OpenRaiser/Intern-Atlas huggingface dataset:
Amazon-kg: A knowledge graph enhanced cross-domain recommendation dataset
Authors: Yuhan Wang, Qing Xie, Mengzi Tang, Lin Li, Jingling Yuan, Yongjian Liu
Venue: Conference SIGIR 2024
Year: 2024

Cross-domain recommendation (CDR) aims to utilize the information from relevant domains to guide the recommendation task in the target domain, and shows great potential in alleviating the data sparsity and cold-start problems of recommender systems. Most existing methods utilize the interaction information (e.g., ratings and clicks) or consider auxiliary information (e.g., tags and comments) to analyze the users' cross-domain preferences, but such kinds of information ignore the intrinsic semantic relationship of different domains. In order to effectively explore the inter-domain correlations, encyclopedic knowledge graphs (KG) involving different domains are highly desired in cross-domain recommendation tasks because they contain general information covering various domains with structured data format. However, there are few datasets containing KG information for CDR tasks, so in order to enrich the available data resource, we build a KG-enhanced cross-domain recommendation dataset, named Amazon-KG, based on the widely used Amazon dataset for CDR and the well-known KG DBpedia. In this work, we analyze the potential of KG applying in cross-domain recommendations, and describe the construction process of our dataset in detail. Finally, we perform quantitative statistical analysis on the dataset. We believe that datasets like Amazon-KG contribute to the development of knowledge-aware cross-domain recommender systems. Our dataset has been released at https://github.com/WangYuhan-0520/Amazon-KG-v2.0-dataset.
Llmgr: Large language model-based generative retrieval in alipay search
Authors: Wei Chen, Yixin Ji, Zeyuan Chen, Jia Xu, Zhongyi Liu
Venue: Conference SIGIR 2024
Year: 2024

The search system aims to help users quickly find items according to queries they enter, which includes the retrieval and ranking modules. Traditional retrieval is a multi-stage process, including indexing and sorting, which cannot be optimized end-to-end. With the real data about mini-apps in the Alipay search, we find that many complex queries fail to display the relevant mini-apps, seriously threatening users' search experience. To address the challenges, we propose a Large Language Model-based Generative Retrieval (LLMGR) approach for retrieving mini-app candidates. The information of the mini-apps is encoded into the large model, and the title of the mini-app is directly generated. Through the online A/B test in Alipay search, LLMGR as a supplementary source has statistically significant improvements in the Click-Through Rate (CTR) of the search system compared to traditional methods. In this paper, we have deployed a novel retrieval method for the Alipay search system and demonstrated that generative retrieval methods based on LLM can improve the performance of search system, particularly for complex queries, which have an average increase of 0.2% in CTR.
A mixed-curvature graph diffusion model
Authors: Yujie Wang, Shuo Zhang, Junda Ye, Hao Peng, Li Sun
Venue: Conference CIKM 2024
Year: 2024

Graph generation plays a vital role in a wide range of applications such as traffic analysis, drug discovery and more, for its rapid and efficient generation speed coupled with its precise and stable generation capabilities. And the diffusion model, which is the dominant solution in image generation domain, has shown its potential in graph generation recently. In the literature, existing graph diffusion models trivialize the graph structure, and prioritize the Euclidean space for graph generation, ignoring the intrinsic difference between non-Euclidean graph structures and Euclidean grid-like image/text data. The few graph diffusion models in hyperbolic space separate the embedding and diffusion process, and loose the geometric constraints in the diffusion process. The problem of generating graph structure in a generic Riemannian space largely remains open. It faces several fundamental challenges. On the one hand, navigating and structuring graphs within Riemannian spaces poses greater difficulty. In other words, how to preserve adherence to the constraints of Riemannian geometry has not been touched in the literature. On the other hand, Riemannian operators for graph diffusion models are not available so far, which is inherently different from that in Euclidean space. In light of the aforementioned issues, we restore the notion of product space, and propose a generic graph generation method, called mixed-curvature Product Space Graph Diffusion Model (ProGDM). Specifically, ProGDM includes a Riemannian embedding module based on contrastive learning and a geometric diffusion models across multiple Riemannian sub-spaces. We evaluate the proposed ProGDM with extensive experiments on benchmark datasets, and the empirical results show that ProGDM has achieved superior performance to the state-of-the-art methods.
War of words: The competitive dynamics of legislative processes
Authors: Victor Kristof, Matthias Grossglauser, Patrick Thiran
Venue: Conference WWW 2020
Year: 2020

A body of law is an example of a dynamic corpus of text documents that are jointly maintained by a group of editors who compete and collaborate in complex constellations. Our goal is to develop predictive models for this process, thereby shedding light on the competitive dynamics of parliamentarians who make laws. For this purpose, we curated a dataset of 450000 legislative edits introduced by European parliamentarians over the last ten years. An edit modifies the status quo of a law, and could be in competition with another edit if it modifies the same part of that law. We propose a model for predicting the success of such edits, in the face of both the inertia of the status quo and the competition between overlapping edits. The parameters of this model can be interpreted in terms of the influence of parliamentarians and of the controversy of laws.
Casting a web of trust over wikipedia: an interaction-based approach
Authors: Silviu Maniu, Talel Abdessalem, Bogdan Cautis
Venue: Conference WWW 2011
Year: 2011

We report in this short paper results on inferring a signed network (a "web of trust") from user interactions. On the Wikipedia network of contributors, from a collection of articles in the politics domain and their revision history, we investigate mechanisms by which relationships between contributors - in the form of signed directed links - can be inferred from their interactions. Our preliminary study provides valuable insight into principles underlying a signed network of Wikipedia contributors that is captured by social interaction. We look into whether this network (called hereafter WikiSigned) represents indeed a plausible configuration of link signs. We assess connections to social theories such as structural balance and status, which have already been considered in online communities. We also evaluate on this network the accuracy of a learned predictor for edge signs. Equipped with learning techniques that have been applied before on three explicit signed networks, we obtain good accuracy over the WikiSigned edges. Moreover, by cross training-testing we obtain strong evidence that our network does reveal an implicit signed configuration and that it has similar characteristics to the explicit ones, even though it is inferred from interactions. We also report on an application of the resulting signed network that impacts Wikipedia readers, namely the classification of Wikipedia articles by importance and quality.
Order-agnostic identifier for large language model-based generative recommendation
Authors: Xinyu Lin, Haihan Shi, Wenjie Wang, Fuli Feng, Qifan Wang, See-Kiong Ng, Tat-Seng Chua
Venue: Conference SIGIR 2025
Year: 2025

Leveraging Large Language Models (LLMs) for generative recommendation has attracted significant research interest, where item tokenization is a critical step. It involves assigning item identifiers for LLMs to encode user history and generate the next item. Existing approaches leverage either token-sequence identifiers, representing items as discrete token sequences, or single-token identifiers, using ID or semantic embeddings. Token-sequence identifiers face issues such as the local optima problem in beam search and low generation efficiency due to step-by-step generation. In contrast, single-token identifiers fail to capture rich semantics or encode Collaborative Filtering (CF) information, resulting in suboptimal performance. To address these issues, we propose two fundamental principles for item identifier design: 1) integrating both CF and semantic information to fully capture multi-dimensional item information, and 2) designing order-agnostic identifiers without token dependency, mitigating the local optima issue and achieving simultaneous generation for generation efficiency. Accordingly, we introduce a novel set identifier paradigm for LLM-based generative recommendation, representing each item as a set of order-agnostic tokens. To implement this paradigm, we propose SETRec, which leverages CF and semantic tokenizers to obtain order-agnostic multi-dimensional tokens. To eliminate token dependency, SETRec uses a sparse attention mask for user history encoding and a query-guided generation mechanism for simultaneous token generation. We instantiate SETRec on T5 and Qwen (from 1.5B to 7B). Extensive experiments on four datasets demonstrate its effectiveness across various scenarios (e.g., full ranking, warm- and cold-start ranking, and various item popularity groups). Moreover, results validate SETRec's superior efficiency and show promising scalability on cold-start items as model sizes increase.
On the scalability of temporal relative positional encoding for dynamic link prediction
Authors: Ke Cheng, Linzhi Peng, Pengyang Wang, Heng Chang, Junchen Ye, Bowen Du
Venue: Conference KDD 2025
Year: 2025

The combination of temporal graph neural networks (TGNNs) and relative positional features is effective for dynamic link prediction tasks because it improves the modeling of relationships between nodes. We summarize the positional feature as Temporal Relative Positional Encoding (TRPE), which incorporates local subgraph computation and temporal decay effects compared to RPE in static graph learning. However, existing dynamic graph learning methods with TRPE are limited by high computational costs and poor scalability due to information loss in neighborhood compression. To address this, we introduce a scalable framework that enhances TRPE computation by integrating it with temporal clusters. Our method replaces high-order relative position information with cluster-based computation to reduce computation costs. Each node is assigned to a cluster based on its structural role, rather than just randomly sketched, reducing information loss during neighborhood compression. TGNNs with our proposed framework are more expressive than existing dynamic graph learning methods and offer greater scalability than existing TRPE models. Our experimental results on seven standard temporal link prediction benchmarks demonstrate that our proposed model achieves comparable or superior performance compared to state-of-the-art models.
iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservation
Authors: Siwei Zhang, Yun Xiong, Yao Zhang, Xixi Wu, Yiheng Sun, Jiawei Zhang
Venue: Conference CIKM 2023
Year: 2023

Continuous-time dynamic graph modeling is a crucial task for many real-world applications, such as financial risk management and fraud detection. Though existing dynamic graph modeling methods have achieved satisfactory results, they still suffer from three key limitations, hindering their scalability and further applicability. i) Indiscriminate updating. For incoming edges, existing methods would indiscriminately deal with them, which may lead to more time consumption and unexpected noisy information. ii) Ineffective node-wise long-term modeling. They heavily rely on recurrent neural networks (RNNs) as a backbone, which has been demonstrated to be incapable of fully capturing node-wise long-term dependencies in event sequences. iii) Neglect of re-occurrence patterns. Dynamic graphs involve the repeated occurrence of neighbors that indicates their importance, which is disappointedly neglected by existing methods. In this paper, we present iLoRE, a novel dynamic graph modeling method with instant node-wise Long-term modeling and Re-occurrence preservation. To overcome the indiscriminate updating issue, we introduce the Adaptive Short-term Updater module that will automatically discard the useless or noisy edges, ensuring iLoRE's effectiveness and instant ability. We further propose the Long-term Updater to realize more effective node-wise long-term modeling, where we innovatively propose the Identity Attention mechanism to empower a Transformer-based updater, bypassing the limited effectiveness of typical RNN-dominated designs. Finally, the crucial re-occurrence patterns are also encoded into a graph module for informative representation learning, which will further improve the expressiveness of our method. Our experimental results on real-world datasets demonstrate the effectiveness of our iLoRE for dynamic graph modeling.
Aligning images in the wild
Authors: Wen-Yan Lin, Linlin Liu, Yasuyuki Matsushita, Kok-Lim Low, Siying Liu
Venue: Conference CVPR 2012
Year: 2012

Aligning image pairs with significant appearance change is a long standing computer vision challenge. Much of this problem stems from the local patch descriptors' instability to appearance variation. In this paper we suggest this instability is due less to descriptor corruption and more the difficulty in utilizing local information to canonically define the orientation (scale and rotation) at which a patch's descriptor should be computed. We address this issue by jointly estimating correspondence and relative patch orientation, within a hierarchical algorithm that utilizes a smoothly varying parameterization of geometric transformations. By collectively estimating the correspondence and orientation of all the features, we can align and orient features that cannot be stably matched with only local information. At the price of smoothing over motion discontinuities (due to independent motion or parallax), this approach can align image pairs that display significant inter-image appearance variations.
Context embeddings for efficient answer generation in retrieval-augmented generation
Authors: David Rau, Shuai Wang, Hervé Déjean, Stéphane Clinchant, Jaap Kamps
Venue: Conference WSDM 2025
Year: 2025

Retrieval-Augmented Generation (RAG) allows overcoming the limited knowledge of LLMs by extending the input with external information. As a consequence, the contextual inputs to the model become much longer slowing down decoding time affecting the time a user has to wait for an answer. We address this challenge by presenting COCOM, an effective context compression method, reducing long contexts to only a handful of Context Embeddings, speeding up the generation time by a large margin. Our method allows for different compression rates, trading off decoding time for answer quality. Compared to earlier methods, COCOM allows for handling multiple contexts more effectively, significantly reducing decoding time for long inputs. Our method demonstrates an inference speed-up of up to 5.69 times while achieving higher performance compared to existing efficient context compression methods
Simplified similarity scoring using term ranks
Authors: Vo Ngoc Anh, Alistair Moffat
Venue: Conference SIGIR 2005
Year: 2005

We propose a method for document ranking that combines a simple document-centric view of text, and fast evaluation strategies that have been developed in connection with the vector space model. The new method defines the importance of a term within a document qualitatively rather than quantitatively, and in doing so reduces the need for tuning parameters. In addition, the method supports very fast query processing, with most of the computation carried out on small integers, and dynamic pruning an effective option. Experiments on a wide range of TREC data show that the new method provides retrieval effectiveness as good as or better than the Okapi BM25 formulation, and variants of language models.
Online Result Cache Invalidation for Real-Time Web Search
Authors: Xiao Bai, Flavio Paiva Junqueira
Venue: Conference SIGIR 2012
Year: 2012

Caches of results are critical components of modern Web search engines, since they enable lower response time to frequent queries and reduce the load to the search engine backend. Results in long-lived cache entries may become stale, however, as search engines continuously update their index to incorporate changes to the Web. Consequently, it is important to provide mechanisms that control the degree of staleness of cached results, ideally enabling the search engine to always return fresh results. In this paper, we present a new mechanism that identifies and invalidates query results that have become stale in the cache online. The basic idea is to evaluate at query time and against recent changes if cache hits have had their results have changed. For enhancing invalidation efficiency, the generation time of cached queries and their chronological order with respect to the latest index update are used to early prune unaffected queries. We evaluate the proposed approach using documents that change over time and query logs of the Yahoo! search engine. We show that the proposed approach ensures good query results (50% fewer stale results) and high invalidation accuracy (90% fewer unnecessary invalidations) compared to a baseline approach that makes invalidation decisions off-line. More importantly, the proposed approach induces less processing overhead, ensuring an average throughput 73% higher than that of the baseline approach.
Search Engines and their Public Interfaces: Which APIs are the most synchronized?
Authors: Frank McCown, Michael L. Nelson
Venue: Conference WWW 2007
Year: 2007

Researchers of commercial search engines often collect datausing the application programming interface (API) or by"scraping" results from the web user interface (WUI), butanecdotal evidence suggests the interfaces produce differentresults. We provide the first in-depth quantitative analysisof the results produced by the Google, MSN and Yahoo APIand WUI interfaces. After submitting a variety of queriesto the interfaces for 5 months, we found significant discrepanciesin several categories. Our findings suggest that theAPI indexes are not older, but they are probably smaller for Google and Yahoo. Researchers may use our findings tobetter understand the differences between the interfaces andchoose the best API for their particular types of queries.
Linked Document Embedding for Classification
Authors: Suhang Wang, Jiliang Tang, Charu C. Aggarwal, Huan Liu
Venue: Conference CIKM 2016
Year: 2016

Word and document embedding algorithms such as Skip-gram and Paragraph Vector have been proven to help various text analysis tasks such as document classification, document clustering and information retrieval. The vast majority of these algorithms are designed to work with independent and identically distributed documents. However, in many real-world applications, documents are inherently linked. For example, web documents such as blogs and online news often have hyperlinks to other web documents, and scientific articles usually cite other articles. Linked documents present new challenges to traditional document embedding algorithms. In addition, most existing document embedding algorithms are unsupervised and their learned representations may not be optimal for classification when labeling information is available. In this paper, we study the problem of linked document embedding for classification and propose a linked document embedding framework LDE, which combines link and label information with content information to learn document representations for classification. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of link and label information in the proposed framework LDE.
Analyzing mathematical content to detect academic plagiarism
Authors: Norman Meuschke, Moritz Schubotz, Felix Hamborg, Tomás Skopal, Bela Gipp
Venue: Conference CIKM 2017
Year: 2017

This paper presents, to our knowledge, the first study on analyzing mathematical expressions to detect academic plagiarism. We make the following contributions. First, we investigate confirmed cases of plagiarism to categorize the similarities of mathematical content commonly found in plagiarized publications. From this investigation, we derive possible feature selection and feature comparison strategies for developing math-based detection approaches and a ground truth for our experiments. Second, we create a test collection by embedding confirmed cases of plagiarism into the NTCIR-11 MathIR Task dataset, which contains approx. 60 million mathematical expressions in 105,120 documents from arXiv.org. Third, we develop a first math-based detection approach by implementing and evaluating different feature comparison approaches using an open source parallel data processing pipeline built using the Apache Flink framework. The best performing approach identifies all but two of our real-world test cases at the top rank and achieves a mean reciprocal rank of 0.86. The results show that mathematical expressions are promising text-independent features to identify academic plagiarism in large collections. To facilitate future research on math-based plagiarism detection, we make our source code and data available.
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks
Authors: Yu-Ru Lin, Yun Chi, Shenghuo Zhu, Hari Sundaram, Belle L. Tseng
Venue: Conference WWW 2008
Year: 2008

We discover communities from social network data, and analyze the community evolution. These communities are inherent characteristics of human interaction in online social networks, as well as paper citation networks. Also, communities may evolve over time, due to changes to individuals' roles and social status in the network as well as changes to individuals' research interests. We present an innovative algorithm that deviates from the traditional two-step approach to analyze community evolutions. In the traditional approach, communities are first detected for each time slice, and then compared to determine correspondences. We argue that this approach is inappropriate in applications with noisy data. In this paper, we propose FacetNet for analyzing communities and their evolutions through a robust unified process. In this novel framework, communities not only generate evolutions, they also are regularized by the temporal smoothness of evolutions. As a result, this framework will discover communities that jointly maximize the fit to the observed data and the temporal evolution. Our approach relies on formulating the problem in terms of non-negative matrix factorization, where communities and their evolutions are factorized in a unified way. Then we develop an iterative algorithm, with proven low time complexity, which is guaranteed to converge to an optimal solution. We perform extensive experimental studies, on both synthetic datasets and real datasets, to demonstrate that our method discovers meaningful communities and provides additional insights not directly obtainable from traditional methods.