OpenRaiser/Intern-Atlas huggingface dataset:
No free lunch: brute force vs. locality-sensitive hashing for cross-lingual pairwise similarity
Authors: Ferhan Türe, Tamer Elsayed, Jimmy Lin
Venue: SIGIR
Year: 2011

This work explores the problem of cross-lingual pairwise similarity, where the task is to extract similar pairs of documents across two different languages. Solutions to this problem are of general interest for text mining in the multilingual context and have specific applications in statistical machine translation. Our approach takes advantage of cross-language information retrieval (CLIR) techniques to project feature vectors from one language into another, and then uses locality-sensitive hashing (LSH) to extract similar pairs. We show that effective cross-lingual pairwise similarity requires working with similarity thresholds that are much lower than in typical monolingual applications, making the problem quite challenging. We present a parallel, scalable MapReduce implementation of the sort-based sliding window algorithm, which is compared to a brute-force approach on German and English Wikipedia collections. Our central finding can be summarized as "no free lunch": there is no single optimal solution. Instead, we characterize effectiveness-efficiency tradeoffs in the solution space, which can guide the developer to locate a desirable operating point based on application- and resource-specific constraints.
Persistence in the ephemeral: utilizing repeat behaviors for multi-session personalized search
Authors: Sarah K. Tyler
Venue: SIGIR
Year: 2011

As the abundance of information on the Internet grows, an increasing burden is placed on the user to specify his or her query precisely in order to avoid extraneous results that may be relevant, but not useful. At the same time, users have a tendency to repeat their search behaviors, seeking the same URL (re-finding) as well as issuing the same query (re-searching). These repeated actions reveal a form of user preference that the search engine can utilize to personalize the results.
Elsevier SIGIR 2011 application challenge abstract
Authors: Jukka Välimäki, Remko Caprio
Venue: SIGIR
Year: 2011

Elsevier SIGIR 2011 Application Challenge is an international competition that encourages software developers to create applications that run on Elsevier's SciVerse platform. The Challenge is open to all SIGIR 2011 Conference participants.
On diversifying and personalizing web search
Authors: David Vallet, Pablo Castells
Venue: SIGIR
Year: 2011

Diversification and personalization methods are common ap-proaches to deal with the one-size-fits-all paradigm of Web search engines. We performed a user study with 190 subjects where we analyzed the effects of diversification and personalization methods in a Web search engine. The obtained results suggest that our proposed combination of diversification and personalization factors may be a way to overcome the notion of intrusiveness in personalized approaches.
Intent-oriented diversity in recommender systems
Authors: Saul Vargas, Pablo Castells, David Vallet
Venue: SIGIR
Year: 2011

Diversity as a relevant dimension of retrieval quality is receiving increasing attention in the Information Retrieval and Recommender Systems (RS) fields. The problem has nonetheless been approached under different views and formulations in IR and RS respectively, giving rise to different models, methodologies, and metrics, with little convergence between both fields. In this poster we explore the adaptation of diversity metrics, techniques, and principles from ad-hoc IR to the recommendation task, by introducing the notion of user profile aspect as an analogue of query intent. As a particular approach, user aspects are automatically extracted from latent item features. Empirical results support the proposed approach and provide further insights.
Temporal latent semantic analysis for collaboratively generated content: preliminary results
Authors: Yu Wang, Eugene Agichtein
Venue: SIGIR
Year: 2011

Latent semantic analysis (LSA) has been intensively studied because of its wide application to Information Retrieval and Natural Language Processing. Yet, traditional models such as LSA only examine one (current) version of the document. However, due to the recent proliferation of collaboratively generated content such as threads in online forums, Collaborative Question Answering archives, Wikipedia, and other versioned content, the document generation process is now directly observable. In this study, we explore how this additional temporal information about the document evolution could be used to enhance the identification of latent document topics. Specifically, we propose a novel hidden-topic modeling algorithm, temporal Latent Semantic Analysis (tLSA), which elegantly extends LSA to modeling document revision history using tensor decomposition. Our experiments show that tLSA outperforms LSA on word relatedness estimation using benchmark data, and explore applications of tLSA for other tasks.
Parallel learning to rank for information retrieval
Authors: Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady Wirawan Lauw
Venue: SIGIR
Year: 2011

Learning to rank represents a category of effective ranking methods for information retrieval. While the primary concern of existing research has been accuracy, learning efficiency is becoming an important issue due to the unprecedented availability of large-scale training data and the need for continuous update of ranking functions. In this paper, we investigate parallel learning to rank, targeting simultaneous improvement in accuracy and efficiency.
Mining weakly labeled web facial images for search-based face annotation
Authors: Dayong Wang, Steven C. H. Hoi, Ying He
Venue: SIGIR
Year: 2011

This paper investigates a framework of search-based face annotation (SBFA) by mining weakly labeled facial images that are freely available on the World Wide Web (WWW). One challenging problem for search-based face annotation scheme is how to effectively perform annotation by exploiting the list of most similar facial images and their weak labels that are often noisy and incomplete. To tackle this problem, we propose an effective unsupervised label refinement (ULR) approach for refining the labels of web facial images using machine learning techniques. We formulate the learning problem as a convex optimization and develop effective optimization algorithms to solve the large-scale learning task efficiently. To further speed up the proposed scheme, we also propose a clustering-based approximation algorithm which can improve the scalability considerably. We have conducted an extensive set of empirical studies on a large-scale web facial image testbed, in which encouraging results showed that the proposed ULR algorithms can significantly boost the performance of the promising SBFA scheme.
Review of MSR-Bing web scale speller challenge
Authors: Kuansan Wang, Jan O. Pedersen
Venue: SIGIR
Year: 2011

In this paper, we provide an overview of the MSR-Bing Web Scale Speller Challenge of 2011. We describe the motivation and outline the algorithmic and engineering challenges posed by this activity. The design and the evaluation methods are also reviewed, and the online resources that will remain publicly available to the community are also described. The Challenge will culminate in a workshop after the time of the writing where the top prize winners will publish their approaches. The main findings and the lessons learned will be summarized and shared in the Industry Track presentation accompanying this paper.
Learning relevance from heterogeneous social network and its application in online targeting
Authors: Chi Wang, Rajat Raina, David Fong, Ding Zhou, Jiawei Han, Greg J. Badros
Venue: SIGIR
Year: 2011

The rise of social networking services in recent years presents new research challenges for matching users with interesting content. While the content-rich nature of these social networks offers many cues on "interests" of a user such as text in user-generated content, the links in the network, and user demographic information, there is a lack of successful methods for combining such heterogeneous data to model interest and relevance. This paper proposes a new method for modeling user interest from heterogeneous data sources with distinct but unknown importance. The model leverages links in the social graph by integrating the conceptual representation of a user's linked objects. The proposed method seeks a scalable relevance model of user interest, that can be discriminatively optimized for various relevance-centric problems, such as Internet advertisement selection, recommendation, and web search personalization. We apply our algorithm to the task of selecting relevant ads for users on Facebook's social network. We demonstrate that our algorithm can be scaled to work with historical data for all users, and learns interesting associations between concept classes automatically. We also show that using the learnt user model to predict the relevance of an ad is the single most important signal in our ranking system for new ads (with no historical clickthrough data), and overall leads to an improvement in the accuracy of the clickthrough rate prediction, a key problem in online advertising.
Words-of-interest selection based on temporal motion coherence for video retrieval
Authors: Lei Wang, Dawei Song, Eyad Elyan
Venue: SIGIR
Year: 2011

The "Bag of Visual Words" (BoW) framework has been widely used in query-by-example video retrieval to model the visual content by a set of quantized local feature descriptors. In this paper, we propose a novel technique to enhance BoW by the selection of Word-of-Interest (WoI) that utilizes the quantified temporal motion coherence of the visual words between the adjacent frames in the query example. Experiments carried out using TRECVID datasets show that our technique improves the retrieval performance of the classical BoW-based approach.
Learning online discussion structures by conditional random fields
Authors: Hongning Wang, Chi Wang, ChengXiang Zhai, Jiawei Han
Venue: SIGIR
Year: 2011

Online forum discussions are emerging as valuable information repository, where knowledge is accumulated by the interaction among users, leading to multiple threads with structures. Such replying structure in each thread conveys important information about the discussion content. Unfortunately, not all the online forum sites would explicitly record such replying relationship, making it hard to for both users and computers to digest the information buried in a thread discussion.
People searching for people: analysis of a people search engine log
Authors: Wouter Weerkamp, Richard Berendsen, Bogomil Kovachev, Edgar Meij, Krisztian Balog, Maarten de Rijke
Venue: SIGIR
Year: 2011

Recent years show an increasing interest in vertical search: searching within a particular type of information. Understanding what people search for in these "verticals" gives direction to research and provides pointers for the search engines themselves. In this paper we analyze the search logs of one particular vertical: people search engines. Based on an extensive analysis of the logs of a search engine geared towards finding people, we propose a classification scheme for people search at three levels: (a) queries, (b) sessions, and (c) users. For queries, we identify three types, (i) event-based high-profile queries (people that become "popular" because of an event happening), (ii) regular high-profile queries (celebrities), and (iii) low-profile queries (other, less-known people). We present experiments on automatic classification of queries. On the session level, we observe five types: (i) family sessions (users looking for relatives), (ii) event sessions (querying the main players of an event), (iii) spotting sessions (trying to "spot" different celebrities online), (iv) polymerous sessions (sessions without a clear relation between queries), and (v) repetitive sessions (query refinement and copying). Finally, for users we identify four types: (i) monitors, (ii) spotters, (iii) followers, and (iv) polymers.
Exploring term temporality for pseudo-relevance feedback
Authors: Stewart Whiting, Yashar Moshfeghi, Joemon M. Jose
Venue: SIGIR
Year: 2011

As digital collections expand, the importance of the temporal aspect of information has become increasingly apparent. The aim of this paper is to investigate the effect of using long-term temporal profiles of terms in information retrieval by enhancing the term selection process of pseudo-relevance feedback (PRF). For this purpose, two temporal PRF approaches were introduced considering only temporal aspect and temporal along with textual aspect. Experiments used the AP88-89 and WSJ87-92 test collections with TREC Ad-Hoc Topics 51-100. Term temporal profiles are extracted from the Google Books n-grams dataset. The results show that the long-term temporal aspects of terms are capable of enhancing retrieval effectiveness.
Rating-based collaborative filtering combined with additional regularization
Authors: Shu Wu, Shengrui Wang
Venue: SIGIR
Year: 2011

The collaborative filtering (CF) approach to recommender system has received much attention recently. However, previous work mainly focuses on improving the formula of rating prediction, e.g. by adding user and item biases, implicit feedback and time-aware factors, etc, to reach a better prediction by minimizing an objective function. However, little effort has been made on improving CF by incorporating additional regularization to the objective function. Regularization can further bound the searching range of predicted ratings. In this paper, we improve the conventional rating-based objective function by using ranking constraints as the supplementary regularization to restrict the searching of predicted ratings in smaller and more likely ranges, and develop a novel method, called RankSVD++, based on the SVD++ model. Experimental results show that RankSVD++ achieves better performance than existing main-streaming methods due to the addition of informative ranking-based regularization. The idea proposed here can also be easily incorporated to the other CF models.
Learning to rank using query-level regression
Authors: Jiajin Wu, Zhihao Yang, Yuan Lin, Hongfei Lin, Zheng Ye, Kan Xu
Venue: SIGIR
Year: 2011

In this paper, we use query-level regression as the loss function. The regression loss function has been used in pointwise methods, however pointwise methods ignore the query boundaries and treat the data equally across queries, and thus the effectiveness is limited. We show that regression is an effective loss function for learning to rank when used in query-level. We use neural network to model the ranking function and gradient descent for optimization and refer our method as ListReg. Experimental results show that ListReg significantly outperforms pointwise Regression and the state-of-the-art listwise method in most cases.
Efficient manifold ranking for image retrieval
Authors: Bin Xu, Jiajun Bu, Chun Chen, Deng Cai, Xiaofei He, Wei Liu, Jiebo Luo
Venue: SIGIR
Year: 2011

Manifold Ranking (MR), a graph-based ranking algorithm, has been widely applied in information retrieval and shown to have excellent performance and feasibility on a variety of data types. Particularly, it has been successfully applied to content-based image retrieval, because of its outstanding ability to discover underlying geometrical structure of the given image database. However, manifold ranking is computationally very expensive, both in graph construction and ranking computation stages, which significantly limits its applicability to very large data sets. In this paper, we extend the original manifold ranking algorithm and propose a new framework named Efficient Manifold Ranking (EMR). We aim to address the shortcomings of MR from two perspectives: scalable graph construction and efficient computation. Specifically, we build an anchor graph on the data set instead of the traditional k-nearest neighbor graph, and design a new form of adjacency matrix utilized to speed up the ranking computation. The experimental results on a real world image database demonstrate the effectiveness and efficiency of our proposed method. With a comparable performance to the original manifold ranking, our method significantly reduces the computational time, makes it a promising method to large scale real world retrieval problems.
Modeling subset distributions for verbose queries
Authors: Xiaobing Xue, W. Bruce Croft
Venue: SIGIR
Year: 2011

Improving verbose (or long) queries poses a new challenge for search systems. Previous techniques mainly focused on two aspects, weighting the important words or phrases and selecting the best subset query. The former does not consider how words and phrases are used in actual subset queries, while the latter ignores alternative subset queries. Recently, a novel reformulation framework has been proposed to transform the original query as a distribution of reformulated queries, which overcomes the disadvantages of previous techniques. In this paper, we apply this framework to verbose queries, where a reformulated query is specified as a subset query. Experiments on TREC collections show that the query distribution based framework outperforms the state-of-the-art techniques.
Social context summarization
Authors: Zi Yang, Keke Cai, Jie Tang, Li Zhang, Zhong Su, Juanzi Li
Venue: SIGIR
Year: 2011

We study a novel problem of social context summarization for Web documents. Traditional summarization research has focused on extracting informative sentences from standard documents. With the rapid growth of online social networks, abundant user generated content (e.g., comments) associated with the standard documents is available. Which parts in a document are social users really caring about? How can we generate summaries for standard documents by considering both the informativeness of sentences and interests of social users? This paper explores such an approach by modeling Web documents and social contexts into a unified framework. We propose a dual wing factor graph (DWFG) model, which utilizes the mutual reinforcement between Web documents and their associated social contexts to generate summaries. An efficient algorithm is designed to learn the proposed factor graph model.Experimental results on a Twitter data set validate the effectiveness of the proposed model. By leveraging the social context information, our approach obtains significant improvement (averagely +5.0%-17.3%) over several alternative methods (CRF, SVM, LR, PR, and DocLead) on the performance of summarization.
Collaborative competitive filtering: learning recommender using context of user choice
Authors: Shuang-Hong Yang, Bo Long, Alexander J. Smola, Hongyuan Zha, Zhaohui Zheng
Venue: SIGIR
Year: 2011

While a user's preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learning recommender models. In particular, existing collaborative filtering (CF) approaches take into account only the binary events of user actions but totally disregard the contexts in which users' decisions are made. In this paper, we propose Collaborative Competitive Filtering (CCF), a framework for learning user preferences by modeling the choice process in recommender systems. CCF employs a multiplicative latent factor model to characterize the dyadic utility function. But unlike CF, CCF models the user behavior of choices by encoding a local competition effect. In this way, CCF allows us to leverage dyadic data that was previously lumped together with missing data in existing CF models. We present two formulations and an efficient large scale optimization algorithm. Experiments on three real-world recommendation data sets demonstrate that CCF significantly outperforms standard CF approaches in both offline and online evaluations.
Award prediction with temporal citation network analysis
Authors: Zaihan Yang, Dawei Yin, Brian D. Davison
Venue: SIGIR
Year: 2011

Each year many ACM SIG communities will recognize an outstanding researcher through an award in honor of his or her profound impact and numerous research contributions. This work is the first to investigate an automated mechanism to help in selecting future award winners. We approach the problem as a researchers' expertise ranking problem, and propose a temporal probabilistic ranking model which combines content with citation network analysis. Experimental results based on real-world citation data and historical awardees indicate that some kinds of SIG awards are well-modeled by this approach.
On theme location discovery for travelogue services
Authors: Mao Ye, Rong Xiao, Wang-Chien Lee, Xing Xie
Venue: SIGIR
Year: 2011

In this paper, we aim to develop a travelogue service that discovers and conveys various travelogue digests, in form of theme locations, geographical scope, traveling trajectory and location snippet, to users. In this service, theme locations in a travelogue are the core information to discover. Thus we aim to address the problem of theme location discovery to enable the above travelogue services. Due to the inherent ambiguity of location relevance, we perform location relevance mining (LRM) in two complementary angles, relevance classification and relevance ranking, to provide comprehensive understanding of locations. Furthermore, we explore the textual (e.g., surrounding words) and geographical (e.g., geographical relationship among locations) features of locations to develop a co-training model for enhancement of classification performance. Built upon the mining result of LRM, we develop a series of techniques for provisioning of the aforementioned travelogue digests in our travelogue system. Finally, we conduct comprehensive experiments on collected travelogues to evaluate the performance of our location relevance mining techniques and demonstrate the effectiveness of the travelogue service.
Exploiting geographical influence for collaborative point-of-interest recommendation
Authors: Mao Ye, Peifeng Yin, Wang-Chien Lee, Dik Lun Lee
Venue: SIGIR
Year: 2011

In this paper, we aim to provide a point-of-interests (POI) recommendation service for the rapid growing location-based social networks (LBSNs), e.g., Foursquare, Whrrl, etc. Our idea is to explore user preference, social influence and geographical influence for POI recommendations. In addition to deriving user preference based on user-based collaborative filtering and exploring social influence from friends, we put a special emphasis on geographical influence due to the spatial clustering phenomenon exhibited in user check-in activities of LBSNs. We argue that the geographical influence among POIs plays an important role in user check-in behaviors and model it by power law distribution. Accordingly, we develop a collaborative recommendation algorithm based on geographical influence based on naive Bayesian. Furthermore, we propose a unified POI recommendation framework, which fuses user preference to a POI with social influence and geographical influence. Finally, we conduct a comprehensive performance evaluation over two large-scale datasets collected from Foursquare and Whrrl. Experimental results with these real datasets show that the unified collaborative recommendation approach significantly outperforms a wide spectrum of alternative recommendation approaches.
Link formation analysis in microblogs
Authors: Dawei Yin, Liangjie Hong, Xiong Xiong, Brian D. Davison
Venue: SIGIR
Year: 2011

Unlike a traditional social network service, a microblogging network like Twitter is a hybrid network, combining aspects of both social networks and information networks. Understanding the structure of such hybrid networks and to predict new links are important for many tasks such as friend recommendation, community detection, and network growth models. In this paper, by analyzing data collected over time, we find that 90% of new links are to people just two hops away and dynamics of friend acquisition are also related to users' account age. Finally, we compare two popular sampling methods which are widely used for network analysis and find that ForestFire does not preserve properties required for the link prediction task.