OpenRaiser/Intern-Atlas huggingface dataset:
M5: Multi-Modal Multi-Interest Multi-Scenario Matching for Over-the-Top Recommendation
Authors: Pengyu Zhao, Xin Gao 0013, Chunxu Xu, Liang Chen
Venue: Conference KDD 2023
Year: 2023

Matching preferred shows to the subscribers is extremely important in the Over-the-Top (OTT) platforms. The existing methods did not adequately consider the characteristics of the OTT services, i.e., rich meta information, diverse user interests, and mixed recommendation scenarios, leading to sub-optimal performance. This paper introduces the Multi-Modal Multi-Interest Multi-Scenario Matching (M5) for the OTT recommendation to fully exploit these attributes. A multi-modal embedding layer is first introduced to transform the show IDs into both ID embeddings initialized randomly and content graph (CG) embeddings derived from the node representations pre-trained on a metagraph. To segregate the semantics between ID and CG embeddings, M5 exploits the mirrored two-tower modeling in the subsequent layers for efficiency and effectiveness. Specifically, a multi-interest extraction layer is proposed separately on ID and CG behaviors to model users' coarse-grained and fine-grained interests through behavioral categorization, subsidiary decoration, masked-language-modeling augmented self-attention modeling and subsidiary-intensity interest calibration. Facing the inherent diverse scenarios, M5 distinguishes the scenario differences at both feature and model levels, which crosses features with the scenario indicators and employs Split Mixture-of-Experts to generate the ID, and CG user embeddings. Finally, a weighted candidate matching layer is established to calculate the ID- and CG-oriented user-item preferences and then merge into a hybrid score with dynamic weighting. The extensive online and offline experiments over two real-world OTT platforms Hulu and Disney+ reveal that M5 significantly outperforms the previous state-of-the-art and online matching algorithms over various scenarios, indicating the effectiveness and robustness of the proposed method. M5 has been fully deployed on the main traffic of the most popular "For You'' sets of both platforms, continuously enhancing the user experience for hundreds of millions of subscribers every day and steadily increasing business revenue.
LiNR: Model Based Neural Retrieval on GPUs at LinkedIn
Authors: Fedor Borisyuk, Qingquan Song, Mingzhou Zhou, Ganesh Parameswaran, Madhu Arun, Siva Popuri, Tugrul Bingol, Zhuotao Pei, Kuang-Hsuan Lee, Lu Zheng, Qizhan Shao, Ali Naqvi, Sen Zhou, Aman Gupta
Venue: Conference CIKM 2024
Year: 2024

This paper introduces LiNR, LinkedIn's large-scale, GPU-based retrieval system. LiNR supports a billion-sized index on GPU models. We discuss our experiences and challenges in creating scalable, differentiable search indexes using TensorFlow and PyTorch at production scale. In LiNR, both items and model weights are integrated into the model binary. Viewing index construction as a form of model training, we describe scaling our system for large indexes, incorporating full scans and efficient filtering. A key focus is on enabling attribute-based pre-filtering for exhaustive GPU searches, addressing the common challenge of post-filtering in KNN searches that often reduces system quality. We further provide multi-embedding retrieval algorithms and strategies for tackling cold start issues in retrieval. Our advancements in supporting larger indexes through quantization are also discussed. We believe LiNR represents one of the industry's first Live-updated model-based retrieval indexes. Applied to out-of-network post recommendations on LinkedIn Feed, LiNR has contributed to a 3% relative increase in professional daily active users. We envisage LiNR as a step towards integrating retrieval and ranking into a single GPU model, simplifying complex infrastructures and enabling end-to-end optimization of the entire differentiable infrastructure through gradient descent.
Low-dimensional Alignment for Cross-Domain Recommendation
Authors: Tianxin Wang, Fuzhen Zhuang, Zhiqiang Zhang, Daixin Wang, Jun Zhou, Qing He
Venue: Conference CIKM 2021
Year: 2021

Cold start problem is one of the most challenging and long-standing problems in recommender systems, and cross-domain recommendation (CDR) methods are effective for tackling it. Most cold-start related CDR methods require training a mapping function between high-dimensional embedding space using overlapping user data. However, the overlapping data is scarce in many recommendation tasks, which makes it difficult to train the mapping function. In this paper, we propose a new approach for CDR, which aims to alleviate the training difficulty. The proposed method can be viewed as a special parameterization of the mapping function without hurting expressiveness, which makes use of non-overlapping user data and leads to effective optimization. Extensive experiments on two real-world CDR tasks are performed to evaluate the proposed method. In the case that there are few overlapping data, the proposed method outperforms the existed state-of-the-art method by 14% (relative improvement).
Physics-guided Active Sample Reweighting for Urban Flow Prediction
Authors: Wei Jiang, Tong Chen, Guanhua Ye, Wentao Zhang, Lizhen Cui, Zi Huang, Hongzhi Yin
Venue: Conference CIKM 2024
Year: 2024

Urban flow prediction is a spatio-temporal modelling task that estimates the throughput of transportation services like buses, taxis, and ride-sharing, where data-driven models have become the most popular solution in the past decade. Meanwhile, the implicitly learned mapping between historical observations to the prediction targets tend to over-simplify the dynamics of real-world urban flows, leading to suboptimal predictions. Some recent spatio-temporal prediction solutions bring remedies with the notion of physics-guided machine learning (PGML), which describes spatio-temporal data with nuanced and principled physics laws, thus enhancing both the prediction accuracy and interpretability. However, these spatio-temporal PGML methods are built upon a strong assumption that the observed data fully conforms to the differential equations that define the physical system, which can quickly become ill-posed in urban flow prediction tasks. The observed urban flow data, especially when sliced into time-dependent snapshots to facilitate predictions, is typically incomplete and sparse, and prone to inherent noise incurred in the collection process (e.g., uncalibrated traffic sensors). As a result, such physical inconsistency between the data and PGML model significantly limits the predictive power and robustness of the solution. Moreover, due to the interval-based predictions and intermittent nature of data filing (e.g., one record per 30 minutes) in many transportation services, the instantaneous dynamics of urban flows can hardly be captured, rendering differential equation-based continuous modelling a loose fit for this setting. To overcome the challenges, we develop a discretized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR) to enhance PN. Technically, P-GASR incorporates an active sample reweighting pipeline, which not only minimizes the model uncertainty of PN to enhance robustness, but also prioritizes data samples that exhibit higher physical compliance to reinforce their contribution to PN training. Experimental results in four real-world datasets demonstrate that our method achieves state-of-the-art performance with a demonstrable improvement in robustness. The code is released at https://github.com/WeiJiang01/P-GASR.
Trustworthy Recommender Systems: Foundations and Frontiers
Authors: Wenqi Fan, Xiangyu Zhao 0001, Lin Wang 0040, Xiao Chen 0016, Jingtong Gao, Qidong Liu 0002, Shijie Wang 0002
Venue: Conference KDD 2023
Year: 2023

Recommender systems aim to provide personalized suggestions to users, helping them make effective decisions. However, recent evidence has revealed the untrustworthy aspects of advanced recommender systems, leading to harmful effects in safety-critical areas like finance and healthcare. This tutorial will offer a comprehensive overview of achieving trustworthy recommender systems. It will cover six important aspects: Safety & Robustness, Non-discrimination & Fairness, Explainability, Privacy, Environmental Well-being, and Accountability & Auditability. Each aspect will be defined and categorized, followed by a discussion of the latest research progress and notable works. Additionally, potential interactions among these aspects and future research directions for trustworthy recommender systems will be explored.
Measuring fairness in ranked results: An analytical and empirical comparison
Authors: Amifa Raj, Michael D. Ekstrand
Venue: Conference SIGIR 2022
Year: 2022

Information access systems, such as search and recommender systems, often use ranked lists to present results believed to be relevant to the user's information need. Evaluating these lists for their fairness along with other traditional metrics provides a more complete understanding of an information access system's behavior beyond accuracy or utility constructs. To measure the (un)fairness of rankings, particularly with respect to the protected group(s) of producers or providers, several metrics have been proposed in the last several years. However, an empirical and comparative analyses of these metrics showing the applicability to specific scenario or real data, conceptual similarities, and differences is still lacking. We aim to bridge the gap between theoretical and practical ap-plication of these metrics. In this paper we describe several fair ranking metrics from the existing literature in a common notation, enabling direct comparison of their approaches and assumptions, and empirically compare them on the same experimental setup and data sets in the context of three information access tasks. We also provide a sensitivity analysis to assess the impact of the design choices and parameter settings that go in to these metrics and point to additional work needed to improve fairness measurement.
Mosaics of Scenes with Moving Objects
Authors: James Davis 0001
Venue: Conference CVPR 1998
Year: 1998

Image mosaics are useful for a variety of tasks in vision and computer graphics. A particularly convenient way to generate mosaics is by 'stitching' together many ordinary photographs. Existing algorithms focus on capturing static scenes. This paper presents a complete system for creating visually pleasing mosaics in the presence of moving objects. There are three primary contributions. The first component of our system is a registration method that remains unbiased by movement-the Mellin transform is extended to register images related by a projective transform. Second an efficient method for finding a globally consistent registration of all images is developed. By solving a linear system of equations, derived from many pairwise registration matrices, we find an optimal global registration. Lastly, a new method of compositing images is presented. Blurred areas due to moving objects are avoided by segmenting the mosaic into disjoint regions and sampling pixels in each region from a single source image.
Transductive object cutout
Authors: Jingyu Cui, Qiong Yang, Fang Wen 0001, Qiying Wu, Changshui Zhang, Luc Van Gool, Xiaoou Tang
Venue: Conference CVPR 2008
Year: 2008

In this paper, we address the issue of transducing the object cutout model from an example image to novel image instances. We observe that although object and background are very likely to contain similar colors in natural images, it is much less probable that they share similar color configurations. Motivated by this observation, we propose a local color pattern model to characterize the color configuration in a robust way. Additionally, we propose an edge profile model to modulate the contrast of the image, which enhances edges along object boundaries and attenuates edges inside object or background. The local color pattern model and edge model are integrated in a graph-cut framework. Higher accuracy and improved robustness of the proposed method are demonstrated through experimental comparison with state-of-the-art algorithms.
Improving web image search with contextual information
Authors: Xiaohui Xie, Jiaxin Mao, Yiqun Liu, Maarten de Rijke, Qingyao Ai, Yufei Huang, Min Zhang, Shaoping Ma
Venue: Conference CIKM 2019
Year: 2019

In web image search, items users search for are images instead of Web pages or online services. Web image search constitutes a very important part of web search. Re-ranking is a trusted technique to improve retrieval effectiveness in web search. Previous work on re-ranking web image search results mainly focuses on intra-query information (e.g., human interactions with the initial list of the current query). Contextual information such as the query sequence and implicit user feedback provided during a search session prior to the current query is known to improve the performance of general web search but has so far not been used in web image search. The differences in result placement and interaction mechanisms of image search make the search process rather different from general Web search engines. Because of these differences, context-aware re-ranking models that have originally been developed for general web search cannot simply be applied to web image search. We propose CARM, a context-aware re-ranking model, a neural network-based framework to re-rank web image search results for a query based on previous interaction behavior in the search session in which the query was submitted. Specifically, we explore a hybrid encoder with an attention mechanism to model intra-query and inter-query user preferences for image results in a two-stage structure. We train context-aware re-ranking model (CARM) to jointly learn query and image representations so as to be able to deal with the multimodal characteristics of web image search. Extensive experiments are carried out on a commercial web image search dataset. The results show that CARM outperforms state-of-the-art baseline models in terms of personalized evaluation metrics. Also, CARM combines the original ranking can improve the original ranking on personalized ranking and relevance estimation. We make the implementation of CARM and relevant datasets publicly available to facilitate future studies.
Concept evolution in deep learning training: A unified interpretation framework and discoveries
Authors: Haekyu Park, Seongmin Lee, Benjamin Hoover, Austin P. Wright, Omar Shaikh, Rahul Duggal, Nilaksh Das, Kevin Li, Judy Hoffman, Duen Horng Chau
Venue: Conference CIKM 2023
Year: 2023

We present ConceptEvo, a unified interpretation framework for deep neural networks (DNNs) that reveals the inception and evolution of learned concepts during training. Our work addresses a critical gap in DNN interpretation research, as existing methods primarily focus on post-training interpretation. ConceptEvo introduces two novel technical contributions: (1) an algorithm that generates a unified semantic space, enabling side-by-side comparison of different models during training, and (2) an algorithm that discovers and quantifies important concept evolutions for class predictions. Through a large-scale human evaluation and quantitative experiments, we demonstrate that ConceptEvo successfully identifies concept evolutions across different models, which are not only comprehensible to humans but also crucial for class predictions. ConceptEvo is applicable to both modern DNN architectures, such as ConvNeXt, and classic DNNs, such as VGGs and InceptionV3.
Improving the estimation of relevance models using large external corpora
Authors: Fernando Diaz, Donald Metzler
Venue: Conference SIGIR 2006
Year: 2006

Information retrieval algorithms leverage various collection statistics to improve performance. Because these statistics are often computed on a relatively small evaluation corpus, we believe using larger, non-evaluation corpora should improve performance. Specifically, we advocate incorporating external corpora based on language modeling. We refer to this process as external expansion. When compared to traditional pseudo-relevance feedback techniques, external expansion is more stable across topics and up to 10% more effective in terms of mean average precision. Our results show that using a high quality corpus that is comparable to the evaluation corpus can be as, if not more, effective than using the web. Our results also show that external expansion outperforms simulated relevance feedback. In addition, we propose a method for predicting the extent to which external expansion will improve retrieval performance. Our new measure demonstrates positive correlation with improvements in mean average precision.
Predicting Searcher Frustration
Authors: Henry Allen Feild, James Allan, Rosie Jones
Venue: Conference SIGIR 2010
Year: 2010

When search engine users have trouble finding information, they may become frustrated, possibly resulting in a bad experience (even if they are ultimately successful). In a user study in which participants were given difficult information seeking tasks, half of all queries submitted resulted in some degree of self-reported frustration. A third of all successful tasks involved at least one instance of frustration. By modeling searcher frustration, search engines can predict the current state of user frustration and decide when to intervene with alternative search strategies to prevent the user from becoming more frustrated, giving up, or switching to another search engine. We present several models to predict frustration using features extracted from query logs and physical sensors. We are able to predict frustration with a mean average precision of 65% from the physical sensors, and 87% from the query log features.
Characterizing and Predicting Voice Query Reformulation
Authors: Ahmed Hassan Awadallah, Ranjitha Gurunath Kulkarni, Umut Ozertem, Rosie Jones
Venue: Conference CIKM 2015
Year: 2015

Voice interactions are becoming more prevalent as the usage of voice search and intelligent assistants gains more popularity. Users frequently reformulate their requests in hope of getting better results either because the system was unable to recognize what they said or because it was able to recognize it but was unable to return the desired response. Query reformulation has been extensively studied in the context of text input. Many of the characteristics studied in the context of text query reformulation are potentially useful for voice query reformulation. However, voice query reformulation has its unique characteristics in terms of the reasons that lead users to reformulating their queries and how they reformulate them. In this paper, we study the problem of voice query reformulation. We perform a large scale human annotation study to collect thousands of labeled instances of voice reformulation and non-reformulation query pairs. We use this data to compare and contrast characteristics of reformulation and non-reformulation queries over a large a number of dimensions. We then train classifiers to distinguish between reformulation and non-reformulation query pairs and to predict the rationale behind reformulation. We demonstrate through experiments with the human labeled data that our classifiers achieve good performance in both tasks.
Evaluating Multi-query Sessions
Authors: Evangelos Kanoulas, Ben Carterette, Paul D. Clough, Mark Sanderson
Venue: Conference SIGIR 2011
Year: 2011

The standard system-based evaluation paradigm has focused on assessing the performance of retrieval systems in serving the best results for a single query. Real users, however, often begin an interaction with a search engine with a sufficiently under-specified query that they will need to reformulate before they find what they are looking for. In this work we consider the problem of evaluating retrieval systems over test collections of multi-query sessions. We propose two families of measures: a model-free family that makes no assumption about the user's behavior over a session, and a model-based family with a simple model of user interactions over the session. In both cases we generalize traditional evaluation metrics such as average precision to multi-query session evaluation. We demonstrate the behavior of the proposed metrics by using the new TREC 2010 Session track collection and simulations over the TREC-9 Query track collection.
Summary Attributes and Perceived Search Quality
Authors: Daniel E. Rose, David Orr, Raj Gopal Prasad Kantamneni
Venue: Conference WWW 2007
Year: 2007

We conducted a series of experiments in which surveyed web search users answered questions about the quality of search results on the basis of the result summaries. Summaries shown to different groups of users were editorially constructed so that they differed in only one attribute, such as length. Some attributes had no effect on users' quality judgments, while in other cases, changing an attribute had a "halo effect" which caused seemingly unrelated dimensions of result quality to be rated higher by users.
Using Shortlists to Support Decision Making and Improve Recommender System Performance
Authors: Tobias Schnabel, Paul N. Bennett, Susan T. Dumais, Thorsten Joachims
Venue: Conference WWW 2016
Year: 2016

In this paper, we study shortlists as an interface component for recommender systems with the dual goal of supporting the user's decision process, as well as improving implicit feedback elicitation for increased recommendation quality. A shortlist is a temporary list of candidates that the user is currently considering, e.g., a list of a few movies the user is currently considering for viewing. From a cognitive perspective, shortlists serve as digital short-term memory where users can off-load the items under consideration -- thereby decreasing their cognitive load. From a machine learning perspective, adding items to the shortlist generates a new implicit feedback signal as a by-product of exploration and decision making which can improve recommendation quality. Shortlisting therefore provides additional data for training recommendation systems without the increases in cognitive load that requesting explicit feedback would incur. We perform an user study with a movie recommendation setup to compare interfaces that offer shortlist support with those that do not. From the user studies we conclude: (i) users make better decisions with a shortlist; (ii) users prefer an interface with shortlist support; and (iii) the additional implicit feedback from sessions with a shortlist improves the quality of recommendations by nearly a factor of two.
Information Retrieval as Card Playing: A Formal Model for Optimizing Interactive Retrieval Interface
Authors: Yinan Zhang, Chengxiang Zhai
Venue: Conference SIGIR 2015
Year: 2015

We propose a novel formal model for optimizing interactive information retrieval interfaces. To model interactive retrieval in a general way, we frame the task of an interactive retrieval system as to choose a sequence of interface cards to present to the user. At each interaction lap, the system's goal is to choose an interface card that can maximize the expected gain of relevant information for the user while minimizing the effort of the user with consideration of the user's action model and any desired constraints on the interface card. We show that such a formal interface card model can not only cover the Probability Ranking Principle for Interactive Information Retrieval as a special case by making multiple simplification assumptions, but also be used to derive a novel formal interface model for adaptively optimizing navigational interfaces in a retrieval system. Experimental results show that the proposed model is effective in automatically generating adaptive navigational interfaces, which outperform the baseline pre-designed static interfaces.