nandini Das, Srinivasan Rakhunathan, Ranganathan Srikanth, Qingwei Lin, Dongmei Zhang 0001
Venue: Conference KDD 2023
Year: 2023

As cloud computing continues to expand globally, the need for effective management of decommissioned cloud assets in data centers becomes increasingly important. This work focuses on predicting the physical decommissioning date of cloud assets as a crucial component in reverse cloud supply chain management and data center warehouse operation. The decommissioning process is modeled as a contextual self-attentive temporal point process, which incorporates contextual information to model sequences with parallel events and provides more accurate predictions with more seen historical data. We conducted extensive offline and online experiments in 20 sampled data centers. The results show that the proposed methodology achieves the best performance compared with baselines and improves remarkable 94% prediction accuracy in online experiments. This modeling methodology can be extended to other domains with similar workflow-like processes.
Are we really achieving better beyond-accuracy performance in next basket recommendation?
Authors: Ming Li, Yuanna Liu, Sami Jullien, Mozhdeh Ariannezhad, Andrew Yates, Mohammad Aliannejadi, Maarten de Rijke
Venue: Conference SIGIR 2024
Year: 2024

Next basket recommendation ( NBR) is a special type of sequential recommendation that is increasingly receiving attention. So far, most NBR studies have focused on optimizing the accuracy of the recommendation, whereas optimizing for beyond-accuracy metrics, e.g., item fairness and diversity remains largely unexplored. Recent studies into NBR have found a substantial performance difference between recommending repeat items and explore items. Repeat items contribute most of the users' perceived accuracy compared with explore items. Informed by these findings, we identify a potential "short-cut" to optimize for beyond-accuracy metrics while maintaining high accuracy. To leverage and verify the existence of such short-cuts, we propose a plug-and-play two-step repetition-exploration (TREx) framework that treats repeat items and explores items separately, where we design a simple yet highly effective repetition module to ensure high accuracy, while two exploration modules target optimizing only beyond-accuracy metrics. Experiments are performed on two widely-used datasets w.r.t. a range of beyond-accuracy metrics, viz. five fairness metrics and three diversity metrics. Our experimental results show that: (i) we can achieve state-of-the-art performance w.r.t. accuracy via the designed repetition module in TREx; and (ii) the simple TREx framework achieves "better" beyond-accuracy performance than existing sophisticated methods. Prima facie, this appears to be good news: we can achieve high accuracy and improved beyond-accuracy metrics at the same time. However, we argue that the real-world value of our algorithmic solution, TREx, is likely to be limited and reflect on the reasonableness of the evaluation setup. We end up challenging existing evaluation paradigms, particularly in the context of beyond-accuracy metrics, and provide insights for researchers to navigate potential pitfalls and determine reasonable metrics to consider when optimizing for accuracy and beyond-accuracy metrics.
Logic-scaffolding: Personalized aspect-instructed recommendation explanation generation using LLMs
Authors: Behnam Rahdari, Hao Ding, Ziwei Fan, Yifei Ma, Zhuotong Chen, Anoop Deoras, Branislav Kveton
Venue: Conference WSDM 2024
Year: 2024

The unique capabilities of Large Language Models (LLMs), such as the natural language text generation ability, position them as strong candidates for providing explanation for recommendations. However, despite the size of the LLM, most existing models struggle to produce zero-shot explanations reliably. To address this issue, we propose a framework called Logic-Scaffolding, that combines the ideas of aspect-based explanation and chain-of-thought prompting to generate explanations through intermediate reasoning steps. In this paper, we share our experience in building the framework and present an interactive demonstration for exploring our results.
Feature matching for object localization in the presence of uncertainty
Authors: Todd A. Cass
Venue: Conference ICCV 1990
Year: 1990

The author focuses on the central problem of image and model feature matching. In particular he defines a model of the geometrical uncertainty of image features, and devises a tractable algorithm for determining all feasible sets of feature correspondences given the uncertainty tolerances. A key insight into the matching problem provided by this work is that the search for a matching should be guided by analysis of (feasible) transformation space rather than the space of feature correspondences. This is because the author is only interested in maximal feasible match-sets and not the exponentially many subsets of them as found by correspondence space search.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Catadioptric omnidirectional camera
Authors: Shree K. Nayar
Venue: Conference CVPR 1997
Year: 1997

Conventional video cameras have limited fields of view that make them restrictive in a variety of vision applications. There are several ways to enhance the field of view of an imaging system. However, the entire imaging system must have a single effective viewpoint to enable the generation of pure perspective images from a sensed image. A new camera with a hemispherical field of view is presented. Two such cameras can be placed back-to-back, without violating the single viewpoint constraint, to arrive at a truly omnidirectional sensor. Results are presented on the software generation of pure perspective images from an omnidirectional image, given any user-selected viewing direction and magnification. The paper concludes with a discussion on the spatial resolution of the proposed camera.
Well-Behaved, Tunable 3D-Affine Invariants
Authors: Isidore Rigoutsos
Venue: Conference CVPR 1998
Year: 1998

We derive and discuss a set of parametric equations which, when given a convex 3D feature domain, K, will generate affine invariants with the property that the invariants' values are uniformly distributed in the region [0,1]/spl times/[0,1]/spl times/[0,1]. Once the shape of the feature domain K is determined and fixed it is straightforward to compute the values of the parameters and thus the proposed scheme can be tuned to a specific feature domain. The features of all recognizable objects (models) are assumed to be three-dimensional points and uniformly distributed over K. The scheme leads to improved discrimination power, improved computational-load and storage-load balancing and can also be used to determine and identify biases in the database of recognizable models (over-represented constructs of object points). Obvious enhancements produce rigid-transformation and similarity-transformation invariants with the same good distribution properties, making this approach generally applicable.
Fast Recognition Using Adaptive Subdivisions of Transformation Space
Authors: Thomas M. Breuel
Venue: Conference CVPR 1992
Year: 1992

An algorithm, RAST, for solving the bounded error recognition problem efficiently using adaptive subdivisions of transformation space, is presented. The RAST algorithm uses no heuristics and loses no solutions. It is a simple, efficient algorithm combining the ideas of multiresolution matching, Hough transform, search-based recognition, and bounded error recognition. Its performance is better than that of alignment and Hough transform methods, and, as opposed to these methods; RAST finds solutions satisfying simple, well-defined bounded error criteria.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Active photometric stereo
Authors: James J. Clark
Venue: Conference CVPR 1992
Year: 1992

An active vision technique for determining the absolute depth of surfaces is described. The algorithm assumes a very general model for the reflectance properties of the surface, and is valid for most of the shading models commonly used in computer vision work. The algorithm relies on the controlled motion of a point light source, which is not at infinity but relatively close to the surface and to the camera. The sensitivity of the computed depth values to errors in the measured quantities is derived, allowing a confidence measure for the depth to be determined. The confidence measure can aid in the estimation of accurate depth values from multiple image measurements taken over time. A method based on robust estimation that permits an unbiased estimate of the depth values to be obtained is presented. The results of experiments on synthetic and real-world imagery are reported, illustrating the efficacy of the active photometric stereo algorithm.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Multitarget data association with higher-order motion models
Authors: Robert T. Collins
Venue: Conference CVPR 2012
Year: 2012

We present an iterative approximate solution to the multidimensional assignment problem under general cost functions. The method maintains a feasible solution at every step, and is guaranteed to converge. It is similar to the iterated conditional modes (ICM) algorithm, but applied at each step to a block of variables representing correspondences between two adjacent frames, with the optimal conditional mode being calculated exactly as the solution to a two-frame linear assignment problem. Experiments with ground-truthed trajectory data show that the method outperforms both network-flow data association and greedy recursive filtering using a constant velocity motion model.
Articulated pose estimation with parts connectivity using discriminative local oriented contours
Authors: Norimichi Ukita
Venue: Conference CVPR 2012
Year: 2012

This paper proposes contour-based features for articulated pose estimation. Most of recent methods are designed using tree-structured models with appearance evaluation only within the region of each part. While these models allow us to speed up global optimization in localizing the whole parts, useful appearance cues between neighboring parts are missing. Our work focuses on how to evaluate parts connectivity using contour cues. Unlike previous works, we locally evaluate parts connectivity only along the orientation between neighboring parts within where they overlap. This adaptive localization of the features is required for suppressing bad effects due to nuisance edges such as those of background clutter and clothing textures, as well as for reducing computational cost. Discriminative training of the contour features improves estimation accuracy more. Experimental results verify the effectiveness of our contour-based features.
Overview of the face recognition grand challenge
Authors: P. Jonathon Phillips, Patrick J. Flynn, W. Todd Scruggs, Kevin W. Bowyer, Jin Chang, Kevin Hoffman, Joe Marques, Jaesik Min, William J. Worek
Venue: Conference CVPR 2005
Year: 2005

Over the last couple of years, face recognition researchers have been developing new techniques. These developments are being fueled by advances in computer vision techniques, computer design, sensor design, and interest in fielding face recognition systems. Such advances hold the promise of reducing the error rate in face recognition systems by an order of magnitude over Face Recognition Vendor Test (FRVT) 2002 results. The face recognition grand challenge (FRGC) is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with data corpus of 50,000 images. The data consists of 3D scans and high resolution still imagery taken under controlled and uncontrolled conditions. This paper describes the challenge problem, data corpus, and presents baseline performance and preliminary results on natural statistics of facial imagery.
Motion Segmentation and Local Structure
Authors: David Sinclair
Venue: Conference ICCV 1993
Year: 1993

The author presents a means of segmenting independently moving objects from rigid backgrounds and recovering the orientations of the normals of local planar surface patches. The camera motion can be arbitrary with the proviso that the independent image motion violates the epipolar constraint. The method relies on describing the motion of points in the world in terms of their angular velocity relative to the camera. The method is crucially dependent on estimating camera rotation around image directions. An assumption about the likely motion of automatic guided vehicles is used to resolve the two-fold ambiguity in the estimate of the normal of a local planar patch. This allows local scene structure and camera rotation to be estimated. Motion segmentation is demonstrated on a real image sequence.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
High-dimensional similarity search with quantum-assisted variational autoencoder
Authors: Nicholas Gao, Max Wilson 0001, Thomas Vandal, Walter Vinci, Ramakrishna R. Nemani, Eleanor Gilbert Rieffel
Venue: Conference KDD 2020
Year: 2020

Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains an active area of research. Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes. For instance, the Quantum-assisted Variational Autoencoder (QVAE) has been proposed as a quantum enhancement to the discrete VAE. We extend on previous work and study the real-world applicability of a QVAE by presenting a proof-of-concept for similarity search in large-scale high-dimensional datasets. While exact and fast similarity search algorithms are available for low dimensional datasets, scaling to high-dimensional data is non-trivial. We show how to construct a space-efficient search index based on the latent space representation of a QVAE. Our experiments show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset.Further, we find real-world speedups compared to linear search and demonstrate memory-efficient scaling to half a billion data points.
Co-embedding attributed networks
Authors: Zaiqiao Meng, Shangsong Liang, Hongyan Bao, Xiangliang Zhang
Venue: Conference WSDM 2019
Year: 2019

Existing embedding methods for attributed networks aim at learning low-dimensional vector representations for nodes only but not for both nodes and attributes, resulting in the fact that they cannot capture the affinities between nodes and attributes. However, capturing such affinities is of great importance to the success of many real-world attributed network applications, such as attribute inference and user profiling. Accordingly, in this paper, we introduce a Co-embedding model for Attributed Networks (CAN), which learns low-dimensional representations of both attributes and nodes in the same semantic space such that the affinities between them can be effectively captured and measured. To obtain high-quality embeddings, we propose a variational auto-encoder that embeds each node and attribute with means and variances of Gaussian distributions. Experimental results on real-world networks demonstrate that our model yields excellent performance in a number of applications compared with state-of-the-art techniques.
A statistical theory for quantitative association rules
Authors: Yonatan Aumann, Yehuda Lindell
Venue: Conference KDD 1999
Year: 1999

rules are a key data-mining tool and as such have been well researched. So far, this research has focused predominantly on databases containing categorical data only. However, many real-world databases contain quantitative attributes and current solutions for this case are so far inadequate. We introduce a new definition of quantitative association rules based on statistical inference theory. Our definition reflects the intuition that the goal of association rules is to find extraordinary and therefore interesting phenomena in databases. We present rigorous experimental evaluation on real-world datasets, demonstrating the usefulness and characteristics of rules mined according to our definition.
Discovering the set of fundamental rule changes
Authors: Bing Liu, Wynne Hsu, Yiming Ma
Venue: Conference KDD 2001
Year: 2001

The world around us changes constantly. Knowing what has changed is an important part of our lives. For businesses, recognizing changes is also crucial. It allows businesses to adapt themselves to the changing market needs. In this paper, we study changes of association rules from one time period to another. One approach is to compare the supports and/or confidences of each rule in the two time periods and report the differences. This technique, however, is too simplistic as it tends to report a huge number of rule changes, and many of them are, in fact, simply the snowball effect of a small subset of fundamental changes. Here, we present a technique to highlight the small subset of fundamental changes. A change is fundamental if it cannot be explained by some other changes. The proposed technique has been applied to a number of real-life datasets. Experiments results show that the number of rules whose changes are unexplainable is quite small (about 20% of the total number of changes discovered), and many of these unexplainable changes reflect some fundamental shifts in the application domain.
Batc: a benchmark for aggregation techniques in crowdsourcing
Authors: Nguyen Quoc Viet Hung, Thanh Tam Nguyen, Ngoc Tran Lam, Karl Aberer
Venue: Conference SIGIR 2013
Year: 2013

As the volumes of AI problems involving human knowledge are likely to soar, crowdsourcing has become essential in a wide range of world-wide-web applications. One of the biggest challenges of crowdsourcing is aggregating the answers collected from crowd workers; and thus, many aggregate techniques have been proposed. However, given a new application, it is difficult for users to choose the best-suited technique as well as appropriate parameter values since each of these techniques has distinct performance characteristics depending on various factors (e.g. worker expertise, question difficulty). In this paper, we develop a benchmarking tool that allows to (i) simulate the crowd and (ii) evaluate aggregate techniques in different aspects (accuracy, sensitivity to spammers, etc.). We believe that this tool will be able to serve as a practical guideline for both researchers and software developers. While researchers can use our tool to assess existing or new techniques, developers can reuse its components to reduce the development complexity.
Simultaneous tensor subspace selection and clustering: the equivalence of high order svd and k-means clustering
Authors: Heng Huang, Chris H. Q. Ding, Dijun Luo, Tao Li
Venue: Conference KDD 2008
Year: 2008

Singular Value Decomposition (SVD)/Principal Component Analysis (PCA) have played a vital role in finding patterns from many datasets. Recently tensor factorization has been used for data mining and pattern recognition in high index/order data. High Order SVD (HOSVD) is a commonly used tensor factorization method and has recently been used in numerous applications like graphs, videos, social networks, etc. In this paper we prove that HOSVD does simultaneous subspace selection (data compression) and K-means clustering widely used for unsupervised learning tasks. We show how to utilize this new feature of HOSVD for clustering. We demonstrate these new results using three real and large datasets, two on face images datasets and one on hand-written digits dataset. Using this new HOSVD clustering feature we provide a dataset quality assessment on many frequently used experimental datasets with expected noise levels.
Subjectively interesting component analysis: Data projections that contrast with prior expectations
Authors: Bo Kang, Jefrey Lijffijt, Raúl Santos-Rodriguez, Tijl De Bie
Venue: Conference KDD 2016
Year: 2016

Methods that find insightful low-dimensional projections are essential to effectively explore high-dimensional data. Principal Component Analysis is used pervasively to find low-dimensional projections, not only because it is straightforward to use, but it is also often effective, because the variance in data is often dominated by relevant structure. However, even if the projections highlight real structure in the data, not all structure is interesting to every user. If a user is already aware of, or not interested in the dominant structure, Principal Component Analysis is less effective for finding interesting components. We introduce a new method called Subjectively Interesting Component Analysis (SICA), designed to find data projections that are subjectively interesting, i.e, projections that truly surprise the end-user. It is rooted in information theory and employs an explicit model of a user's prior expectations about the data. The corresponding optimization problem is a simple eigenvalue problem, and the result is a trade-off between explained variance and novelty. We present five case studies on synthetic data, images, time-series, and spatial data, to illustrate how SICA enables users to find (subjectively) interesting projections.
Forecasting ad-impressions on online retail websites using non-homogeneous hawkes processes
Authors: Krunal Parmar, Samuel Bushi, Sourangshu Bhattacharya, Surender Kumar
Venue: Conference CIKM 2017
Year: 2017

Promotional listing of products or advertisements is a major source of revenue for online retail companies. These advertisements are often sold in the guaranteed delivery market, serving of which critically depends on the ability to predict supply or potential impressions from a target segment of users. In this paper, we study the problem of predicting user visits or potential ad-impressions to online retail websites, based on historical time-stamps. We explore the time-series and temporal point process models. We find that a successful model must encompass three properties of the data: (1) temporally non-homgeneous rates, (2) self excitation and (3) handling special events. We propose a novel non-homogeneous Hawkes process based model for the same, and new algorithm for fitting this model without overfitting the self-excitation part. We validate the proposed model and algorithm using mulitple large scale ad-serving dataset from a top online retail company in India.