OpenRaiser/Intern-Atlas huggingface dataset:
Long document re-ranking with modular re-ranker
Authors: Luyu Gao, Jamie Callan
Venue: Conference SIGIR 2022
Year: 2022

Long document re-ranking has been a challenging problem for neural re-rankers based on deep language models like BERT. Early work breaks the documents into short passage-like chunks. These chunks are independently mapped to scalar scores or latent vectors, which are then pooled into a final relevance score. These encode-and-pool methods however inevitably introduce an information bottleneck: the low dimension representations. In this paper, we propose instead to model full query-to-document interaction, leveraging the attention operation and modular Transformer re-ranker framework. First, document chunks are encoded independently with an encoder module. An interaction module then encodes the query and performs joint attention from the query to all document chunk representations. We demonstrate that the model can use this new degree of freedom to aggregate important information from the entire document. Our experiments show that this design produces effective re-ranking on two classical IR collections Robust04 and ClueWeb09, and a large-scale supervised collection MS-MARCO document ranking.
Enhancing information diffusion prediction with self-supervised disentangled user and cascade representations
Authors: Zhangtao Cheng, Wenxue Ye, Leyuan Liu, Wenxin Tai, Fan Zhou
Venue: Conference CIKM 2023
Year: 2023

Accurately predicting information diffusion is critical for a vast range of applications. Existing methods generally consider user re-sharing behaviors to be driven by a single intent, and/or assume cascade temporal influence to be unchanged, which might not be consistent with real-world scenarios. To address these issues, we propose a self-supervised disentanglement framework (DisenIDP) for information diffusion prediction. First, we construct intent-aware hypergraphs to capture users' potential intents from different perspectives, and then perform the light hypergraph convolution to adaptively activate disentangled intents. Second, we extract long-term and short-term cascade influence via independent attention-based encoders. Finally, we set a self-supervised disentanglement task to alleviate the information loss and learn better-disentanglement representations. Extensive experiments conducted on two real-world social datasets demonstrate that DisenIDP outperforms state-of-the-art models across several settings.
Online additive quantization
Authors: Qi Liu, Jin Zhang, Defu Lian, Yong Ge, Jianhui Ma, Enhong Chen
Venue: Conference KDD 2021
Year: 2021

Approximate nearest neighbor search (ANNs) plays an important role in many applications ranging from information retrieval, recommender systems to machine translation. Several ANN indexes, such as hashing and quantization, have been designed to update for the evolving database, but there exists a remarkable performance gap between them and retrained indexes on the entire database. To close the gap, we propose an online additive quantization algorithm (online AQ) to dynamically update quantization codebooks with the incoming streaming data. Then we derive the regret bound to theoretically guarantee the performance of the online AQ algorithm. Moreover, to improve the learning efficiency, we develop a randomized block beam search algorithm for assigning each data to the codewords of the codebook. Finally, we extensively evaluate the proposed online AQ algorithm on four real-world datasets, showing that it remarkably outperforms the state-of-the-art baselines.
An Offline Metric for the Debiasedness of Click Models
Authors: Romain Deffayet, Philipp Hager, Jean-Michel Renders, Maarten de Rijke
Venue: Conference SIGIR 2023
Year: 2023

A well-known problem when learning from user clicks are inherent biases prevalent in the data, such as position or trust bias. Click models are a common method for extracting information from user clicks, such as document relevance in web search, or to estimate click biases for downstream applications such as counterfactual learning-to-rank, ad placement, or fair ranking. Recent work shows that the current evaluation practices in the community fail to guarantee that a well-performing click model generalizes well to downstream tasks in which the ranking distribution differs from the training distribution, i.e., under covariate shift. In this work, we propose an evaluation metric based on conditional independence testing to detect a lack of robustness to covariate shift in click models. We introduce the concept of debiasedness and a metric for measuring it. We prove that debiasedness is a necessary condition for recovering unbiased and consistent relevance scores and for the invariance of click prediction under covariate shift. In extensive semi-synthetic experiments, we show that our proposed metric helps to predict the downstream performance of click models under covariate shift and is useful in an off-policy model selection setting.
Position paper: ontology construction from online ontologies
Authors: Harith Alani
Venue: Conference WWW 2006
Year: 2006

One of the main hurdles towards a wide endorsement of ontologies is the high cost of constructing them. Reuse of existing ontologies offers a much cheaper alternative than building new ones from scratch, yet tools to support such reuse are still in their infancy. However, more ontologies are becoming available on the web, and online libraries for storing and indexing ontologies are increasing in number and demand. Search engines have also started to appear, to facilitate search and retrieval of online ontologies. This paper presents a fresh view on constructing ontologies automatically, by identifying, ranking, and merging fragments of online ontologies.
Optimal rare query suggestion with implicit user feedback
Authors: Yang Song 0008, Li-wei He
Venue: Conference WWW 2010
Year: 2010

Query suggestion has been an effective approach to help users narrow down to the information they need. However, most of existing studies focused on only popular/head queries. Since rare queries possess much less information (e.g., clicks) than popular queries in the query logs, it is much more difficult to efficiently suggest relevant queries to a rare query. In this paper, we propose an optimal rare query suggestion framework by leveraging implicit feedbacks from users in the query logs. Our model resembles the principle of pseudo-relevance feedback which assumes that top-returned results by search engines are relevant. However, we argue that the clicked URLs and skipped URLs contain different levels of information and thus should be treated differently. Hence, our framework optimally combines both the click and skip information from users and uses a random walk model to optimize the query correlation. Our model specifically optimizes two parameters: (1) the restarting (jumping) rate of random walk, and (2) the combination ratio of click and skip information. Unlike the Rocchio algorithm, our learning process does not involve the content of the URLs but simply leverages the click and skip counts in the query-URL bipartite graphs. Consequently, our model is capable of scaling up to the need of commercial search engines. Experimental results on one-month query logs from a large commercial search engine with over 40 million rare queries demonstrate the superiority of our framework, with statistical significance, over the traditional random walk models and pseudo-relevance feedback models.
Robust Recommendation with Adversarial Gaussian Data Augmentation
Authors: Zhenlei Wang, Xu Chen
Venue: Conference WWW 2023
Year: 2023

Recommender system holds the promise of accurately understanding and estimating the user preferences. However, due to the extremely sparse user-item interactions, the learned recommender models can be less robust and sensitive to the highly dynamic user preferences and easily changed recommendation environments. To alleviate this problem, in this paper, we propose a simple yet effective robust recommender framework by generating additional samples from the Gaussian distributions. In specific, we design two types of data augmentation strategies. For the first one, we directly produce the data based on the original samples, where we simulate the generation process in the latent space. For the second one, we firstly change the original samples towards the direction of maximizing the loss function, and then produce the data based on the altered samples to make more effective explorations. Based on both of the above strategies, we leverage adversarial training to optimize the recommender model with the generated data which can achieve the largest losses. In addition, we theoretically analyze our framework, and find that the above two data augmentation strategies equal to impose a gradient based regularization on the original recommender models. We conduct extensive experiments based on six real-world datasets to demonstrate the effectiveness of our framework.
Adaptive Multi- Modalities Fusion in Sequential Recommendation Systems
Authors: Hengchang Hu, Wei Guo, Yong Liu, Min-Yen Kan
Venue: Conference CIKM 2023
Year: 2023

In sequential recommendation, multi-modal information (e.g., text or image) can provide a more comprehensive view of an item's profile. The optimal stage (early or late) to fuse modality features into item representations is still debated. We propose a graph-based approach (named MMSR) to fuse modality features in an adaptive order, enabling each modality to prioritize either its inherent sequential nature or its interplay with other modalities. MMSR represents each user's history as a graph, where the modality features of each item in a user's history sequence are denoted by cross-linked nodes. The edges between homogeneous nodes represent intra-modality sequential relationships, and the ones between heterogeneous nodes represent inter-modality interdependence relationships. During graph propagation, MMSR incorporates dual attention, differentiating homogeneous and heterogeneous neighbors. To adaptively assign nodes with distinct fusion orders, MMSR allows each node's representation to be asynchronously updated through an update gate. In scenarios where modalities exhibit stronger sequential relationships, the update gate prioritizes updates among homogeneous nodes. Conversely, when the interdependent relationships between modalities are more pronounced, the update gate prioritizes updates among heterogeneous nodes. Consequently, MMSR establishes a fusion order that spans a spectrum from early to late modality fusion. In experiments across six datasets, MMSR consistently outperforms state-of-the-art models, and our graph propagation methods surpass other graph neural networks. Additionally, MMSR naturally manages missing modalities. The code is available at: https://github.com/HoldenHu/MMSR.
Exploiting Explicit and Implicit Item relationships for Session-based Recommendation
Authors: Zihao Li, Xianzhi Wang, Chao Yang, Lina Yao, Julian J. McAuley, Guandong Xu
Venue: Conference WSDM 2023
Year: 2023

The session-based recommendation aims to predict users' immediate next actions based on their short-term behaviors reflected by past and ongoing sessions. Graph neural networks (GNNs) recently dominated the related studies, yet their performance heavily relies on graph structures, which are often predefined, task-specific, and designed heuristically. Furthermore, existing graph-based methods either neglect implicit correlations among items or consider explicit and implicit relationships altogether in the same graphs. We propose to decouple explicit and implicit relationships among items. As such, we can capture the prior knowledge encapsulated in explicit dependencies and learned implicit correlations among items simultaneously in a flexible and more interpretable manner for effective recommendations. We design a dual graph neural network that leverages the feature representations extracted by two GNNs: a graph neural network with a single gate (SG-GNN) and an adaptive graph neural network (A-GNN). The former models explicit dependencies among items. The latter employs a self-learning strategy to capture implicit correlations among items. Our experiments on four real-world datasets show our model outperforms state-of-the-art methods by a large margin, achieving 18.46% and 70.72% improvement in HR@20, and 49.10% and 115.29% improvement in MRR@20 on Diginetica and LastFM datasets.
A constraint to automatically regulate document-length normalisation
Authors: Ronan Cummins, Colm O&apos, Riordan
Venue: Conference CIKM 2012
Year: 2012

Retrieval functions in information retrieval (IR) are fundamental to the effectiveness of search systems. However, considerable parameter tuning is often needed to increase the effectiveness of the retrieval. Document length normalisation is one such aspect that requires tuning on a per-query and per-collection basis for many retrieval functions.
Enhancing ad-hoc relevance weighting using probability density estimation
Authors: Xiaofeng Zhou, Jimmy Xiangji Huang, Ben He
Venue: Conference SIGIR 2011
Year: 2011

Classical probabilistic information retrieval (IR) models, e.g. BM25, deal with document length based on a trade-off between the Verbosity hypothesis, which assumes the independence of a document's relevance of its length, and the Scope hypothesis, which assumes the opposite. Despite the effectiveness of the classical probabilistic models, the potential relationship between document length and relevance is not fully explored to improve retrieval performance. In this paper, we conduct an in-depth study of this relationship based on the Scope hypothesis that document length does have its impact on relevance. We study a list of probability density functions and examine which of the density functions fits the best to the actual distribution of the document length. Based on the studied probability density functions, we propose a length-based BM25 relevance weighting model, called BM25L, which incorporates document length as a substantial weighting factor. Extensive experiments conducted on standard TREC collections show that our proposed BM25L markedly outperforms the original BM25 model, even if the latter is optimized.
Extracting and Ranking Travel Tips from User-Generated Reviews
Authors: Ido Guy, Avihai Mejer, Alexander Nus, Fiana Raiber
Venue: Conference WWW 2017
Year: 2017

User-generated reviews are a key driving force behind some of the leading websites, such as Amazon, TripAdvisor, and Yelp. Yet, the proliferation of user reviews in such sites also poses an information overload challenge: many items, especially popular ones, have a large number of reviews, which cannot all be read by the user. In this work, we propose to extract short practical tips from user reviews. We focus on tips for travel attractions extracted from user reviews on TripAdvisor. Our method infers a list of templates from a small gold set of tips and applies them to user reviews to extract tip candidates. For each attraction, the associated candidates are then ranked according to their predicted usefulness. Evaluation based on labeling by professional annotators shows that our method produces high-quality tips, with good coverage of cities and attractions.
Ranking factors of team success
Authors: Nataliia Pobiedina, Julia Neidhardt, María del Carmen Calatrava Moreno, Hannes Werthner
Venue: Conference WWW 2013
Year: 2013

As an increasing number of human activities are moving to the Web, more and more teams are predominantly virtual. Therefore, formation and success of virtual teams is an important issue in a wide range of fields. In this paper we model social behavior patterns of team work using data from virtual communities. In particular, we use data about the Web community of the multiplayer online game Dota 2 to study cooperation within teams. By applying statistical analysis we investigate how and to which extent different factors of the team in the game, such as role distribution, experience, number of friends and national diversity, have an influence on the team's success. In order to complete the picture we also rank the factors according to their influence. The results of our study imply that cooperation within the team is better than competition.
Mining (maximal) span-cores from temporal networks
Authors: Edoardo Galimberti, Alain Barrat, Francesco Bonchi, Ciro Cattuto, Francesco Gullo
Venue: Conference CIKM 2018
Year: 2018

When analyzing temporal networks, a fundamental task is the identification of dense structures (i.e., groups of vertices that exhibit a large number of links), together with their temporal span (i.e., the period of time for which the high density holds). We tackle this task by introducing a notion of temporal core decomposition where each core is associated with its span: we call such cores span-cores. As the total number of time intervals is quadratic in the size of the temporal domain $T$ under analysis, the total number of span-cores is quadratic in $|T|$ as well. Our first contribution is an algorithm that, by exploiting containment properties among span-cores, computes all the span-cores efficiently. Then, we focus on the problem of finding only the maximal span-cores, i.e., span-cores that are not dominated by any other span-core by both the coreness property and the span. We devise a very efficient algorithm that exploits theoretical findings on the maximality condition to directly compute the maximal ones without computing all span-cores. Experimentation on several real-world temporal networks confirms the efficiency and scalability of our methods. Applications on temporal networks, gathered by a proximity-sensing infrastructure recording face-to-face interactions in schools, highlight the relevance of the notion of (maximal) span-core in analyzing social dynamics and detecting/correcting anomalies in the data.
Efficient personalized pagerank with accuracy assurance
Authors: Yasuhiro Fujiwara, Makoto Nakatsuji, Takeshi Yamamuro, Hiroaki Shiokawa, Makoto Onizuka
Venue: Conference KDD 2012
Year: 2012

Personalize PageRank (PPR) is an effective relevance (proximity) measure in graph mining. The goal of this paper is to efficiently compute single node relevance and top-k/highly relevant nodes without iteratively computing the relevances of all nodes. Based on a "random surfer model", PPR iteratively computes the relevances of all nodes in a graph until convergence for a given user preference distribution. The problem with this iterative approach is that it cannot compute the relevance of just one or a few nodes. The heart of our solution is to compute single node relevance accurately in non-iterative manner based on sparse matrix representation, and to compute top-k/highly relevant nodes exactly by pruning unnecessary relevance computations based on upper/lower relevance estimations. Our experiments show that our approach is up to seven orders of magnitude faster than the existing alternatives.
A Distributed Graph Algorithm for Discovering Unique Behavioral Groups from Large-Scale Telco Data
Authors: Qirong Ho, Wenqing Lin, Eran Shaham, Shonali Krishnaswamy, The Anh Dang, Jingxuan Wang, Isabel Choo Zhongyan, Amy She-Nash
Venue: Conference CIKM 2016
Year: 2016

It is critical for a large telecommunications company such as Singtel to truly understand the behavior and preference of its customers, in order to win their loyalty in a highly fragmented and competitive market. In this paper we propose a novel graph edge-clustering algorithm (DGEC) that can discover unique behavioral groups, from rich usage data sets (such as CDRs and beyond). A behavioral group is a set of nodes that share similar edge properties reflecting customer behavior, but are not necessarily connected to each other and therefore different from the usual notion of graph communities. DGEC is an optimization-based model that uses the stochastic proximal gradient method, implemented as a distributed algorithm that scales to tens of millions of nodes and edges. The performance of DGEC is satisfactory for deployment, with an execution time of 2.4 hours over a graph of 5 million nodes and 27 million edges in a 8-machine environment (32 cores and 64GB memory per machine). We evaluate the behavioral groups discovered by DGEC by combining other information such as demographics and customer profiles, and demonstrate that these behavioral groups are objective, consistent and insightful. DGEC has now been deployed in production, and also shows promising potential to extract new usage behavioral features from other data sources such as web browsing, app usage and TV consumption.
PowerWalk: Scalable Personalized PageRank via Random Walks with Vertex-Centric Decomposition
Authors: Qin Liu, Zhenguo Li, John C. S. Lui, Jiefeng Cheng
Venue: Conference CIKM 2016
Year: 2016

Most methods for Personalized PageRank (PPR) precompute and store all accurate PPR vectors, and at query time, return the ones of interest directly. However, the storage and computation of all accurate PPR vectors can be prohibitive for large graphs, especially in caching them in memory for real-time online querying. In this paper, we propose a distributed framework that strikes a better balance between offline indexing and online querying. The offline indexing attains a fingerprint of the PPR vector of each vertex by performing billions of ``short'' random walks in parallel across a cluster of machines. We prove that our indexing method has an exponential convergence, achieving the same precision with previous methods using a much smaller number of random walks. At query time, the new PPR vector is composed by a linear combination of related fingerprints, in a highly efficient vertex-centric decomposition manner. Interestingly, the resulting PPR vector is much more accurate than its offline counterpart because it actually uses more random walks in its estimation. More importantly, we show that such decomposition for a batch of queries can be very efficiently processed using a shared decomposition. Our implementation, PowerWalk, takes advantage of advanced distributed graph engines and it outperforms the state-of-the-art algorithms by orders of magnitude. Particularly, it responses to tens of thousands of queries on graphs with billions of edges in just a few seconds.
Edge- Weighted Personalized PageRank: Breaking A Decade-Old Performance Barrier
Authors: Wenlei Xie, David Bindel, Alan J. Demers, Johannes Gehrke
Venue: Conference KDD 2015
Year: 2015

Personalized PageRank is a standard tool for finding vertices in a graph that are most relevant to a query or user. To personalize PageRank, one adjusts node weights or edge weights that determine teleport probabilities and transition probabilities in a random surfer model. There are many fast methods to approximate PageRank when the node weights are personalized; however, personalization based on edge weights has been an open problem since the dawn of personalized PageRank over a decade ago. In this paper, we describe the first fast algorithm for computing PageRank on general graphs when the edge weights are personalized. Our method, which is based on model reduction, outperforms existing methods by nearly five orders of magnitude. This huge performance gain over previous work allows us --- for the very first time --- to solve learning-to-rank problems for edge weight personalization at interactive speeds, a goal that had not previously been achievable for this class of problems.