OpenRaiser/Intern-Atlas huggingface dataset:
Freshness matters: in flowers, food, and web authority
Authors: Na Dai, Brian D. Davison
Venue: SIGIR
Year: 2010

The collective contributions of billions of users across the globe each day result in an ever-changing web. In verticals like news and real-time search, recency is an obvious significant factor for ranking. However, traditional link-based web ranking algorithms typically run on a single web snapshot without concern for user activities associated with the dynamics of web pages and links. Therefore, a stale page popular many years ago may still achieve a high authority score due to its accumulated in-links. To remedy this situation, we propose a temporal web link-based ranking scheme, which incorporates features from historical author activities. We quantify web page freshness over time from page and in-link activity, and design a web surfer model that incorporates web freshness, based on a temporal web graph composed of multiple web snapshots at different time points. It includes authority propagation among snapshots, enabling link structures at distinct time points to influence each other when estimating web page authority. Experiments on a real-world archival web corpus show our approach improves upon PageRank in both relevance and freshness of the search results.
Towards subjectifying text clustering
Authors: Sajib Dasgupta, Vincent Ng
Venue: SIGIR
Year: 2010

Although it is common practice to produce only a single clustering of a dataset, in many cases text documents can be clustered along different dimensions. Unfortunately, not only do traditional text clustering algorithms fail to produce multiple clusterings of a dataset, the only clustering they produce may not be the one that the user desires. In this paper, we propose a simple active clustering algorithm that is capable of producing multiple clusterings of the same data according to user interest. In comparison to previous work on feedback-oriented clustering, the amount of user feedback required by our algorithm is minimal. In fact, the feedback turns out to be as simple as a cursory look at a list of words. Experimental results are very promising: our system is able to generate clusterings along the user-specified dimensions with reasonable accuracies on several challenging text classification tasks, thus providing suggestive evidence that our approach is viable.
DivQ: diversification for keyword search over structured databases
Authors: Elena Demidova, Peter Fankhauser, Xuan Zhou, Wolfgang Nejdl
Venue: SIGIR
Year: 2010

Keyword queries over structured databases are notoriously ambiguous. No single interpretation of a keyword query can satisfy all users, and multiple interpretations may yield overlapping results. This paper proposes a scheme to balance the relevance and novelty of keyword search results over structured databases. Firstly, we present a probabilistic model which effectively ranks the possible interpretations of a keyword query over structured data. Then, we introduce a scheme to diversify the search results by re-ranking query interpretations, taking into account redundancy of query results. Finally, we propose α-nDCG-W and WS-recall, an adaptation of α-nDCG and S-recall metrics, taking into account graded relevance of subtopics. Our evaluation on two real-world datasets demonstrates that search results obtained using the proposed diversification algorithms better characterize possible answers available in the database than the results of the initial relevance ranking.
Relevance and ranking in online dating systems
Authors: Fernando Diaz, Donald Metzler, Sihem Amer-Yahia
Venue: SIGIR
Year: 2010

Match-making systems refer to systems where users want to meet other individuals to satisfy some underlying need. Examples of match-making systems include dating services, resume/job bulletin boards, community based question answering, and consumer-to-consumer marketplaces. One fundamental component of a match-making system is the retrieval and ranking of candidate matches for a given user. We present the first in-depth study of information retrieval approaches applied to match-making systems. Specifically, we focus on retrieval for a dating service. This domain offers several unique problems not found in traditional information retrieval tasks. These include two-sided relevance, very subjective relevance, extremely few relevant matches, and structured queries. We propose a machine learned ranking function that makes use of features extracted from the uniquely rich user profiles that consist of both structured and unstructured attributes. An extensive evaluation carried out using data gathered from a real online dating service shows the benefits of our proposed methodology with respect to traditional match-making baseline systems. Our analysis also provides deep insights into the aspects of match-making that are particularly important for producing highly relevant matches.
Using search session context for named entity recognition in query
Authors: Junwu Du, Zhimin Zhang, Jun Yan, Yan Cui, Zheng Chen
Venue: SIGIR
Year: 2010

Recently, the problem of Named Entity Recognition in Query (NERQ) is attracting increasingly attention in the field of information retrieval. However, the lack of context information in short queries makes some classical named entity recognition (NER) algorithms fail. In this paper, we propose to utilize the search session information before a query as its context to address this limitation. We propose to improve two classical NER solutions by utilizing the search session context, which are known as Conditional Random Field (CRF) based solution and Topic Model based solution respectively. In both approaches, the relationship between current focused query and previous queries in the same session are used to extract novel context aware features. Experimental results on real user search session data show that the NERQ algorithms using search session context performs significantly better than the algorithms using only information of the short queries.
A user behavior model for average precision and its generalization to graded judgments
Authors: Georges Dupret, Benjamin Piwowarski
Venue: SIGIR
Year: 2010

We explore a set of hypothesis on user behavior that are potentially at the origin of the (Mean) Average Precision (AP) metric. This allows us to propose a more realistic version of AP where users click non-deterministically on relevant documents and where the number of relevant documents in the collection needs not be known in advance. We then depart from the assumption that a document is either relevant or irrelevant and we use instead relevance judgment similar to editorial labels used for Discounted Cumulated Gain (DCG). We assume that clicked documents provide users with a certain level of "utility" and that a user ends a search when she gathered enough utility. Based on the query logs of a commercial search engine we show how to evaluate the utility associated with a label from the record of past user interactions with the search engine and we show how the two different user models can be evaluated based on their ability to predict accurately future clicks. Finally, based on these user models, we propose a measure that captures the relative quality of two rankings.
Finding and filtering information for children
Authors: Desmond Elliott, Richard Glassey, Tamara Polajnar, Leif Azzopardi
Venue: SIGIR
Year: 2010

Children face several challenges when using information access systems. These include formulating queries, judging the relevance of documents, and focusing attention on interface cues, such as query suggestions, while typing queries. It has also been shown that children want a personalised Web experience and prefer content presented to them that matches their long-term entertainment and education needs. To this end, we have developed an interaction-based information filtering system to address these challenges.
Entity information management in complex networks
Authors: Yi Fang
Venue: SIGIR
Year: 2010

Entity information management (EIM) deals with organizing, processing and delivering information about entities. Its emergence is a result of satisfying more sophisticated information needs that go beyond document search. In the recent years, entity retrieval has attracted much attention in the IR community. INEX has started the XML Entity Ranking track since 2007 and TREC has launched the Entity track since 2009 to investigate the problem of related entity finding. Some EIM problems go beyond retrieval and ranking such as: 1) entity profiling, which is about characterizing a specific entity, and 2) entity distillation, which is about discovering the trend about an entity. These problems have received less attention while they have many important applications.
Discriminative models of integrating document evidence and document-candidate associations for expert search
Authors: Yi Fang, Luo Si, Aditya P. Mathur
Venue: SIGIR
Year: 2010

Generative models such as statistical language modeling have been widely studied in the task of expert search to model the relationship between experts and their expertise indi-cated in supporting documents. On the other hand, dis-criminative models have received little attention in expert search research, although they have been shown to outper-form generative models in many other information retrieval and machine learning applications. In this paper, we propose a principled relevance-based discriminative learning frame-work for expert search and derive specific discriminative models from the framework. Compared with the state-of-the-art language models for expert search, the proposed re-search can naturally integrate various document evidence and document-candidate associations into a single model without extra modeling assumptions or effort. An extensive set of experiments have been conducted on two TREC En-terprise track corpora (i.e., W3C and CERC) to demonstrate the effectiveness and robustness of the proposed framework.
Multi-modal query expansion for web video search
Authors: Bailan Feng, Juan Cao, Zhineng Chen, Yongdong Zhang, Shouxun Lin
Venue: SIGIR
Year: 2010

Query expansion is an effective method to improve the usability of multimedia search. Most existing multimedia search engines are able to automatically expand a list of textual query terms based on text search techniques, which can be called textual query expansion (TQE). However, the annotations (title and tag) around web videos are generally noisier for text-only query expansion and search matching. In this paper, we propose a novel multi-modal query expansion (MMQE) framework for web video search to solve the issue. Compared with traditional methods, MMQE provides a more intuitive query suggestion by transforming tex-tual query to visual presentation based on visual clustering. Paral-lel to this, MMQE can enhance the process of search matching with strong pertinence of intent-specific query by joining textual, visual and social cues from both metadata and content of videos. Experimental results on real web videos from YouTube demon-strate the effectiveness of the proposed method.
Mining adjacent markets from a large-scale ads video collection for image advertising
Authors: Guwen Feng, Xin-Jing Wang, Lei Zhang, Wei-Ying Ma
Venue: SIGIR
Year: 2010

The research on image advertising is still in its infancy. Most previous approaches suggest ads by directly matching an ad to a query image, which lacks the power to identify ads from adjacent market. In this paper, we tackle the problem by mining knowledge on adjacent markets from ads videos with a novel Multi-Modal Dirichlet Process Mixture Sets model, which is a unified model of (video frames) clustering and (ads) ranking. Our approach is not only capable of discovering relevant ads (e.g. car ads for a query car image), but also suggesting ads from adjacent markets (e.g. tyre ads). Experimental results show that our proposed approach is fairly effective.
Where to start filtering redundancy?: a cluster-based approach
Authors: Ronald T. Fernández, Javier Parapar, David E. Losada, Alvaro Barreiro
Venue: SIGIR
Year: 2010

Novelty detection is a difficult task, particularly at sentence level. Most of the approaches proposed in the past consist of re-ordering all sentences following their novelty scores. However, this re-ordering has usually little value. In fact, a naive baseline with no novelty detection capabilities yields often better performance than any state-of-the-art novelty detection mechanism. We argue here that this is because current methods initiate too early the novelty detection process. When few sentences have been seen, it is unlikely that the user is negatively affected by redundancy. Therefore, re-ordering the first sentences may be harmful in terms of performance. We propose here a query-dependent method based on cluster analysis to determine where we must start filtering redundancy.
Transitive history-based query disambiguation for query reformulation
Authors: Karim Filali, Anish Nair, Chris Leggetter
Venue: SIGIR
Year: 2010

We present a probabilistic model of a user's search history and a target query reformulation. We derive a simple transitive similarity algorithm for disambiguating queries and improving history-based query reformulation accuracy. We compare the merits of this approach to other methods and present results on both examples assessed by human editors and on automatically-labeled click data.
Refactoring the search problem
Authors: Gary William Flake
Venue: SIGIR
Year: 2010

The most common way of framing the search problem is as an exchange between a user and a database, where the user issues queries and the database replies with results that satisfy constraints imposed by the query but that also optimize some notion of relevance. There are several variations to this basic model that augment the dialogue between humans and machines through query refinement, relevance feedback, and other mechanism. However, rarely is this problem ever posed in a way in which the properties of the client and server are fundamentally different and in a way in which exploiting the differences can be used to yield substantially different experiences.
Learning to rank only using training data from related domain
Authors: Wei Gao, Peng Cai, Kam-Fai Wong, Aoying Zhou
Venue: SIGIR
Year: 2010

Like traditional supervised and semi-supervised algorithms, learning to rank for information retrieval requires document annotations provided by domain experts. It is costly to annotate training data for different search domains and tasks. We propose to exploit training data annotated for a related domain to learn to rank retrieved documents in the target domain, in which no labeled data is available. We present a simple yet effective approach based on instance-weighting scheme. Our method first estimates the importance of each related-domain document relative to the target domain. Then heuristics are studied to transform the importance of individual documents to the pairwise weights of document pairs, which can be directly incorporated into the popular ranking algorithms. Due to importance weighting, ranking model trained on related domain is highly adaptable to the data of target domain. Ranking adaptation experiments on LETOR3.0 dataset [27] demonstrate that with a fair amount of related-domain training data, our method significantly outperforms the baseline without weighting, and most of time is not significantly worse than an "ideal" model directly trained on target domain.
Proximity-based opinion retrieval
Authors: Shima Gerani, Mark James Carman, Fabio Crestani
Venue: SIGIR
Year: 2010

Blog post opinion retrieval aims at finding blog posts that are relevant and opinionated about a user's query. In this paper we propose a simple probabilistic model for assigning relevant opinion scores to documents. The key problem is how to capture opinion expressions in the document, that are related to the query topic. Current solutions enrich general opinion lexicons by finding query-specific opinion lexicons using pseudo-relevance feedback on external corpora or the collection itself. In this paper we use a general opinion lexicon and propose using proximity information in order to capture opinion term relatedness to the query. We propose a proximity-based opinion propagation method to calculate the opinion density at each point in a document. The opinion density at the position of a query term in the document can then be considered as the probability of opinion about the query term at that position. The effect of different kernels for capturing the proximity is also discussed. Experimental results on the BLOG06 dataset show that the proposed method provides significant improvement over standard TREC baselines and achieves a 2.5% increase in MAP over the best performing run in the TREC 2008 blog track.
Context aware query classification using dynamic query window and relationship net
Authors: Nazli Goharian, Saket S. R. Mengle
Venue: SIGIR
Year: 2010

The context of the user queries, preceding a given query, is utilized to improve the effectiveness of query classification. Earlier efforts utilize fixed number of preceding queries to derive such context information. We propose and evaluate an approach (DQW) that identifies a set of unambiguous preceding queries in a dynamically determined window to utilize in classifying an ambiguous query. Furthermore, utilizing a relationship-net (R-net) that represents relationships among known categories, we improve the classification effectiveness for those ambiguous queries whose predicted category in this relationship-net is related to the category of a query within the window. Our results indicate that the hybrid approach (DQW+R-net) statistically significantly improves the Conditional Random Field (CRF) query classification approach when static query windowing and hierarchical taxonomy are used (SQW+Tax), in terms of precision (10.8%), recall (13.2%), and F1 measure (11.9%).
A picture is worth a thousand search results: finding child-oriented multimedia results with collAge
Authors: Karl Gyllstrom, Marie-Francine Moens
Venue: SIGIR
Year: 2010

We present a simple and effective approach to complement search results for children’s web queries with child-oriented multimedia results, such as coloring pages and music sheets. Our approach determines appropriate media types for a query by searching Google’s database of frequent queries for co-occurrences of a query’s terms (e.g., “dinosaurs”) with preselected multimedia terms (e.g., “coloring pages”). We show the effectiveness of this approach through an online user evaluation.
Retrieval system evaluation: automatic evaluation versus incomplete judgments
Authors: Claudia Hauff, Franciska de Jong
Venue: SIGIR
Year: 2010

In information retrieval (IR), research aiming to reduce the cost of retrieval system evaluations has been conducted along two lines: (i) the evaluation of IR systems with reduced amounts of manual relevance assessments, and (ii) the fully automatic evaluation of IR systems, thus foregoing the need for manual assessments altogether. The proposed methods in both areas are commonly evaluated by comparing their performance estimates for a set of systems to a ground truth (provided for instance by evaluating the set of systems according to mean average precision). In contrast, in this poster we compare an automatic system evaluation approach directly to two evaluations based on incomplete manual relevance assessments. For the particular case of TREC's Million Query track, we show that the automatic evaluation leads to results which are highly correlated to those achieved by approaches relying on incomplete manual judgments.
A ranking approach to target detection for automatic link generation
Authors: Jiyin He, Maarten de Rijke
Venue: SIGIR
Year: 2010

We focus on the task of target detection in automatic link generation with Wikipedia, i.e., given an N-gram in a snippet of text, find the relevant Wikipedia concepts that explain or provide background knowledge for it. We formulate the task as a ranking problem and investigate the effectiveness of learning to rank approaches and of the features that we use to rank the target concepts for a given N-gram. Our experiments show that learning to rank approaches outperform traditional binary classification approaches. Also, our proposed features are effective both in binary classification and learning to rank settings.
Clicked phrase document expansion for sponsored search ad retrieval
Authors: Dustin Hillard, Chris Leggetter
Venue: SIGIR
Year: 2010

We present a document expansion approach that uses Conditional Random Field (CRF) segmentation to automatically extract salient phrases from ad titles. We then supplement the ad document with query segments that are probable translations of the document phrases, as learned from a large commercial search engine's click logs. Our approach provides a significant improvement in DCG and interpolated precision and recall on a large set of human labeled query-ad pairs.
Comparing click-through data to purchase decisions for retrieval evaluation
Authors: Katja Hofmann, Bouke Huurnink, Marc Bron, Maarten de Rijke
Venue: SIGIR
Year: 2010

Traditional retrieval evaluation uses explicit relevance judgments which are expensive to collect. Relevance assessments inferred from implicit feedback such as click-through data can be collected inexpensively, but may be less reliable. We compare assessments derived from click-through data to another source of implicit feedback that we assume to be highly indicative of relevance: purchase decisions. Evaluating retrieval runs based on a log of an audio-visual archive, we find agreement between system rankings and purchase decisions to be surprisingly high.
Medical search and classification tools for recommendation
Authors: Xiangji Huang, Aijun An, Qinmin Hu
Venue: SIGIR
Year: 2010

their patients' records from paper to computer, enormous amounts of electronic medical records (EMR) have become available for medical research. Some of the EMR data are well-structured, for which traditional database management systems can provide effective retrieval and management functions. However, most of the EMR data (such as progress notes and consultation letters) are in free text formats. How to effectively and efficiently retrieve and discover useful information from the vast amount of such semi-structured data is a challenge faced by medical professionals. Without proper tools, the rich information and knowledge buried in the medical health records are unavailable for clinical research and decision-making. The objective of our research is to develop text analytics tools that are capable of parsing clinical medical data so that predefined search subjects that correspond to a list of medical diagnoses can be extracted. In addition to this particular core functionality, it is also desired that several important assets should be present within the text-analytics tools in order to improve its overall ability to be used as recommendation tools. In this research, we work with research scientists at the Institute for Clinical Evaluative Sciences (ICES) in Toronto and examine a number of techniques for structuring and processing free text documents in order to effectively and efficiently search and analyze vast amount of medical records. We implement several powerful medical text analytics tools for clinical data searching and classification. For data classification, our tools sort through a great amount of patientrecords to identify the likelihood of a patient having myocardial infarction (MI) or hypertension (HTN), and classify the patients accordingly. Our tools can also identify the likelihood of a patient being a smoker, previous smoker or non-smoker based on the text data of medical records.
A stack decoder approach to approximate string matching
Authors: Juan M. Huerta
Venue: SIGIR
Year: 2010

We present a new efficient algorithm for top-N match retrieval of sequential patterns. Our approach is based on an incremental approximation of the string edit distance using index information and a stack based search. Our approach produces hypotheses with average edit error of about 0.29 edits from the optimal SED result while using only about 5% of the CPU computation.