OpenRaiser/Intern-Atlas huggingface dataset:
Entity Set Expansion via Knowledge Graphs
Authors: Xiangling Zhang, Yueguo Chen, Jun Chen, Xiaoyong Du, Ke Wang, Ji-Rong Wen
Venue: Conference SIGIR 2017
Year: 2017

The entity set expansion problem is to expand a small set of seed entities to a more complete set of similar entities. It can be applied in applications such as web search, item recommendation and query expansion. Traditionally, people solve this problem by exploiting the co-occurrence of entities within web pages, where latent semantic correlation among seed entities cannot be revealed. We propose a novel approach to solve the problem using knowledge graphs, by considering the deficiency (e.g., incompleteness) of knowledge graphs. We design an effective ranking model based on the semantic features of seeds to retrieve the candidate entities. Extensive experiments on public datasets show that the proposed solution significantly outperforms the state-of-the-art techniques.
Churn prediction in new users of yahoo! answers
Authors: Gideon Dror, Dan Pelleg, Oleg Rokhlenko, Idan Szpektor
Venue: Conference WWW 2012
Year: 2012

One of the important targets of community-based question answering (CQA) services, such as Yahoo! Answers, Quora and Baidu Zhidao, is to maintain and even increase the number of active answerers, that is the users who provide answers to open questions. The reasoning is that they are the engine behind satisfied askers, which is the overall goal behind CQA. Yet, this task is not an easy one. Indeed, our empirical observation shows that many users provide just one or two answers and then leave. In this work we try to detect answerers that are about to quit, a task known as churn prediction, but unlike prior work, we focus on new users. To address the task of churn prediction in new users, we extract a variety of features to model the behavior of \YA{} users over the first week of their activity, including personal information, rate of activity, and social interaction with other users. Several classifiers trained on the data show that there is a statistically significant signal for discriminating between users who are likely to churn and those who are not. A detailed feature analysis shows that the two most important signals are the total number of answers given by the user, closely related to the motivation of the user, and attributes related to the amount of recognition given to the user, measured in counts of best answers, thumbs up and positive responses by the asker.
User churn in focused question answering sites: characterizations and prediction
Authors: Jagat Sastry Pudipeddi, Leman Akoglu, Hanghang Tong
Venue: Conference WWW 2014
Year: 2014

Given a user on a Q&A site, how can we tell whether s/he is engaged with the site or is rather likely to leave? What are the most evidential factors that relate to users churning? Question and Answer (Q&A) sites form excellent repositories of collective knowledge. To make these sites self- sustainable and long-lasting, it is crucial to ensure that new users as well as the site veterans who provide most of the answers keep engaged with the site. As such, quantifying the engagement of users and preventing churn in Q&A sites are vital to improve the lifespan of these sites. We study a large data collection from stackoverflow.com to identify significant factors that correlate with newcomer user churn in the early stage and those that relate to veterans leaving in the later stage. We consider the problem under two settings: given (i) the first k posts, or (ii) first T days of activity of a user, we aim to identify evidential features to automatically classify users so as to spot those who are about to leave. We find that in both cases, the time gap between subsequent posts is the most significant indicator of diminishing interest of users, besides other indicative factors like answering speed, reputation of those who answer their questions, and number of answers received by the user.
Hashtag Usage in a Geographically-Local Microblogging App
Authors: Helge Reelfs, Timon Mohaupt, Oliver Hohlfeld, Niklas Henckell
Venue: Conference WWW 2019
Year: 2019

This paper studies for the first time the usage and propagation of hashtags in a new and fundamentally different type of social media that is i) without profiles and ii) location-based to only show nearby posted content. Our study is based on analyzing the mobile-only Jodel microblogging app, which has an established user base in several European countries and Saudi Arabia. All posts are user to user anonymous (i.e., no displayed user handles) and are only displayed in the proximity of the user’s location (up to 20 km). It thereby forms local communities and opens the question of how information propagates within and between these communities. We tackle this question by applying established metrics for Twitter hashtags to a ground-truth data set of Jodel posts within Germany that spans three years. We find the usage of hashtags in Jodel to differ from Twitter; despite embracing local communication in its design, Jodel hashtags are mostly used country-wide.
Crowd fraud detection in internet advertising
Authors: Tian Tian, Jun Zhu, Fen Xia, Xin Zhuang, Tong Zhang
Venue: Conference WWW 2015
Year: 2015

The rise of crowdsourcing brings new types of malpractices in Internet advertising. One can easily hire web workers through malicious crowdsourcing platforms to attack other advertisers. Such human generated crowd frauds are hard to detect by conventional fraud detection methods. In this paper, we carefully examine the characteristics of the group behaviors of crowd fraud and identify three persistent patterns, which are moderateness, synchronicity and dispersivity. Then we propose an effective crowd fraud detection method for search engine advertising based on these patterns, which consists of a constructing stage, a clustering stage and a filtering stage. At the constructing stage, we remove irrelevant data and reorganize the click logs into a surfer-advertiser inverted list; At the clustering stage, we define the sync-similarity between surfers' click histories and transform the coalition detection to a clustering problem, solved by a nonparametric algorithm; and finally we build a dispersity filter to remove false alarm clusters. The nonparametric nature of our method ensures that we can find an unbounded number of coalitions with nearly no human interaction. We also provide a parallel solution to make the method scalable to Web data and conduct extensive experiments. The empirical results demonstrate that our method is accurate and scalable.
Learning from user interactions in personal search via attribute parameterization
Authors: Michael Bendersky, Xuanhui Wang, Donald Metzler, Marc Najork
Venue: Conference WSDM 2017
Year: 2017

User interaction data (e.g., click data) has proven to be a powerful signal for learning-to-rank models in web search. However, such models require observing multiple interactions across many users for the same query-document pair to achieve statistically meaningful gains. Therefore, utilizing user interaction data for improving search over personal, rather than public, content is a challenging problem. First, the documents (e.g., emails or private files) are not shared across users. Second, user search queries are of personal nature (e.g., "alice's address") and may not generalize well across users. In this paper, we propose a solution to these challenges, by projecting user queries and documents into a multi-dimensional space of fine-grained and semantically coherent attributes. We then introduce a novel parameterization technique to overcome sparsity in the multi-dimensional attribute space. Attribute parameterization enables effective usage of cross-user interactions for improving personal search quality -- which is a first such published result, to the best of our knowledge. Experiments with a dataset derived from interactions of users of one of the world's largest personal search engines demonstrate the effectiveness of the proposed attribute parameterization technique.
Rank by time or by relevance?: Revisiting email search
Authors: David Carmel, Guy Halawi, Liane Lewin-Eytan, Yoelle Maarek, Ariel Raviv
Venue: Conference CIKM 2015
Year: 2015

With Web mail services offering larger and larger storage capacity, most users do not feel the need to systematically delete messages anymore and inboxes keep growing. It is quite surprising that in spite of the huge progress of relevance ranking in Web Search, mail search results are still typically ranked by date. This can probably be explained by the fact that users demand perfect recall in order to "re-find" a previously seen message, and would not trust relevance ranking. Yet mail search is still considered a difficult and frustrating task, especially when trying to locate older messages. In this paper, we study the current search traffic of Yahoo mail, a major Web commercial mail service, and discuss the limitations of ranking search results by date. We argue that this sort-by-date paradigm needs to be revisited in order to account for the specific structure and nature of mail messages, as well as the high-recall needs of users. We describe a two-phase ranking approach, in which the first phase is geared towards maximizing recall and the second phase follows a learning-to-rank approach that considers a rich set of mail-specific features to maintain precision. We present our results obtained on real mail search query traffic, for three different datasets, via manual as well as automatic evaluation. We demonstrate that the default time-driven ranking can be significantly improved in terms of both recall and precision, by taking into consideration time recency and textual similarity to the query, as well as mail-specific signals such as users' actions.
Promoting relevant results in time-ranked mail search
Authors: David Carmel, Liane Lewin-Eytan, Alex Libov, Yoelle Maarek, Ariel Raviv
Venue: Conference WWW 2017
Year: 2017

Mail search has traditionally served time-ranked results, even if it has been shown that relevance ranking provides higher retrieval quality on average. Some Web mail services have recently started to provide relevance ranking options such as the relevance toggle in the search results page of Yahoo Mail, or the ``top results" section in Inbox by Gmail. Yet, ranking results by relevance is not accepted by all, either in mail search, or in in other domains such as social media, where it has even triggered some public outcry. Given the sensitivity of the topic, we propose here to investigate a mixed approach of promoting the most relevant results, to which we refer as ``heroes'', on top of time-ranked results. We argue that this approach represents a good compromise to mail searchers, supporting on one hand the time sorted paradigm they are familiar with, while being almost as effective as full relevance ranking view that Web mail users seem to be reluctant to adopt. We describe three hero-selection algorithms we have devised and the associated experiments we have conducted in Yahoo mail. We measure retrieval success via two metrics: MRR (Mean Reciprocal Rank) and [email protected], and verify agreement between these metrics and users' direct feedback. We demonstrate that supplementing time-sorted results with hero results leads to a higher MRR than the traditional time-sorted view. We additionally show that MRR better reflects users' perception of quality than [email protected] Finally, we report on online results following the successful launch of one of our hero-selection algorithms for all Yahoo enterprise mail users and a few million Yahoo Web mail users.
Query expansion for email search
Authors: Saar Kuzi, David Carmel, Alex Libov, Ariel Raviv
Venue: Conference SIGIR 2017
Year: 2017

This work studies the effectiveness of query expansion for email search. Three state-of-the-art expansion methods are examined: 1) a global translation-based expansion model; 2) a personalized-based word embedding model; 3) the classical pseudo-relevance-feedback model. Experiments were conducted with two mail datasets extracted from a large query log of a Web mail service. Our results demonstrate the significant contribution of query expansion for measuring the similarity between the query and email messages. On the other hand, the contribution of expansion methods for a well trained learning-to-rank scoring function that exploits many relevance signals, was found to be modest.
Multi-view Embedding-based Synonyms for Email Search
Authors: Cheng Li, Mingyang Zhang, Michael Bendersky, Hongbo Deng, Donald Metzler, Marc Najork
Venue: Conference SIGIR 2019
Year: 2019

Synonym expansion is a technique that adds related words to search queries, which may lead to more relevant documents being retrieved, thus improving recall. There is extensive prior work on synonym expansion for web search, however very few studies have tackled its application for email search. Synonym expansion for private corpora like emails poses several unique research challenges. First, the emails are not shared across users, which precludes us from directly employing query-document bipartite graphs, which are standard in web search synonym expansion. Second, user search queries are of personal nature, and may not be generalizable across users. Third, the size of the underlying corpora from which the synonyms may be mined is relatively small (i.e., user's private email inbox) compared to the size of the web corpus. Therefore, in this paper, we propose a solution tailored to the challenges of synonym expansion for email search. We formulate it as a multi-view learning problem, and propose a novel embedding-based model that joins information from multiple sources to obtain the optimal synonym candidates. To demonstrate the effectiveness of the proposed technique, we evaluate our model using both explicit human ratings as well as a live experiment using the Gmail Search service, one of the world's largest email search engines.
Combining fields in known-item email search
Authors: Craig Macdonald, Iadh Ounis
Venue: Conference SIGIR 2006
Year: 2006

Emails are examples of structured documents with various fields. These fields can be exploited to enhance the retrieval effectiveness of an Information Retrieval (IR) system that mailing list archives. In recent experiments of the TREC2005 Enterprise track, various fields were applied to varying degrees of success by the participants. In his work, using a field-based weighting model, we investigate the retrieval performance attainable by each field, and examine when fields evidence should be combined or not.
Personalizing web search using long term browsing history
Authors: Nicolaas Matthijs, Filip Radlinski
Venue: Conference WSDM 2011
Year: 2011

Personalizing web search results has long been recognized as an avenue to greatly improve the search experience. We present a personalization approach that builds a user interest profile using users' complete browsing behavior, then uses this model to rerank web results. We show that using a combination of content and previously visited websites provides effective personalization. We extend previous work by proposing a number of techniques for filtering previously viewed content that greatly improve the user model used for personalization. Our approaches are compared to previous work in offline experiments and are evaluated against unpersonalized web search in large scale online tests. Large improvements are found in both cases.
Separate and Attend in Personal Email Search
Authors: Yu Meng, Maryam Karimzadehgan, Honglei Zhuang, Donald Metzler
Venue: Conference WSDM 2020
Year: 2020

In personal email search, user queries often impose different requirements on different aspects of the retrieved emails. For example, the query "my recent flight to the US" requires emails to be ranked based on both textual contents and recency of the email documents, while other queries such as "medical history" do not impose any constraints on the recency of the email. Recent deep learning-to-rank models for personal email search often directly concatenate dense numerical features (e.g., document age) with embedded sparse features (e.g., n-gram embeddings). In this paper, we first show with a set of experiments on synthetic datasets that direct concatenation of dense and sparse features does not lead to the optimal search performance of deep neural ranking models. To effectively incorporate both sparse and dense email features into personal email search ranking, we propose a novel neural model, SepAttn. SepAttn first builds two separate neural models to learn from sparse and dense features respectively, and then applies an attention mechanism at the prediction level to derive the final prediction from these two models. We conduct a comprehensive set of experiments on a large-scale email search dataset, and demonstrate that our SepAttn model consistently improves the search quality over the baseline models.
Inlook: Revisiting email search experience
Authors: Pranav Ramarao, Suresh Iyengar, Pushkar V. Chitnis, Raghavendra Udupa, Balasubramanyan Ashok
Venue: Conference SIGIR 2016
Year: 2016

Emails continue to remain the most important and widely used mode of online communication despite having its origins in the middle of last century and being threatened by a variety of online communication innovations. While several studies have predicted the continuous growth of volume of email communication, there is little innovation on improving the search in emails, an imperative part of the user experience. In this work, we present a lightweight email application codenamed InLook, that intends to provide a productive search experience.
Predicting short-term interests using activity-based search context
Authors: Ryen W. White, Paul N. Bennett, Susan T. Dumais
Venue: Conference CIKM 2010
Year: 2010

A query considered in isolation offers limited information about a searcher's intent. Query context that considers pre-query activity (e.g., previous queries and page visits), can provide richer information about search intentions. In this paper, we describe a study in which we developed and evaluated user interest models for the current query, its context (from pre-query session activity), and their combination, which we refer to as intent. Using large-scale logs, we evaluate how accurately each model predicts the user's short-term interests under various experimental conditions. In our study we: (i) determine the extent of opportunity for using context to model intent; (ii) compare the utility of different sources of behavioral evidence (queries, search result clicks, and Web page visits) for building predictive interest models, and; (iii) investigate optimally combining the query and its context by learning a model that predicts the context weight for each query. Our findings demonstrate significant opportunity in leveraging contextual information, show that context and source influence predictive accuracy, and show that we can learn a near-optimal combination of the query and context for each query. The findings can inform the design of search systems that leverage contextual information to better understand, model, and serve searchers' information needs.
Situational context for ranking in personal search
Authors: Hamed Zamani, Michael Bendersky, Xuanhui Wang, Mingyang Zhang
Venue: Conference WWW 2017
Year: 2017

Modern search engines leverage a variety of sources, beyond the conventional query-document content similarity, to improve their ranking performance. Among them, query context has attracted attention in prior work. Previously, query context was mainly modeled by user search history, either long-term or short-term, to help the ranking of future queries. In this paper, we focus on situational context, i.e., the contextual features of the current search request that are independent from both query content and user history. As an example, situational context can depend on search request time and location. We propose two context-aware ranking models based on neural networks. The first model learns a low-dimensional deep representation from the combination of contextual features. The second model extends the first one by leveraging binarized contextual features in addition to the high-level abstractions learned using a deep network.
Cross-language linking of news stories on the web using interlingual topic modelling
Authors: Wim De Smet, Marie-Francine Moens
Venue: Conference CIKM 2009
Year: 2009

We have studied the problem of linking event information across different languages without the use of translation systems or dictionaries. The linking is based on interlingua information obtained through probabilistic topic models trained on comparable corpora written in two languages (in our case English and Dutch). The achieve this, we expand the Latent Dirichlet Allocation model to process documents in two languages. We demonstrate the validity of the learned interlingual topics in a document clustering task, where the evaluation is performed on Google News.
Migration motif: a spatial - temporal pattern mining approach for financial markets
Authors: Xiaoxi Du, Ruoming Jin, Liang Ding, Victor E. Lee, John H. Thornton Jr.
Venue: Conference KDD 2009
Year: 2009

A recent study by two prominent finance researchers, Fama and French, introduces a new framework for studying risk vs. return: the migration of stocks across size-value portfolio space. Given the financial events of 2008, this first attempt to disentangle the relationships between migration behavior and stock returns is especially timely. Their work, however, derives results only for market segments, not individual companies, and only for one-year moves. Thus, we see a new challenge for financial data mining: how to capture and categorize the migration of individual companies, and how such behavior affects their returns.
AppHolmes: Detecting and characterizing app collusion among third-party Android markets
Authors: Mengwei Xu, Yun Ma, Xuanzhe Liu, Felix Xiaozhu Lin, Yunxin Liu
Venue: Conference WWW 2017
Year: 2017

Background activities on smartphones are essential to today's "always-on" mobile device experience. Yet, there lacks a clear understanding of the cooperative behaviors among background activities as well as a quantification of the consequences. In this paper, we present the first in-depth study of app collusion, in which one app surreptitiously launches others in the background without user's awareness. To enable the study, we develop AppHolmes, a static analysis tool for detecting app collusion by examining the app binaries. By analyzing 10,000 apps from top third-party app markets, we found that i) covert, cooperative behaviors in background app launch are surprisingly pervasive, ii) most collusion is caused by shared services, libraries, or common interest among apps, and iii) collusion has serious impact on performance, efficiency, and security. Overall, our work presents a strong implication on future mobile system design.
Boosting Recommender Systems with Advanced Embedding Models
Authors: Gjorgjina Cenikj, Sonja Gievska
Venue: Conference WWW 2020
Year: 2020

Recommender systems are paramount in providing personalized content and intelligent content filtering on any social media platform, web portal, and online application. In line with the current trends in the field directed towards mapping problem and data encoding representations from other fields, this research investigates the feasibility of augmenting a graph-based recommender system for Amazon products with two state-of-the-art representation models. In particular, the potential benefits of using the language representation model BERT and GraphSage based representations of nodes and edges for improving the quality of the recommendations were investigated. Link prediction and link attribute inference were used to identify the products that the users will buy and predict the rating they will give to a product, respectively. The initial results of our exploratory study are encouraging and point to potential directions for future research.