OpenRaiser/Intern-Atlas huggingface dataset:
Extracting search-focused key n-grams for relevance ranking in web search
Authors: Chen Wang, Keping Bi, Yunhua Hu, Hang Li, Guihong Cao
Venue: WSDM
Year: 2012

In web search, relevance ranking of popular pages is relatively easy, because of the inclusion of strong signals such as anchor text and search log data. In contrast, with less popular pages, relevance ranking becomes very challenging due to a lack of information. In this paper the former is referred to as head pages, and the latter tail pages. We address the challenge by learning a model that can extract search-focused key n-grams from web pages, and using the key n-grams for searches of the pages, particularly, the tail pages. To the best of our knowledge, this problem has not been previously studied. Our approach has four characteristics. First, key n-grams are search-focused in the sense that they are defined as those which can compose "good queries" for searching the page. Second, key n-grams are learned in a relative sense using learning to rank techniques. Third, key n-grams are learned using search log data, such that the characteristics of key n-grams in the search log data, particularly in the heads; can be applied to the other data, particularly to the tails. Fourth, the extracted key n-grams are used as features of the relevance ranking model also trained with learning to rank techniques. Experiments validate the effectiveness of the proposed approach with large-scale web search datasets. The results show that our approach can significantly improve relevance ranking performance on both heads and tails; and particularly tails, compared with baseline approaches. Characteristics of our approach have also been fully investigated through comprehensive experiments.
Answers, not links: extracting tips from yahoo! answers to address how-to web queries
Authors: Ingmar Weber, Antti Ukkonen, Aristides Gionis
Venue: WSDM
Year: 2012

We investigate the problem of mining "tips" from Yahoo! Answers and displaying those tips in response to related web queries. Here, a "tip" is a short, concrete and self-contained bit of non-obvious advice such as "To zest a lime if you don't have a zester : use a cheese grater." First, we estimate the volume of web queries with "how-to" intent, which could be potentially addressed by a tip. Second, we analyze how to detect such queries automatically without solely relying on literal "how to *" patterns. Third, we describe how to derive potential tips automatically from Yahoo! Answers, and we develop machine-learning techniques to remove low-quality tips. Finally, we discuss how to match web queries with "how-to" intent to tips. We evaluate both the quality of these direct displays as well as the size of the query volume that can be addressed by serving tips.
Relational click prediction for sponsored search
Authors: Chenyan Xiong, Taifeng Wang, Wenkui Ding, Yidong Shen, Tie-Yan Liu
Venue: WSDM
Year: 2012

This paper is concerned with the prediction of clicking an ad in sponsored search. The accurate prediction of user's click on an ad plays an important role in sponsored search, because it is widely used in both ranking and pricing of the ads. Previous work on click prediction usually takes a single ad as input, and ignores its relationship to the other ads shown in the same page. This independence assumption here, however, might not be valid in the real scenario. In this paper, we first perform an analysis on this issue by looking at the click-through rates (CTR) of the same ad, in the same position and for the same query, but surrounded by different ads. We found that in most cases the CTR varies largely, which suggests that the relationship between ads is really an important factor in predicting click probability. Furthermore, our investigation shows that the more similar the surrounding ads are to an ad, the lower the CTR of the ad is. Based on this observation, we design a continuous conditional random fields (CRF) based model for click prediction, which considers both the features of an ad and its similarity to the surrounding ads. We show that the model can be effectively learned using maximum likelihood estimation, and can also be efficiently inferred due to its closed form solution. Our experimental results on the click-through log from a commercial search engine show that the proposed model can predict clicks more accurately than previous independent models. To our best knowledge this is the first work that predicts ad clicks by considering the relationship between ads.
Find me opinion sources in blogosphere: a unified framework for opinionated blog feed retrieval
Authors: Xueke Xu, Songbo Tan, Yue Liu, Xueqi Cheng, Zheng Lin, Jiafeng Guo
Venue: WSDM
Year: 2012

This paper aims to find blog feeds having a principal inclination towards making opinionated comments on the given topic, so that we can subscribe to them to track influential and interesting opinions in the blogosphere. One major challenge is assigning topic-related opinion scores to blog feeds, which is embodied in two aspects. Firstly, we should identify whether the blog feed has a principal on-topic opinionated inclination. This inclination should be collectively revealed by all posts of the feed. We should fully consider evidences from all the posts of the feed to identify salient information among many posts of the feed. Secondly, we should capture topic-related opinions in the blog feed while ignoring irrelevant opinions. In this paper, we propose a unified framework for opinionated blog feed retrieval, which combines topic relevance and opinion scores with a generative model. Furthermore, we propose a language modeling approach to estimating opinion scores that is seamlessly integrated into the framework, where two language models, Topic-specific Opinion Model (TOM) and Topic-biased Feed Model (TFM), work collectively to reflect whether the blog feed shows a principal on-topic opinionated inclination. To estimate TFM, we propose a topic-biased random walk to exploit both content and structural information to capture topic-biased salient information in the feed. As for TOM estimation, we propose to use a generative mixture model with prior guidance to effectively capture topic-specific opinion expressing language usage. The conducted experiments in the context of the TREC 2009-2010 Blog Track show the effectiveness of our proposed approaches.
Spatially-aware indexing for image object retrieval
Authors: Roelof van Zwol, Lluis Garcia Pueyo
Venue: WSDM
Year: 2012

The success of image object retrieval systems relies on the visual bag-of-words paradigm, which allows image retrieval systems to adopt a retrieval strategy analogous to text retrieval. In this paper we propose two spatially-aware retrieval strategies for image object retrieval that replaces the vector space model. The advantage of the proposed spatially-aware indexing and retrieval strategies are threefold: (1) It allows for the deployment of small visual vocabularies, (2) the number of images evaluated at retrieval time is significantly reduced, and (3) it eliminates the need for a post-retrieval phase, which is normally used to test the spatial composition of the visual words in the retrieved images.
Latent factor models with additive and hierarchically-smoothed user preferences
Authors: Amr Ahmed, Bhargav Kanagal, Sandeep Pandey, Vanja Josifovski, Lluis Garcia Pueyo, Jeffrey Yuan
Venue: WSDM
Year: 2013

Items in recommender systems are usually associated with annotated attributes: for e.g., brand and price for products; agency for news articles, etc. Such attributes are highly informative and must be exploited for accurate recommendation. While learning a user preference model over these attributes can result in an interpretable recommender system and can hands the cold start problem, it suffers from two major drawbacks: data sparsity and the inability to model random effects. On the other hand, latent-factor collaborative filtering models have shown great promise in recommender systems; however, its performance on rare items is poor. In this paper we propose a novel model LFUM, which provides the advantages of both of the above models. We learn user preferences (over the attributes) using a personalized Bayesian hierarchical model that uses a combination(additive model) of a globally learned preference model along with user-specific preferences. To combat data-sparsity, we smooth these preferences over the item-taxonomy using an efficient forward-filtering and backward-smoothing inference algorithm. Our inference algorithms can handle both discrete attributes (e.g., item brands) and continuous attributes (e.g., item prices). We combine the user preferences with the latent-factor models and train the resulting collaborative filtering system end-to-end using the successful BPR ranking algorithm. In our extensive experimental analysis, we show that our proposed model outperforms several commonly used baselines and we carry out an ablation study showing the benefits of each component of our model.
Anomaly, event, and fraud detection in large network datasets
Authors: Leman Akoglu, Christos Faloutsos
Venue: WSDM
Year: 2013

Detecting anomalies and events in data is a vital task, with numerous applications in security, finance, health care, law enforcement, and many others. While many techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured graph data have been of focus recently. As objects in graphs have long-range correlations, novel technology has been developed for abnormality detection in graph data.
Characterizing and curating conversation threads: expansion, focus, volume, re-entry
Authors: Lars Backstrom, Jon M. Kleinberg, Lillian Lee, Cristian Danescu-Niculescu-Mizil
Venue: WSDM
Year: 2013

Discussion threads form a central part of the experience on many Web sites, including social networking sites such as Facebook and Google Plus and knowledge creation sites such as Wikipedia. To help users manage the challenge of allocating their attention among the discussions that are relevant to them, there has been a growing need for the algorithmic curation of on-line conversations --- the development of automated methods to select a subset of discussions to present to a user. Here we consider two key sub-problems inherent in conversational curation: length prediction --- predicting the number of comments a discussion thread will receive --- and the novel task of re-entry prediction --- predicting whether a user who has participated in a thread will later contribute another comment to it. The first of these sub-problems arises in estimating how interesting a thread is, in the sense of generating a lot of conversation; the second can help determine whether users should be kept notified of the progress of a thread to which they have already contributed. We develop and evaluate a range of approaches for these tasks, based on an analysis of the network structure and arrival pattern among the participants, as well as a novel dichotomy in the structure of long threads. We find that for both tasks, learning-based approaches using these sources of information.
(big) usage data in web search
Authors: Ricardo Baeza-Yates, Yoelle Maarek
Venue: WSDM
Year: 2013

Web Search, which takes its root in the mature field of information retrieval, evolved tremendously over the last 15 years. The field encountered its first revolution when it started to deal with huge amounts of Web pages. Then, a major step was accomplished when engines started to consider the structure of the Web graph and leveraged link analysis in both crawling and ranking. Finally, a more discrete, but no less critical step, was made when search engines started to monitor and exploit the numerous (mostly implicit) signals provided by users while interacting with the search engine. In this tutorial we focus on this "revolution" of large scale usage data.
Group sparse topical coding: from code to topic
Authors: Lu Bai, Jiafeng Guo, Yanyan Lan, Xueqi Cheng
Venue: WSDM
Year: 2013

Learning low dimensional representations of text corpora is critical in many content analysis and data mining applications. It is even more desired and challenging to learn a sparse representation in practice for large scale text modeling. However, traditional probabilistic topic models (PTM) lack a mechanism to directly control the posterior sparsity of the inferred representations; While the emerged non-probabilistic models (NPM) can explicitly control sparsity using sparse constraint like l_1 norm, they convey different limitations in latent representations. To address the existing problems, we propose a novel non-probabilistic topic model for discovering sparse latent representations of large text corpora, referred as group sparse topical coding (GSTC). Our model enjoys both the merits of the PTMs and NPMs. On one hand, GSTC can naturally derive document-level admixture proportions in topic simplex like PTMs, which is useful for semantic analysis, classification or retrieval. On the other hand, GSTC can directly control the sparsity of the inferred representations with group lasso by relaxing the normalization constraint. Moreover, the relaxed non-probabilistic GSTC can be effectively learned using coordinate descent method. Experimental results on benchmark datasets show that GSTC can discover meaningful compact latent representations of documents, and improve the document classification accuracy and time efficiency.
3rd workshop on context-awareness in retrieval and recommendation
Authors: Matthias Böhmer, Ernesto William De Luca, Alan Said, Jaime Teevan
Venue: WSDM
Year: 2013

Context-aware information is widely available in various ways and is becoming more and more important for enhancing retrieval performance and recommendation results. The current main issue to cope with is not only recommending or retrieving the most relevant items and content, but defining them ad hoc. Other relevant issues include personalizing and adapting the information and the way it is displayed to the user's current situation and interests. Ubiquitous computing further provides new means for capturing user feedback on items and providing information.
From machu_picchu to "rafting the urubamba river": anticipating information needs via the entity-query graph
Authors: Ilaria Bordino, Gianmarco De Francisci Morales, Ingmar Weber, Francesco Bonchi
Venue: WSDM
Year: 2013

We study the problem of anticipating user search needs, based on their browsing activity. Given the current web page p that a user is visiting we want to recommend a small and diverse set of search queries that are relevant to the content of p, but also non-obvious and serendipitous.
Differences in search engine evaluations between query owners and non-owners
Authors: Alexandra Chouldechova, David Mease
Venue: WSDM
Year: 2013

The query-document relevance judgments used in web search engine evaluation are traditionally provided by human assessors who have no particular association with the specific queries selected for the evaluation. Most commonly, queries are randomly sampled from search logs and in turn randomly assigned to the human assessors. In this paper, we consider a very different approach in which we instead ask the human assessors to provide their own queries from their recent search experiences. Using these queries as our sample, we compare the relevance judgments from the "owners" of the queries to the relevance judgments of the non-owners.
Sponsored search auctions
Authors: Riccardo Colini-Baldeschi
Venue: WSDM
Year: 2013

Sponsored search auctions are used to allocate ad slots to advertisers. The standard mechanism for sponsored search auctions is the Generalized-Second-Price (GSP) auction.
Robust query rewriting using anchor data
Authors: Nick Craswell, Bodo Billerbeck, Dennis Fetterly, Marc Najork
Venue: WSDM
Year: 2013

Query rewriting algorithms can be used as a form of query expansion, by combining the user's original query with automatically generated rewrites. Rewriting algorithms bring linguistic datasets to bear without the need for iterative relevance feedback, but most studies of rewriting have used proprietary datasets such as large-scale search logs. By contrast this paper uses readily available data, particularly ClueWeb09 link text with over 1.2 billion anchor phrases, to generate rewrites. To avoid overfitting, our initial analysis is performed using Million Query Track queries, leading us to identify three algorithms which perform well. We then test the algorithms on Web and newswire data. Results show good properties in terms of robustness and early precision.
Optimizing top-k document retrieval strategies for block-max indexes
Authors: Constantinos Dimopoulos, Sergey Nepomnyachiy, Torsten Suel
Venue: WSDM
Year: 2013

Large web search engines use significant hardware and energy resources to process hundreds of millions of queries each day, and a lot of research has focused on how to improve query processing efficiency. One general class of optimizations called early termination techniques is used in all major engines, and essentially involves computing top results without an exhaustive traversal and scoring of all potentially relevant index entries. Recent work in [9,7] proposed several early termination algorithms for disjunctive top-k query processing, based on a new augmented index structure called Block-Max Index that enables aggressive skipping in the index.
Bursty subgraphs in social networks
Authors: Milad Eftekhar, Nick Koudas, Yashar Ganjali
Venue: WSDM
Year: 2013

Data available through social media and content sharing platforms present opportunities for analysis and mining. In the context of social networks, it is interesting to formalize and locate bursts of activities amongst users, related to a particular event and to report sets of socially connected users participating in such bursts. Such collections present new opportunities for understanding social events, and render new ways of online marketing.
Personalizing atypical web search sessions
Authors: Carsten Eickhoff, Kevyn Collins-Thompson, Paul N. Bennett, Susan T. Dumais
Venue: WSDM
Year: 2013

Most research in Web search personalization models users as static or slowly evolving entities with a given set of preferences defined by their past behavior. However, recent publications as well as empirical evidence suggest that for a significant number of search sessions, users diverge from their regular search profiles in order to satisfy atypical, limited-duration information needs. In this work, we conduct a large-scale inspection of real-life search sessions to further understand this scenario. Subsequently, we design an automatic means of detecting and supporting such atypical sessions. We demonstrate significant improvements over state-of-the-art Web search personalization techniques by accounting for the typicality of search sessions. The proposed method is evaluated based on Web-scale search session data spanning several months of user activity.
Econometric analysis and digital marketing: how tomeasure the effectiveness of an ad
Authors: Ayman Farahat, James Shanahan
Venue: WSDM
Year: 2013

Over the past 18 years online advertising has grown to a $70 billion industry worldwide annually. Despite this impressive growth, online advertising faces many (and some would say traditional) challenges including how to measure the efficiency or the potential loss of sales caused by the inefficient use of advertising dollars. Consequently, it is vital to measure, maximize, and benchmark the efficiency of advertising media expenditures. This tutorial introduces the field of econometrics as a means of measuring the effectiveness of digital marketing. Econometrics is a field that extends and applies statistical methods to the analysis of economic phenomena. In that vein, econometrics goes beyond traditional statistics and explicitly recognizes the complexities of human behavior. Consider for example the impact of deep discounts on survival of restaurants. Struggling businesses are more likely to offer these deep discounts and eventually fail. A naïve application of statistical techniques will overestimate the impact of deep discounts on business survival. In this case, the discounts are an endogenous variable as compared to an exogenous variable. This type of specification error highlights why we need a deeper look at the variables that go into statistical models. Econometrics addresses these and other issues in a formal and rigorous manner.
On the quest of discovering cultural trails in social media
Authors: Ruth Olimpia Garcia Gavilanes
Venue: WSDM
Year: 2013

With the constant increasing reach of the Web and in particular of Social Media, people create and share content that harbors information about habits, norms, preferences and values. Consequently, studying how culture influences users in online social media has increased the interest of several sectors such as the advertising industry, search engines and corporations. As a consequence, anthropological and computational models need to interact and complement each other to better target these new demands. Recently, several studies have analyzed culture from large-scale data but not many took into consideration the cultural models proposed by anthropological theory. By carrying out several experiments on large-scale data from the Web, we propose to combine theoretical concepts of culture with information technology techniques to process, analyze, model and interpret data from the Web. We plan to discover synergies between traditional social studies of culture and those derived from our experiments.
Advanced graph mining for community evaluation in social networks and the web
Authors: Christos Giatsidis, Fragkiskos D. Malliaros, Michalis Vazirgiannis
Venue: WSDM
Year: 2013

Graphs constitute a dominant data structure and appear essentially in all forms of information. Examples are the Web graph, numerous social networks, protein interaction networks, terms dependency graphs and network topologies. The main features of these graphs are their huge volume and rate of change. Presumably, there is important hidden knowledge in the macroscopic topology and features of these graphs. A cornerstone issue here is the detection and evaluation of communities -- bearing multiple and diverse semantics. The tutorial reports the basic models of graph structures for undirected, directed and signed graphs and their properties. Next we offer a thorough review of fundamental methods for graph clustering and community detection, on both undirected and directed graphs. Then we survey community evaluation measures, including both the individual node based ones as well as those that take into account aggregate properties of communities. A special mention is made on approaches that capitalize on the concept of degeneracy (k-cores and extensions), as a novel means of community detection and evaluation. We justify the above foundational framework with applications on citation graphs, trust networks and protein graphs.
Expediting search trend detection via prediction of query counts
Authors: Nadav Golbandi, Liran Katzir, Yehuda Koren, Ronny Lempel
Venue: WSDM
Year: 2013

The massive volume of queries submitted to major Web search engines reflects human interest at a global scale. While the popularity of many search queries is stable over time or fluctuates with periodic regularity, some queries experience a sudden and ephemeral rise in popularity that is unexplained by their past volumes. Typically the popularity surge is precipitated by some real-life event in the news cycle. Such queries form what are known as search trends. All major search engines, using query log analysis and other signals, invest in detecting such trends. The goal is to surface trends accurately, with low latency relative to the actual event that sparked the trend.