OpenRaiser/Intern-Atlas huggingface dataset:
Se-pqa: Personalized community question answering
Authors: Pranav Kasela, Marco Braga, Gabriella Pasi, Raffaele Perego
Venue: Conference WWW 2024
Year: 2024

Personalization in Information Retrieval is a topic studied for a long time. Nevertheless, there is still a lack of high-quality, real-world datasets to conduct large-scale experiments and evaluate models for personalized search. This paper contributes to filling this gap by introducing SE-PQA (StackExchange - Personalized Question Answering), a new curated resource to design and evaluate personalized models related to the task of community Question Answering (cQA). The contributed dataset includes more than 1 million queries and 2 million answers, annotated with a rich set of features modeling the social interactions among the users of a popular cQA platform. We describe the characteristics of SE-PQA and detail the features associated with questions and answers. We also provide reproducible baseline methods for the cQA task based on the resource, including deep learning models and personalization approaches. The results of the preliminary experiments conducted show the appropriateness of SE-PQA to train effective cQA models; they also show that personalization remarkably improves the effectiveness of all the methods tested. Furthermore, we show the benefits in terms of robustness and generalization of combining data from multiple communities for personalization purposes.
Diffusion models for generative outfit recommendation
Authors: Yiyan Xu, Wenjie Wang, Fuli Feng, Yunshan Ma, Jizhi Zhang, Xiangnan He
Venue: Conference SIGIR 2024
Year: 2024

Outfit Recommendation (OR) in the fashion domain has evolved through two stages: Pre-defined Outfit Recommendation and Personalized Outfit Composition. However, both stages are constrained by existing fashion products, limiting their effectiveness in addressing users' diverse fashion needs. Recently, the advent of AI-generated content provides the opportunity for OR to transcend these limitations, showcasing the potential for personalized outfit generation and recommendation. To this end, we introduce a novel task called Generative Outfit Recommendation (GOR), aiming to generate a set of fashion images and compose them into a visually compatible outfit tailored to specific users. The key objectives of GOR lie in the high fidelity, compatibility, and personalization of generated outfits. To achieve these, we propose a generative outfit recommender model named DiFashion, which empowers exceptional diffusion models to accomplish the parallel generation of multiple fashion images. To ensure three objectives, we design three kinds of conditions to guide the parallel generation process and adopt Classifier-Free-Guidance to enhance the alignment between the generated images and conditions. We apply DiFashion on both personalized Fill-In-The-Blank and GOR tasks and conduct extensive experiments on iFashion and Polyvore-U datasets. The quantitative and human-involved qualitative evaluation demonstrate the superiority of DiFashion over competitive baselines.
A new creative generation pipeline for click-through rate with stable diffusion model
Authors: Hao Yang, Jianxin Yuan, Shuai Yang, Linhe Xu, Shuo Yuan, Yifan Zeng
Venue: Conference WWW 2024
Year: 2024

In online advertising scenario, sellers often create multiple creatives to provide comprehensive demonstrations, making it essential to present the most appealing design to maximize the Click-Through Rate (CTR). However, sellers generally struggle to consider users' preferences for creative design, leading to the relatively lower aesthetics and quantities compared to Artificial Intelligence (AI)-based approaches. Traditional AI-based approaches still face the same problem of not considering user information while having limited aesthetic knowledge from designers. In fact that fusing the user information, the generated creatives can be more attractive because different users may have different preferences. To optimize the results, the generated creatives in traditional methods are then ranked by another module named creative ranking model. The ranking model can predict the CTR score for each creative considering user features. However, the two above stages (generating creatives and ranking creatives) are regarded as two different tasks and are optimized separately. Specifically, generating creatives in the first stage without considering the target of improving CTR task may generate several creatives with poor quality, leading to dilute online impressions and directly making bad effectiveness on online results. In this paper, we proposed a new automated C reative G eneration pipeline for Click-Through Rate (CG4CTR).1 The code is at with the goal of improving CTR during the creative generation stage. In this pipeline, a new creative is automatically generated and selected by stable diffusion method with the LoRA model and two novel models: prompt model and reward model. Our contributions have four parts: 1) The inpainting mode in stable diffusion method is firstly applied to creative image generation task in online advertising scene. A self-cyclic generation pipeline is proposed to ensure the convergence of training. 2) Prompt model is designed to generate individualized creative images for different user groups, which can further improve the diversity and quality of the generated creatives. 3) Reward model comprehensively considers the multi-modal features of image and text to improve the effectiveness of creative ranking task, and it is also critical in self-cyclic generation pipeline. 4) The significant benefits obtained in online and offline experiments verify the significance of our proposed method.
Corroborating information from disagreeing views
Authors: Alban Galland, Serge Abiteboul, Amélie Marian, Pierre Senellart
Venue: Conference WSDM 2010
Year: 2010

We consider a set of views stating possibly conflicting facts. Negative facts in the views may come, e.g., from functional dependencies in the underlying database schema. We want to predict the truth values of the facts. Beyond simple methods such as voting (typically rather accurate), we explore techniques based on "corroboration", i.e., taking into account trust in the views. We introduce three fixpoint algorithms corresponding to different levels of complexity of an underlying probabilistic model. They all estimate both truth values of facts and trust in the views. We present experimental studies on synthetic and real-world data. This analysis illustrates how and in which context these methods improve corroboration results over baseline methods. We believe that corroboration can serve in a wide range of applications such as source selection in the semantic Web, data quality assessment or semantic annotation cleaning in social networks. This work sets the bases for a wide range of techniques for solving these more complex problems.
Exploring social annotations for the semantic web
Authors: Xian Wu, Lei Zhang, Yong Yu
Venue: Conference WWW 2006
Year: 2006

In order to obtain a machine understandable semantics for web resources, research on the Semantic Web tries to annotate web resources with concepts and relations from explicitly defined formal ontologies. This kind of formal annotation is usually done manually or semi-automatically. In this paper, we explore a complement approach that focuses on the "social annotations of the web" which are annotations manually made by normal web users without a pre-defined formal ontology. Compared to the formal annotations, although social annotations are coarse-grained, informal and vague, they are also more accessible to more people and better reflect the web resources' meaning from the users' point of views during their actual usage of the web resources. Using a social bookmark service as an example, we show how emergent semantics [2] can be statistically derived from the social annotations. Furthermore, we apply the derived emergent semantics to discover and search shared web bookmarks. The initial evaluation on our implementation shows that our method can effectively discover semantically related web bookmarks that current social bookmark service can not discover easily.
Question Answering on Top of the BT Digital Library
Authors: Philipp Cimiano, Peter Haase, York Sure, Johanna Völker, Yimin Wang
Venue: Conference WWW 2006
Year: 2006

In this poster we present an approach to query answering over knowledge sources that makes use of different ontology management components within an application scenario of the BT Digital Library. The novelty of the approach lies in the combination of different semantic technologies providing a clear benefit for the application scenario considered.
Effective latent space graph-based re-ranking model with global consistency
Authors: Hongbo Deng, Michael R. Lyu, Irwin King
Venue: Conference WSDM 2009
Year: 2009

Recently the re-ranking algorithms have been quite popular for web search and data mining. However, one of the issues is that those algorithms treat the content and link information individually. Inspired by graph-based machine learning algorithms, we propose a novel and general framework to model the re-ranking algorithm, by regularizing the smoothness of ranking scores over the graph, along with a regularizer on the initial ranking scores (which are obtained by the base ranker). The intuition behind the model is the global consistency over the graph: similar entities are likely to have the same ranking scores with respect to a query. Our approach simultaneously incorporates the content with other explicit or implicit link information in a latent space graph. Then an effective unified re-ranking algorithm is performed on the graph with respect to the query. To illustrate our methodology, we apply the framework to literature retrieval and expert finding applications on DBLP bibliography data. We compare the proposed method with the initial language model method and another PageRank-style re-ranking method. Also, we evaluate the proposed method with varying graphs and settings. Experimental results show that the improvement in our proposed method is consistent and promising.
Opinion mining from noisy text data
Authors: Lipika Dey, S. K. Mirajul Haque
Venue: Conference SIGIR 2008
Year: 2008

The proliferation of Internet has not only generated huge volumes of unstructured information in the form of web documents, but a large amount of text is also generated in the form of emails, blogs, and feedbacks etc. The data generated from online communication acts as potential gold mines for discovering knowledge. Text analytics has matured and is being successfully employed to mine important information from unstructured text documents. Most of these techniques use Natural Language Processing techniques which assume that the underlying text is clean and correct. Statistical techniques, though not as accurate as linguistic mechanisms, are also employed for the purpose to overcome the dependence on clean text. The chief bottleneck for designing statistical mechanisms is however its dependence on appropriately annotated training data. None of these methodologies are suitable for mining information from online communication text data due to the fact that they are often noisy. These texts are informally written. They suffer from spelling mistakes, grammatical errors, improper punctuation and irrational capitalization. This paper focuses on opinion extraction from noisy text data. It is aimed at extracting and consolidating opinions of customers from blogs and feedbacks, at multiple levels of granularity. Ours is a hybrid approach, in which we initially employ a semi-supervised method to learn domain knowledge from a training repository which contains both noisy and clean text. Thereafter we employ localized linguistic techniques to extract opinion expressions from noisy text. We have developed a system based on this approach, which provides the user with a platform to analyze opinion expressions extracted from a repository.
Clustering and Exploring Search Results Using Timeline Constructions
Authors: Omar Alonso, Michael Gertz, Ricardo Baeza-Yates
Venue: Conference CIKM 2009
Year: 2009

Time is an important dimension of any information space and can be very useful in information retrieval and in particular clustering and exploration of search results. Search result clustering is a feature integrated in some of today's search engines, allowing users to further explore search results. However, only little work has been done on exploiting temporal information embedded in documents for the presentation, clustering, and exploration of search results along well-defined timelines. In this paper, we present an add-on to traditional information retrieval applications in which we exploit various temporal information associated with documents to present and cluster documents along timelines. Temporal information expressed in the form of, e.g., date and time tokens or temporal references, appear in documents as part of the textual context or metadata. Using temporal entity extraction techniques, we show how temporal expressions are made explicit and used in the construction of multiple-granularity timelines. We discuss how hit-list based search results can be clustered according to temporal aspects, anchored in the constructed timelines, and how time-based document clusters can be used to explore search results that include temporal snippets. We also outline a prototypical implementation and evaluation that demonstrates the feasibility and functionality of our framework.
Penguins in sweaters, or serendipitous entity search on user-generated content
Authors: Ilaria Bordino, Yelena Mejova, Mounia Lalmas
Venue: Conference CIKM 2013
Year: 2013

In many cases, when browsing the Web users are searching for specific information or answers to concrete questions. Sometimes, though, users find unexpected, yet interesting and useful results, and are encouraged to explore further. What makes a result serendipitous? We propose to answer this question by exploring the potential of entities extracted from two sources of user-generated content -- Wikipedia, a user-curated online encyclopedia, and Yahoo! Answers, a more unconstrained question/answering forum -- in promoting serendipitous search. In this work, the content of each data source is represented as an entity network, which is further enriched with metadata about sentiment, writing quality, and topical category. We devise an algorithm based on lazy random walk with restart to retrieve entity recommendations from the networks. We show that our method provides novel results from both datasets, compared to standard web search engines. However, unlike previous research, we find that choosing highly emotional entities does not increase user interest for many categories of entities, suggesting a more complex relationship between topic matter and the desirable metadata attributes in serendipitous search.
Model-based document clustering with a collapsed gibbs sampler
Authors: Daniel David Walker, Eric K. Ringger
Venue: Conference KDD 2008
Year: 2008

Model-based algorithms are emerging as a preferred method for document clustering. As computing resources improve, methods such as Gibbs sampling have become more common for parameter estimation in these models. Gibbs sampling is well understood for many applications, but has not been extensively studied for use in document clustering. We explore the convergence rate, the possibility of label switching, and chain summarization methodologies for document clustering on a particular model, namely a mixture of multinomials model, and show that fairly simple methods can be employed, while still producing clusterings of superior quality compared to those produced with the EM algorithm.
Query representation for cross-temporal information retrieval
Authors: Miles Efron
Venue: Conference SIGIR 2013
Year: 2013

This paper addresses the problem of long-term language change in information retrieval (IR) systems. IR research has often ignored lexical drift. But in the emerging domain of massive digitized book collections, the risk of vocabulary mismatch due to language change is high. Collections such as Google Books and the Hathi Trust contain text written in the vernaculars of many centuries. With respect to IR, changes in vocabulary and orthography make 14th-Century English qualitatively different from 21st-Century English. This challenges retrieval models that rely on keyword matching. With this challenge in mind, we ask: given a query written in contemporary English, how can we retrieve relevant documents that were written in early English? We argue that search in historically diverse corpora is similar to cross-language retrieval (CLIR). By considering "modern" English and "archaic" English as distinct languages, CLIR techniques can improve what we call cross-temporal IR (CTIR). We focus on ways to combine evidence to improve CTIR effectiveness, proposing and testing several ways to handle language change during book search. We find that a principled combination of three sources of evidence during relevance feedback yields strong CTIR performance.
Interactive system for reasoning about document age
Authors: Adam Jatowt, Ricardo Campos
Venue: Conference CIKM 2017
Year: 2017

Recently, many historical texts have become digitized and made accessible for search and browsing. Professionals who work with collections of such texts often need to verify the correctness of documents' key metadata - their creation dates. In this paper, we demonstrate an interactive system for estimating the age of documents. It may be useful not only for tagging a large number of undated documents, but also for verifying already known timestamps. In order to infer probable dates, we rely on a large scale lexical corpora, Google Books Ngrams. Besides estimating the document creation year, the system also outputs evidences to support age detection and reasoning process and allows testing different hypotheses about document's age.
Large scale analysis of changes in english vocabulary over recent time
Authors: Adam Jatowt, Katsumi Tanaka
Venue: Conference CIKM 2012
Year: 2012

Recently many historical texts have become digitized and made accessible for search and browsing. As human language is subject to constant evolution, these texts pose varying challenges to current users. In this paper we report the results of large-scale studies on the usage of words and the evolution of English language vocabulary over the last two centuries to help with understanding its impact on readability and retrieval of historical documents. We perform analysis of several lexical factors which may influence accessibility and readability of historical texts based on two large scale lexical corpora: the Corpus of Historical American English and Google Books 1-gram.
Publication date prediction through reverse engineering of the web
Authors: Liudmila Ostroumova Prokhorenkova, Petr Prokhorenkov, Egor Samosvat, Pavel Serdyukov
Venue: Conference WSDM 2016
Year: 2016

In this paper, we focus on one of the most challenging tasks in temporal information retrieval: detection of a web page publication date. The natural approach to this problem is to find the publication date in the HTML body of a page. However, there are two fundamental problems with this approach. First, not all web pages contain the publication dates in their texts. Second, it is hard to distinguish the publication date among all the dates found in the page's text. The approach we suggest in this paper supplements methods of date extraction from the page's text with novel link-based methods of dating. Some of our link-based methods are based on a probabilistic model of the Web graph structure evolution, which relies on the publication dates of web pages as on its parameters. We use this model to estimate the publication dates of web pages: based only on the link structure currently observed, we perform a ``reverse engineering'' to reveal the whole process of the Web's evolution.
An effective framework for question answering over freebase via reconstructing natural sequences
Authors: Bin Yue, Min Gui, Jiahui Guo, Zhenglu Yang, Jin-Mao Wei, Shaodi You
Venue: Conference WWW 2017
Year: 2017

Question answering over knowledge bases has rapidly developed with the continuous expansion of resources. However, how to match the natural language question to the structured answer entities in the knowledge bases remains a major challenge. In this paper, we propose an effective framework that bridges the gap between the given question and the answer entities, by reconstructing the intermediate natural sequences on the basis of the entities and relations in knowledge bases. The intuitive idea is that these intermediate sequences may encode rich semantic information that can identify the candidate answer entities. Experimental evaluation is conducted on a benchmark dataset WebQuestions. Results demonstrate the effectiveness of our proposed framework, i.e., it outperforms state-of-the-art models by up to 6.8% in terms of F1 score.
Joke retrieval: recognizing the same joke told differently
Authors: Lisa Friedland, James Allan
Venue: Conference CIKM 2008
Year: 2008

In a corpus of jokes, a human might judge two documents to be the "same joke" even if characters, locations, and other details are varied. A given joke could be retold with an entirely different vocabulary while still maintaining its identity. Since most retrieval systems consider documents to be related only when their word content is similar, we propose joke retrieval as a domain where standard language models may fail. Other meaning-centric domains include logic puzzles, proverbs and recipes; in such domains, new techniques may be required to enable us to search effectively. For jokes, a necessary component of any retrieval system will be the ability to identify the "same joke," so we examine this task in both ranking and classification settings. We exploit the structure of jokes to develop two domain-specific alternatives to the "bag of words" document model. In one, only the punch lines, or final sentences, are compared; in the second, certain categories of words (e.g., professions and countries) are tagged and treated as interchangeable. Each technique works well for certain jokes. By combining the methods using machine learning, we create a hybrid that achieves higher performance than any individual approach.
Fractal summarization for mobile devices to access large documents on the web
Authors: Christopher C. Yang, Fu Lee Wang
Venue: Conference WWW 2003
Year: 2003

Wireless access with mobile (or handheld) devices is a promising addition to the WWW and traditional electronic business. Mobile devices provide convenience and portable access to the huge information space on the Internet without requiring users to be stationary with network connection. However, the limited screen size, narrow network bandwidth, small memory capacity and low computing power are the shortcomings of handheld devices. Loading and visualizing large documents on handheld devices become impossible. The limited resolution restricts the amount of information to be displayed. The download time is intolerably long. In this paper, we introduce the fractal summarization model for document summarization on handheld devices. Fractal summarization is developed based on the fractal theory. It generates a brief skeleton of summary at the first stage, and the details of the summary on different levels of the document are generated on demands of users. Such interactive summarization reduces the computation load in comparing with the generation of the entire summary in one batch by the traditional automatic summarization, which is ideal for wireless access. Three-tier architecture with the middle-tier conducting the major computation is also discussed. Visualization of summary on handheld devices is also investigated.