OpenRaiser/Intern-Atlas huggingface dataset:
Aggregating multiple opinion evidence in proximity-based opinion retrieval
Authors: Shima Gerani, Mostafa Keikha, Fabio Crestani
Venue: SIGIR
Year: 2011

Blog post opinion retrieval is the problem of ranking blog posts according to the likelihood that the post is relevant to the query and that the author was expressing an opinion about the topic (of the query). A recent study has proposed a method for finding the opinion density at query term positions in a document which uses the proximity of query term and opinion term as an indicator of their relatedness. The maximum opinion density between different query positions was used as an opinion score of the whole document. In this paper we investigate the effect of exploiting multiple opinion evidence of a document. We propose using the ordered weighted averaging (OWA) operator in order to combine the opinion score of different query positions for a final score of a document, in the proximity-based opinion retrieval system.
What are the real differences of children's and adults' web search
Authors: Tatiana Gossen, Thomas Low, Andreas Nürnberger
Venue: SIGIR
Year: 2011

We present first results of a logfile analysis on web search engines for children. The aim of this research is to analyse fundamental facts about how children's web search behaviour differs from that of adults. We show differences to previous results, which are often based on small lab experiments. Our large-scale analysis suggests that children search queries are more information-oriented and shorter on average. Children indeed make a lot of spelling errors and often repeat searches and revisit web pages.
Why searchers switch: understanding and predicting engine switching rationales
Authors: Qi Guo, Ryen W. White, Yunqiao Zhang, Blake Anderson, Susan T. Dumais
Venue: SIGIR
Year: 2011

Search engine switching is the voluntary transition between Web search engines. Engine switching can occur for a number of reasons, including user dissatisfaction with search results, a desire for broader topic coverage or verification, user preferences, or even unintentionally. An improved understanding of switching rationales allows search providers to tailor the search experience according to the different causes. In this paper we study the reasons behind search engine switching within a session. We address the challenge of identifying switching rationales by designing and implementing client-side instrumentation to acquire in-situ feedbacks from users. Using this feedback, we investigate in detail the reasons that users switch engines within a session. We also study the relationship between implicit behavioral signals and the switching causes, and develop and evaluate models to predict the reasons for switching. In addition, we collect editorial judgments of switching rationales by third-party judges and show that we can recover switching causes a posteriori. Our findings provide valuable insights into why users switch search engines in a session and demonstrate the relationship between search behavior and switching motivations. The findings also reveal sufficient behavioral consistency to afford accurate prediction of switching rationale, which can be used to dynamically adapt the search experience and derive more accurate competitive metrics.
Detecting success in mobile search from interaction
Authors: Qi Guo, Shuai Yuan, Eugene Agichtein
Venue: SIGIR
Year: 2011

Predicting searcher success and satisfaction is a key problem in Web search, which is essential for automatic evaluating and improving search engine performance. This problem has been studied actively in the desktop search setting, but not specifically for mobile search, despite many known differences between the two modalities. As mobile devices become increasingly popular for searching the Web, improving the searcher experience on such devices is becoming crucially important. In this paper, we explore the possibility of predicting searcher success and satisfaction in mobile search with a smart phone. Specifically, we investigate client-side interaction signals, including the number of browsed pages, and touch screen-specific actions such as zooming and sliding. Exploiting this information with machine learning techniques results in nearly 80% accuracy for predicting searcher success -- significantly outperforming the previous models.
Enhanced results for web search
Authors: Kevin Haas, Peter Mika, Paul Tarjan, Roi Blanco
Venue: SIGIR
Year: 2011

"Ten blue links" have defined web search results for the last fifteen years -- snippets of text combined with document titles and URLs. In this paper, we establish the notion of enhanced search results that extend web search results to include multimedia objects such as images and video, intent-specific key value pairs, and elements that allow the user to interact with the contents of a web page directly from the search results page. We show that users express a preference for enhanced results both explicitly, and when observed in their search behavior. We also demonstrate the effectiveness of enhanced results in helping users to assess the relevance of search results. Lastly, we show that we can efficiently generate enhanced results to cover a significant fraction of search result pages.
A weighted curve fitting method for result merging in federated search
Authors: Chuan He, Dzung Hong, Luo Si
Venue: SIGIR
Year: 2011

Result merging is an important step in federated search to merge the documents returned from multiple source-specific ranked lists for a user query. Previous result merging methods such as Semi-Supervised Learning (SSL) and Sample- Agglomerate Fitting Estimate (SAFE) use regression methods to estimate global document scores from document ranks in individual ranked lists. SSL relies on overlapping documents that exist in both individual ranked lists and a centralized sample database. SAFE goes a step further by using both overlapping documents with accurate rank information and documents with estimated rank information for regression. However, existing methods do not distinguish the accurate rank information from the estimated information. Furthermore, all documents are assigned equal weights in regression while intuitively, documents in the top should carry higher weights. This paper proposes a weighted curve fitting method for result merging in federated search. The new method explicitly models the importance of information from overlapping documents over non-overlapping ones. It also weights documents at different positions differently. Empirically results on two datasets clearly demonstrate the advantage of the proposed algorithm.
Faster temporal range queries over versioned text
Authors: Jinru He, Torsten Suel
Venue: SIGIR
Year: 2011

Versioned textual collections are collections that retain multiple versions of a document as it evolves over time. Important large-scale examples are Wikipedia and the web collection of the Internet Archive. Search queries over such collections often use keywords as well as temporal constraints, most commonly a time range of interest. In this paper, we study how to support such temporal range queries over versioned text. Our goal is to process these queries faster than the corresponding keyword-only queries, by exploiting the additional constraint. A simple approach might partition the index into different time ranges, and then access only the relevant parts. However, specialized inverted index compression techniques are crucial for large versioned collections, and a naive partitioning can negatively affect index size and query throughput. We show how to achieve high query throughput by using smart index partitioning techniques that take index compression into account. Experiments on over 85 million versions of Wikipedia articles show that queries can be executed in a few milliseconds on memory-based index structures, and only slightly more time on disk-based structures. We also show how to efficiently support the recently proposed stable top-k search primitive on top of our schemes.
Search engines that learn online
Authors: Katja Hofmann
Venue: SIGIR
Year: 2011

The goal of my research is to develop self-learning search engines, that can learn online, i.e., directly from interactions with actual users. Such systems can continuously adapt to user preferences throughout their lifetime, leading to better search performance in settings where expensive manual tuning is infeasible. Challenges that are addressed in my work include the development of effective online learning to rank algorithms for IR, user aspects, and evaluation.
Sample selection for dictionary-based corpus compression
Authors: Christopher Hoobin, Simon J. Puglisi, Justin Zobel
Venue: SIGIR
Year: 2011

Compression of large text corpora has the potential to drastically reduce both storage requirements and per-document access costs. Adaptive methods used for general-purpose compression are ineffective for this application, and historically the most successful methods have been based on word-based dictionaries, which allow use of global properties of the text. However, these are dependent on the text complying with assumptions about content and lead to dictionaries of unpredictable size. In recent work we have described an LZ-like approach in which sampled blocks of a corpus are used as a dictionary against which the complete corpus is compressed, giving compression twice as effective than that of zlib. Here we explore how pre-processing can be used to eliminate redundancy in our sampled dictionary. Our experiments show that dictionary size can be reduced by 50% or more (less than 0.1% of the collection size) with no significant effect on compression or access speed.
Region-based landmark discovery by crowdsourcing geo-referenced photos
Authors: Yen-Ta Huang, An-Jung Cheng, Liang-Chi Hsieh, Winston H. Hsu, Kuo-Wei Chang
Venue: SIGIR
Year: 2011

We propose a novel model for landmark discovery that locates region-based landmarks on map in contrast to the traditional point-based landmarks. The proposed method preserves more information and automatically identifies candidate regions on map by crowdsourcing geo-referenced photos. Gaussian kernel convolution is applied to remove noises and generate detected region. We adopt F1 measure to evaluate discovered landmarks and manually check the association between tags and regions. The experiment results show that more than 90% of attractions in the selected city can be correctly located by this method.
The Meta-Dex Suite: generating and analyzing indexes and meta-indexes
Authors: Michael Huggett, Edie Rasmussen
Venue: SIGIR
Year: 2011

Our Meta-dex software suite extracts content and index text from a corpus of PDF files, and generates a meta-index that references entries across an entire domain. We provide tools to analyze the individual and integrated indexes, and visualize entries and books within the meta-index. The suite is scalable to very large data sets.
Enhancing mobile search using web search log data
Authors: Yoshiyuki Inagaki, Jiang Bian, Yi Chang, Motoko Maki
Venue: SIGIR
Year: 2011

Mobile search is still in infancy compared with general purpose web search. With limited training data and weak relevance features, the ranking performance in mobile search is far from satisfactory. To address this problem, we propose to leverage the knowledge of Web search to enhance the ranking of mobile search. In this paper, we first develop an equivalent page conversion between web search and mobile search, then we design a few novel ranking features, generated from the click-through data in web search, for estimating the relevance of mobile search. Large scale evaluations demonstrate that the knowledge from web search is quite effective for boosting the relevance of ranking on mobile search.
Synthesizing high utility suggestions for rare web search queries
Authors: Alpa Jain, Umut Ozertem, Emre Velipasaoglu
Venue: SIGIR
Year: 2011

Search engines are continuously looking into methods to alleviate users' effort in finding desired information. For this, all major search engines employ query suggestions methods to facilitate effective query formulation and reformulation. Providing high quality query suggestions is a critical task for search engines and so far most research efforts have focused on tapping various information available in search query logs to identify potential suggestions. By relying on this single source of information, suggestion providing systems often restrict themselves to only previously observed query sessions. Therefore, a critical challenge faced by query suggestions provision mechanism is that of coverage, i.e., the number of unique queries for which users are provided with suggestions, while keeping the suggestion quality high. To address this problem, we propose a novel way of generating suggestions for user search queries by moving beyond the dependency on search query logs and providing synthetic suggestions for web search queries. The key challenges in providing synthetic suggestions include identifying important concepts in a query and systematically exploring related concepts while ensuring that the resulting suggestions are relevant to the user query and of high utility. We present an end-to-end system to generate synthetic suggestions that builds upon novel query-level operations and combines information available from various textual sources. We evaluate our suggestion system over a large-scale real-world dataset of query logs and show that our methods increase the coverage of query-suggestion pairs by up to 39% without compromising the quality or the utility of the suggestions.
Scalable multi-dimensional user intent identification using tree structured distributions
Authors: Vinay Jethava, Liliana Calderón-Benavides, Ricardo Baeza-Yates, Chiranjib Bhattacharyya, Devdatt P. Dubhashi
Venue: SIGIR
Year: 2011

The problem of identifying user intent has received considerable attention in recent years, particularly in the context of improving the search experience via query contextualization. Intent can be characterized by multiple dimensions, which are often not observed from query words alone. Accurate identification of Intent from query words remains a challenging problem primarily because it is extremely difficult to discover these dimensions. The problem is often significantly compounded due to lack of representative training sample. We present a generic, extensible framework for learning the multi-dimensional representation of user intent from the query words. The approach models the latent relationships between facets using tree structured distribution which leads to an efficient and convergent algorithm, FastQ, for identifying the multi-faceted intent of users based on just the query words. We also incorporated WordNet to extend the system capabilities to queries which contain words that do not appear in the training data. Empirical results show that FastQ yields accurate identification of intent when compared to a gold standard.
Learning search tasks in queries and web pages via graph regularization
Authors: Ming Ji, Jun Yan, Siyu Gu, Jiawei Han, Xiaofei He, Wei Vivian Zhang, Zheng Chen
Venue: SIGIR
Year: 2011

As the Internet grows explosively, search engines play a more and more important role for users in effectively accessing online information. Recently, it has been recognized that a query is often triggered by a search task that the user wants to accomplish. Similarly, many web pages are specifically designed to help accomplish a certain task. Therefore, learning hidden tasks behind queries and web pages can help search engines return the most useful web pages to users by task matching. For instance, the search task that triggers query "thinkpad T410 broken" is to maintain a computer, and it is desirable for a search engine to return the Lenovo troubleshooting page on the top of the list. However, existing search engine technologies mainly focus on topic detection or relevance ranking, which are not able to predict the task that triggers a query and the task a web page can accomplish. In this paper, we propose to simultaneously classify queries and web pages into the popular search tasks by exploiting their content together with click-through logs. Specifically, we construct a taskoriented heterogeneous graph among queries and web pages. Each pair of objects in the graph are linked together as long as they potentially share similar search tasks. A novel graph-based regularization algorithm is designed for search task prediction by leveraging the graph. Extensive experiments in real search log data demonstrate the effectiveness of our method over state-of-the-art classifiers, and the search performance can be significantly improved by using the task prediction results as additional information.
Query expansion based on a semantic graph model
Authors: Xue Jiang
Venue: SIGIR
Year: 2011

Query expansion is a classical topic in the field of information retrieval, which is proposed to bridge the gap between searchers' information intents and their queries. Previous researches usually expand queries based on document collections, or some external resources such as WordNet and Wikipedia [1, 2, 3, 4, 5]. However, it seems that independently using one of these resources has some defects, document collections lack semantic information of words, while WordNet and Wikipedia may not include domain-specific knowledge in certain document collection. Our work aims to combine these two kinds of resources to establish an expansion model which represents not only domain-specific information but also semantic information. In our preliminary experiments, we construct a two-layer word graph and use Random-Walk algorithm to calculate the weights of each term in pseudo-relevance feedback documents, then select the highest weighted term to expand original query. The first layer of the word graph contains terms in related documents, while the second layer contains semantic senses corresponding to these terms. These terms and semantic senses are treated as vertices of the graph and connected with each other by all possible relationships, such as mutual information and semantic similarities. We utilized mutual information, semantic similarity and uniform distribution as the weight of term-term relation, sense-sense relation and word-sense relation respectively. Though these experiments show that our expansion outperform original queries, we are troubled with some difficult problems.
Enhancing web search by mining search and browse logs
Authors: Daxin Jiang, Jian Pei, Hang Li
Venue: SIGIR
Year: 2011

Huge amounts of search log data have been accumulated in various search engines. Currently, a commercial search engine receives billions of queries and collects tera-bytes of log data on any single day. Other than search log data, browse logs can be collected by client-side browser plug-ins, which record the browse information if users' permissions are granted. Such massive amounts of search/browse log data, on the one hand, provide great opportunities to mine the wisdom of crowds and improve web search results. On the other hand, designing effective and efficient methods to clean, model, and process large scale log data also presents great challenges. In this tutorial, we will focus on mining search and browse log data for search engines. We will start with an introduction of search and browse log data and an overview of frequently-used data summarization in log mining. We will then elaborate how log mining applications enhance the five major components of a search engine, namely, query understanding, document understanding, query-document matching, user understanding, and monitoring and feedbacks. For each aspect, we will survey the major tasks, fundamental principles, and state-of-the-art methods. Finally, we will discuss the challenges and future trends of log data mining. The goal of this tutorial is to provide a systematic survey on large-scale search/browse log mining to the IR community. It may help IR researchers to get familiar with the core challenges and promising directions in log mining. At the same time, this tutorial may also serve the developers of web information retrieval systems as a comprehensive and in-depth reference to the advanced log mining techniques.
Bootstrapping subjectivity detection
Authors: Valentin Jijkoun, Maarten de Rijke
Venue: SIGIR
Year: 2011

We describe a method for automatically generating subjectivity clues for a specific topic and a set of (relevant) document, evaluating it on the task of classifying sentences w.r.t. subjectivity, with improvements over previous work.
Integrating hierarchical feature selection and classifier training for multi-label image annotation
Authors: Cheng Jin, Chunlei Yang
Venue: SIGIR
Year: 2011

It is well accepted that using high-dimensional multi-modal visual features for image content representation and classifier training may achieve more sufficient characterization of the diverse visual properties of the images and further result in higher discrimination power of the classifiers. However, training the classifiers in a high-dimensional multi-modal feature space requires a large number of labeled training images, which will further result in the problem of curse of dimensionality. To tackle this problem, a hierarchical feature subset selection algorithm is proposed to enable more accurate image classification, where the processes for feature selection and classifier training are seamlessly integrated in a single framework. First, a feature hierarchy (i.e., concept tree for automatic feature space partition and organization) is used to automatically partition high-dimensional heterogeneous multi-modal visual features into multiple low-dimensional homogeneous single-modal feature subsets according to their certain physical meanings and each of them is used to characterize one certain type of the diverse visual properties of the images. Second, principal component analysis (PCA) is performed on each homogeneous singlemodal feature subset to select the most representative feature dimensions and a weak classifier is learned simultaneously. After the weak classifiers and their representative feature dimensions are available for all these homogeneous single-modal feature subsets, they are combined to generate an ensemble image classifier and achieve hierarchical feature subset selection. Our experiments on a specific domain of natural images have also obtained very positive results.
Ranking related news predictions
Authors: Nattiya Kanhabua, Roi Blanco, Michael Matthews
Venue: SIGIR
Year: 2011

We estimate that nearly one third of news articles contain references to future events. While this information can prove crucial to understanding news stories and how events will develop for a given topic, there is currently no easy way to access this information. We propose a new task to address the problem of retrieving and ranking sentences that contain mentions to future events, which we call ranking related news predictions. In this paper, we formally define this task and propose a learning to rank approach based on 4 classes of features: term similarity, entity-based similarity, topic similarity, and temporal similarity. Through extensive evaluations using a corpus consisting of 1.8 millions news articles and 6,000 manually judged relevance pairs, we show that our approach is able to retrieve a significant number of relevant predictions related to a given topic.
Time-based query performance predictors
Authors: Nattiya Kanhabua, Kjetil Nørvåg
Venue: SIGIR
Year: 2011

Query performance prediction is aimed at predicting the retrieval effectiveness that a query will achieve with respect to a particular ranking model. In this paper, we study query performance prediction for a ranking model that explicitly incorporates the time dimension into ranking. Different time-based predictors are proposed as analogous to existing keyword-based predictors. In order to improve predicting performance, we combine different predictors using linear regression and neural networks. Extensive experiments are conducted using queries and relevance judgments obtained by crowdsourcing.