OpenRaiser/Intern-Atlas huggingface dataset:
An Information-Theoretic Account of Static Index Pruning
Authors: Ruey-Cheng Chen, Chia-Jung Lee
Venue: Conference SIGIR 2013
Year: 2013

In this paper, we recast static index pruning as a model induction problem under the framework of Kullback's principle of minimum cross-entropy. We show that static index pruning has an approximate analytical solution in the form of convex integer program. Further analysis on computation feasibility suggests that one of its surrogate model can be solved efficiently. This result has led to the rediscovery of \emph{uniform pruning}, a simple yet powerful pruning method proposed in 2001 and later easily ignored by many of us. To empirically verify this result, we conducted experiments under a new design in which prune ratio is strictly controlled. Our result on standard ad-hoc retrieval benchmarks has confirmed that uniform pruning is robust to high prune ratio and its performance is currently state of the art.
Interactive Audience Expansion On Large Scale Online Visitor Data
Authors: Gromit Yeuk-Yin Chan, Tung Mai, Anup B. Rao, Ryan A. Rossi, Fan Du, Cláudio T. Silva, Juliana Freire
Venue: Conference KDD 2021
Year: 2021

Online marketing platforms often store millions of website visitors' behavior as a large sparse matrix with rows as visitors and columns as behavior. These platforms allow marketers to conduct Audience Expansion, a technique to identify new audiences with similar behavior to the original target audiences. In this paper, we propose a method to achieve interactive Audience Expansion from millions of visitor data efficiently. Unlike other methods that undergo significant computations upon inputs, our approach provides interactive responses when a marketer inputs the target audiences and similarity measures. The idea is to apply data summarization technique on the large visitor matrix to obtain a small set of summaries representing the similarities in the matrix. We propose efficient algorithms to compute the data summaries on a distributed computing environment (i.e., Spark) and conduct the expansion using the summaries. Our experiment shows that our approach (1) provides 10 times more accurate and 27 times faster Audience Expansion results on real datasets and (2) achieves a 98% speed-up compared to straightforward data summarization implementations. We also present an interface to apply the algorithm for real-world scenarios.
Fashion compatibility modeling through a multi-modal try-on-guided scheme
Authors: Xue Dong, Jianlong Wu, Xuemeng Song, Hongjun Dai, Liqiang Nie
Venue: Conference SIGIR 2020
Year: 2020

Recent years have witnessed a growing trend of fashion compatibility modeling, which scores the matching degree of the given outfit and then provides people with some dressing advice. Existing methods have primarily solved this problem by analyzing the discrete interaction among multiple complementary items. However, the fashion items would present certain occlusion and deformation when they are worn on the body. Therefore, the discrete item interaction cannot capture the fashion compatibility in a combined manner due to the neglect of a crucial factor: the overall try-on appearance. In light of this, we propose a multi-modal try-on-guided compatibility modeling scheme to jointly characterize the discrete interaction and try-on appearance of the outfit. In particular, we first propose a multi-modal try-on template generator to automatically generate a try-on template from the visual and textual information of the outfit, depicting the overall look of its composing fashion items. Then, we introduce a new compatibility modeling scheme which integrates the outfit try-on appearance into the traditional discrete item interaction modeling. To fulfill the proposal, we construct a large-scale real-world dataset from SSENSE, named FOTOS, consisting of 11,000 well-matched outfits and their corresponding realistic try-on images. Extensive experiments have demonstrated its superiority to state-of-the-arts.
Content Recommendation by Noise Contrastive Transfer Learning of Feature Representation
Authors: Yiyang Li, Guanyu Tao, Weinan Zhang, Yong Yu, Jun Wang
Venue: Conference CIKM 2017
Year: 2017

Personalized recommendation has been proved effective as a content discovery tool for many online news publishers. As fresh news articles are frequently coming to the system while the old ones are fading away quickly, building a consistent and coherent feature representation over the ever-changing articles pool is fundamental to the performance of the recommendation. However, learning a good feature representation is challenging, especially for some small publishers that have normally fewer than 10,000 articles each year. In this paper, we consider to transfer knowledge from a larger text corpus. In our proposed solution, an effective article recommendation engine can be established with a small number of target publisher articles by transferring knowledge from a large corpus of text with a different distribution. Specifically, we leverage noise contrastive estimation techniques to learn the word conditional distribution given the context words, where the noise conditional distribution is pre-trained from the large corpus. Our solution has been deployed in a commercial recommendation service. The large-scale online A/B testing on two commercial publishers demonstrates up to 9.97% relative overall performance gain of our proposed model on the recommendation click-though rate metric over the non-transfer learning baselines.
CODEC: Complex Document and Entity Collection
Authors: Iain Mackie, Paul Owoicho, Carlos Gemmell, Sophie Fischer, Sean MacAvaney, Jeffrey Dalton
Venue: Conference SIGIR 2022
Year: 2022

CODEC is a document and entity ranking benchmark that focuses on complex research topics. We target essay-style information needs of social science researchers, i.e. "How has the UK's Open Banking Regulation benefited Challenger Banks?". CODEC includes 42 topics developed by researchers and a new focused web corpus with semantic annotations including entity links. This resource includes expert judgments on 17,509 documents and entities (416.9 per topic) from diverse automatic and interactive manual runs. The manual runs include 387 query reformulations, providing data for query performance prediction and automatic rewriting evaluation. CODEC includes analysis of state-of-the-art systems, including dense retrieval and neural re-ranking. The results show the topics are challenging with headroom for document and entity ranking improvement. Query expansion with entity information shows significant gains in document ranking, demonstrating the resource's value for evaluating and improving entity-oriented search. We also show that the manual query reformulations significantly improve document ranking and entity ranking performance. Overall, CODEC provides challenging research topics to support the development and evaluation of entity-centric search methods.
Intent-Driven Similarity in E-Commerce Listings
Authors: Gilad Fuchs, Yoni Acriche, Idan Hasson, Pavel Petrov
Venue: Conference CIKM 2020
Year: 2020

Discovering similarities between online listings is a common backend task being used across different downstream experiences in eBay. Our baseline unstructured listing similarity method relies on measuring the semantic textual similarity between the embedding vectors of listing titles. However, we discovered that even with the latest contextualized embedding methods, our similarity fails to give the proper weight to the key tokens in the title that matter. This often results in identifying listing similarities that are not sufficient, which later hurts the downstream experiences. In this paper we present a method we call "Listing2Query", or "L2Q", which uses a Sequence Labeling approach to learn token importance from our users? search queries and on-site behaviour. We used pairs of listing titles and their matching search queries, and leveraged a contextualized character language model, to train L2Q as a bidirectional recurrent neural network to produce token importance weights. We demonstrate that plugging these weights into relatively straightforward listing similarity methods is a simple way to significantly improve the similarity results, even to the extent that it consistently outperforms those created by popular representations such as BERT. Notably, this approach is not reserved to only large online marketplaces but can be generalized to other cases that include a search-driven experience and a recall set of short documents.
SigniTrend: scalable detection of emerging topics in textual streams by hashed significance thresholds
Authors: Erich Schubert, Michael Weiler, Hans-Peter Kriegel
Venue: Conference KDD 2014
Year: 2014

Social media such as Twitter or weblogs are a popular source for live textual data. Much of this popularity is due to the fast rate at which this data arrives, and there are a number of global events - such as the Arab Spring - where Twitter is reported to have had a major influence. However, existing methods for emerging topic detection are often only able to detect events of a global magnitude such as natural disasters or celebrity deaths, and can monitor user-selected keywords or operate on a curated set of hashtags only. Interesting emerging topics may, however, be of much smaller magnitude and may involve the combination of two or more words that themselves are not unusually hot at that time. Our contributions to the detection of emerging trends are three-fold first of all, we propose a significance measure that can be used to detect emerging topics early, long before they become "hot tags", by drawing upon experience from outlier detection. Secondly, by using hash tables in a heavy-hitters type algorithm for establishing a noise baseline, we show how to track even all keyword pairs using only a fixed amount of memory. Finally, we aggregate the detected co-trends into larger topics using clustering approaches, as often as a single event will cause multiple word combinations to trend at the same time.
Influence and correlation in social networks
Authors: Aris Anagnostopoulos, Ravi Kumar, Mohammad Mahdian
Venue: Conference KDD 2008
Year: 2008

In many online social systems, social ties between users play an important role in dictating their behavior. One of the ways this can happen is through social influence, the phenomenon that the actions of a user can induce his/her friends to behave in a similar way. In systems where social influence exists, ideas, modes of behavior, or new technologies can diffuse through the network like an epidemic. Therefore, identifying and understanding social influence is of tremendous interest from both analysis and design points of view.
Trust relationship prediction using online product review data
Authors: Nan Ma, Ee-Peng Lim, Viet-An Nguyen, Aixin Sun, Haifeng Liu
Venue: Conference CIKM 2009
Year: 2009

Trust between users is an important piece of knowledge that can be exploited in search and recommendation.Given that user-supplied trust relationships are usually very sparse, we study the prediction of trust relationships using user interaction features in an online user generated review application context. We show that trust relationship prediction can achieve better accuracy when one adopts personalized and cluster-based classification methods. The former trains one classifier for each user using user-specific training data. The cluster-based method first constructs user clusters before training one classifier for each user cluster. Our proposed methods have been evaluated in a series of experiments using two datasets from Epinions.com. It is shown that the personalized and cluster-based classification methods outperform the global classification method, particularly for the active users.
Text retrieval priors for Bayesian logistic regression
Authors: Eugene Yang, David D. Lewis, Ophir Frieder
Venue: Conference SIGIR 2019
Year: 2019

Discriminative learning algorithms such as logistic regression excel when training data are plentiful, but falter when it is meager. An extreme case is text retrieval (zero training data), where discriminative learning is impossible and heuristics such as BM25, which combine domain knowledge (a topical keyword query) with generative learning (Naive Bayes), are dominant. Building on past work, we show that BM25-inspired Gaussian priors for Bayesian logistic regression based on topical keywords provide better effectiveness than the usual L2 (zero mode, uniform variance) Gaussian prior. On two high recall retrieval datasets, the resulting models transition smoothly from BM25 level effectiveness to discriminative effectiveness as training data volume increases, dominating L2 regularization even when substantial training data is available.
Open-Domain Aspect- Opinion Co-Mining with Double-Layer Span Extraction
Authors: Mohna Chakraborty, Adithya Kulkarni, Qi Li 0012
Venue: Conference KDD 2022
Year: 2022

The aspect-opinion extraction tasks extract aspect terms and opinion terms from reviews. The supervised extraction methods achieve state-of-the-art performance but require large-scale human-annotated training data. Thus, they are restricted for open-domain tasks due to the lack of training data. This work addresses this challenge and simultaneously mines aspect terms, opinion terms, and their correspondence in a joint model. We propose an Open-Domain Aspect-Opinion Co-Mining (ODAO) method with a Double-Layer span extraction framework. Instead of acquiring human annotations, ODAO first generates weak labels for unannotated corpus by employing rules-based on universal dependency parsing. Then, ODAO utilizes this weak supervision to train a double-layer span extraction framework to extract aspect terms (ATE), opinion terms (OTE), and aspect-opinion pairs (AOPE). ODAO applies canonical correlation analysis as an early stopping indicator to avoid the model over-fitting to the noise to tackle the noisy weak supervision. ODAO applies a self-training process to gradually enrich the training data to tackle the weak supervision bias issue. We conduct extensive experiments and demonstrate the power of the proposed ODAO. The results on four benchmark datasets for aspect-opinion co-extraction and pair extraction tasks show that ODAO can achieve competitive or even better performance compared with the state-of-the-art fully supervised methods.
Irec: An interactive recommendation framework
Authors: Thiago Silva, Nícollas Silva, Heitor Werneck, Carlos Mito, Adriano C. M. Pereira, Leonardo Rocha
Venue: Conference SIGIR 2022
Year: 2022

Nowadays, most e-commerce and entertainment services have adopted interactive Recommender Systems (RS) to guide the entire journey of users into the system. This task has been addressed as a Multi-Armed Bandit problem where systems must continuously learn and recommend at each iteration. However, despite the recent advances, there is still a lack of consensus on the best practices to evaluate such bandit solutions. Several variables might affect the evaluation process, but most of the works have only been concerned about the accuracy of each method. Thus, this work proposes an interactive RS framework named iRec. It covers the whole experimentation process by following the main RS guidelines. The iRec provides three modules to prepare the dataset, create new recommendation agents, and simulate the interactive scenario. Moreover, it also contains several state-of-the-art algorithms, a hyperparameter tuning module, distinct evaluation metrics, different ways of visualizing the results, and statistical validation.
A Geometric Framework for Query Performance Prediction in Conversational Search
Authors: Guglielmo Faggioli, Nicola Ferro, Cristina Ioana Muntean, Raffaele Perego, Nicola Tonellotto
Venue: Conference SIGIR 2023
Year: 2023

Thanks to recent advances in IR and NLP, the way users interact with search engines is evolving rapidly, with multi-turn conversations replacing traditional one-shot textual queries. Given its interactive nature, Conversational Search (CS) is one of the scenarios that can benefit the most from Query Performance Prediction (QPP) techniques. QPP for the CS domain is a relatively new field and lacks proper framing. In this study, we address this gap by proposing a framework for the application of QPP in the CS domain and use it to evaluate the performance of predictors. We characterize what it means to predict the performance in the CS scenario, where information needs are not independent queries but a series of closely related utterances. We identify three main ways to use QPP models in the CS domain: as a diagnostic tool, as a way to adjust the system's behaviour during a conversation, or as a way to predict the system's performance on the next utterance. Due to the lack of established evaluation procedures for QPP in the CS domain, we propose a protocol to evaluate QPPs for each of the use cases. Additionally, we introduce a set of spatial-based QPP models designed to work the best in the conversational search domain, where dense neural retrieval models are the most common approaches and query cutoffs are typically small. We show how the proposed QPP approaches improve significantly the predictive performance over the state-of-the-art in different scenarios and collections.
Finding the bias and prestige of nodes in networks based on trust scores
Authors: Abhinav Mishra, Arnab Bhattacharya
Venue: Conference WWW 2011
Year: 2011

Many real-life graphs such as social networks and peer-to-peer networks capture the relationships among the nodes by using trust scores to label the edges. Important usage of such networks includes trust prediction, finding the most reliable or trusted node in a local subgraph, etc. For many of these applications, it is crucial to assess the prestige and bias of a node. The bias of a node denotes its propensity to trust/mistrust its neighbours and is closely related to truthfulness. If a node trusts all its neighbours, its recommendation of another node as trustworthy is less reliable. It is based on the idea that the recommendation of a highly biased node should weigh less. In this paper, we propose an algorithm to compute the bias and prestige of nodes in networks where the edge weight denotes the trust score. Unlike most other graph-based algorithms, our method works even when the edge weights are not necessarily positive. The algorithm is iterative and runs in O(km) time where k is the number of iterations and m is the total number of edges in the network. The algorithm exhibits several other desirable properties. It converges to a unique value very quickly. Also, the error in bias and prestige values at any particular iteration is bounded. Further, experiments show that our model conforms well to social theories such as the balance theory (enemy of a friend is an enemy, etc.).
Skolemising blank nodes while preserving isomorphism
Authors: Aidan Hogan
Venue: Conference WWW 2015
Year: 2015

In this paper, we propose and evaluate a scheme to produce canonical labels for blank nodes in RDF graphs. These labels can be used as the basis for a Skolemisation scheme that gets rid of the blank nodes in an RDF graph by mapping them to globally canonical IRIs. Assuming no hash collisions, the scheme guarantees that two Skolemised graphs will be equal if and only if the two input graphs are isomorphic. Although the proposed scheme is exponential in the worst case, we claim that such cases are unlikely to be encountered in practice. To support these claims, we present the results of applying our Skolemisation scheme over a diverse collection of 43.5 million real-world RDF graphs (BTC-2014); we also provide results for some nasty synthetic cases.
Databugger: A test-driven framework for debugging the web of data
Authors: Dimitris Kontokostas, Patrick Westphal, Sören Auer, Sebastian Hellmann, Jens Lehmann, Roland Cornelissen
Venue: Conference WWW 2014
Year: 2014

Linked Open Data (LOD) comprises of an unprecedented volume of structured data on the Web. However, these datasets are of varying quality ranging from extensively curated datasets to crowd-sourced or extracted data of often relatively low quality. We present Databugger, a framework for test-driven quality assessment of Linked Data, which is inspired by test-driven software development. Databugger ensures a basic level of quality by accompanying vocabularies, ontologies and knowledge bases with a number of test cases. The formalization behind the tool employs SPARQL query templates, which are instantiated into concrete quality test queries. The test queries can be instantiated automatically based on a vocabulary or manually based on the data semantics. One of the main advantages of our approach is that domain specific semantics can be encoded in the data quality test cases, thus being able to discover data quality problems beyond conventional quality heuristics.
Protecting User Privacy: An Approach for Untraceable Web Browsing History and Unambiguous User Profiles
Authors: Ghazaleh Beigi, Ruocheng Guo, Alexander Nou, Yanchao Zhang, Huan Liu
Venue: Conference WSDM 2019
Year: 2019

The overturning of the Internet Privacy Rules by the Federal Communications Commissions (FCC) in late March 2017 allows Internet Service Providers (ISPs) to collect, share and sell their customers' Web browsing data without their consent. With third-party trackers embedded on Web pages, this new rule has put user privacy under more risk. The need arises for users on their own to protect their Web browsing history from any potential adversaries. Although some available solutions such as Tor, VPN, and HTTPS can help users conceal their online activities, their use can also significantly hamper personalized online services, i.e., degraded utility. In this paper, we design an effective Web browsing history anonymization scheme, PBooster, aiming to protect users' privacy while retaining the utility of their Web browsing history. The proposed model pollutes users' Web browsing history by automatically inferring how many and what links should be added to the history while addressing the utility-privacy trade-off challenge. We conduct experiments to validate the quality of the manipulated Web browsing history and examine the robustness of the proposed approach for user privacy protection.
Trust prediction using positive, implicit, and negative information
Authors: Min-Hee Jang, Christos Faloutsos, Sang-Wook Kim
Venue: Conference WWW 2014
Year: 2014

We propose a novel method to predict accurately trust relationships of a target user even if he/she does not have much interaction information. The proposed method considers positive, implicit, and negative information of all users in a network based on belief propagation to predict trust relationships of a target user.
Negative link prediction in social media
Authors: Jiliang Tang, Shiyu Chang, Charu C. Aggarwal, Huan Liu
Venue: Conference WSDM 2015
Year: 2015

Signed network analysis has attracted increasing attention in recent years. This is in part because research on signed network analysis suggests that negative links have added value in the analytical process. A major impediment in their effective use is that most social media sites do not enable users to specify them explicitly. In other words, a gap exists between the importance of negative links and their availability in real data sets. Therefore, it is natural to explore whether one can predict negative links automatically from the commonly available social network data. In this paper, we investigate the novel problem of negative link prediction with only positive links and content-centric interactions in social media. We make a number of important observations about negative links, and propose a principled framework NeLP, which can exploit positive links and content-centric interactions to predict negative links. Our experimental results on real-world social networks demonstrate that the proposed NeLP framework can accurately predict negative links with positive links and content-centric interactions. Our detailed experiments also illustrate the relative importance of various factors to the effectiveness of the proposed framework.