OpenRaiser/Intern-Atlas huggingface dataset:
Using k-Top retrieved web snippets to date temporalimplicit queries based on web content analysis
Authors: Ricardo Nuno Taborda Campos
Venue: SIGIR
Year: 2011

The World Wide Web (WWW) is a huge information network from which retrieving and organizing quality relevant content remains an open question for mostly all ambiguous queries. As an example, many queries have temporal implicit intents associated with them but they are not inferred by search engines. Inferring the user intentions and the period he has in mind, may therefore play an extremely important role in the improvement of the results. Our work goes in this direction. We aim to introduce a temporal analysis framework for analyzing documents in a temporal dimension in order to identify and understand the temporal nature of any given query, namely implicit ones. Our analysis is not based on metadata, but on the exploitation of temporal information from the content itself, particularly within web snippets, which are interesting pieces of concentrated information, where time clues, especially years, often appear. Our intention is to develop a language-independent solution and to model the degree of relationship between the terms and dates identified. This is the core part of the framework and the basis for both temporal query understanding and search results exploration, such as temporal clustering. We believe that inferring this knowledge is a very important step in the process of adding a temporal dimension to IR systems, thus disambiguating a large class of queries for which search engines continue to fail.
CrowdTracker: enabling community-based real-time web monitoring
Authors: James Caverlee, Zhiyuan Cheng, Brian Eoff, Chiao-Fang Hsu, Krishna Yeswanth Kamath, Jeffrey McGee
Venue: SIGIR
Year: 2011

CrowdTracker is a community-based web monitoring system optimized for real-time web streams like Twitter, Facebook, and Google Buzz. In this demo summary, we provide an overview of the system and architecture, and outline the demonstration plan.
Identifying similar people in professional social networks with discriminative probabilistic models
Authors: Suleyman Cetintas, Monica Rogati, Luo Si, Yi Fang
Venue: SIGIR
Year: 2011

Identifying similar professionals is an important task for many core services in professional social networks. Information about users can be obtained from heterogeneous information sources, and different sources provide different insights on user similarity.
Sensor-aided mobile information management and retrieval
Authors: Edward Y. Chang
Venue: SIGIR
Year: 2011

The number of "smart" mobile devices such as wireless phones and tablet computers has been rapidly growing. These mobile devices are equipped with a variety of sensors such as camera, gyroscope, accelerometer, compass, NFC, WiFi, GPS, etc. These sensors can be used to capture images and voice, detect motion patterns, and predict locations, to name just a few. This keynote depicts techniques in configuration, calibration, computation, and fusion for improving sensor performance and conserving power consumption. We also present novel mobile information management and retrieval applications that can benefit a great deal from enhanced sensor technologies.
Bag-of-visual-words vs global image descriptors on two-stage multimodal retrieval
Authors: Savvas A. Chatzichristofis, Konstantinos Zagoris, Avi Arampatzis
Venue: SIGIR
Year: 2011

The Bag-Of-Visual-Words (BOVW) paradigm is fast becoming a popular image representation for Content-Based Image Retrieval (CBIR), mainly because of its better retrieval effectiveness over global feature representations on collections with images being near-duplicate to queries. In this experimental study we demonstrate that this advantage of BOVW is diminished when visual diversity is enhanced by using a secondary modality, such as text, to pre-filter images.
The TREC files: the (ground) truth is out there
Authors: Savvas A. Chatzichristofis, Konstantinos Zagoris, Avi Arampatzis
Venue: SIGIR
Year: 2011

Traditional tools for information retrieval (IR) evaluation, such as TREC's trec_eval, have outdated command-line interfaces with many unused features, or 'switches', accumulated over the years. They are usually seen as cumbersome applications by new IR researchers, steepening the learning curve. We introduce a platform-independent application for IR evaluation with a graphical easy-to-use interface: the TREC_Files Evaluator. The application supports most of the standard measures used for evaluation in TREC, CLEF, and elsewhere, such as MAP, P10, P20, and bpref, as well as the Averaged Normalized Modified Retrieval Rank (ANMRR) proposed by MPEG for image retrieval evaluation. Additional features include a batch mode and statistical significance testing of the results against a pre-selected baseline.
UPS: efficient privacy protection in personalized web search
Authors: Gang Chen, He Bai, Lidan Shou, Ke Chen, Yunjun Gao
Venue: SIGIR
Year: 2011

In recent years, personalized web search (PWS) has demonstrated effectiveness in improving the quality of search service on the Internet. Unfortunately, the need for collecting private information in PWS has become a major barrier for its wide proliferation. We study privacy protection in PWS engines which capture personalities in user profiles. We propose a PWS framework called UPS that can generalize profiles in for each query according to user-specified privacy requirements. Two predictive metrics are proposed to evaluate the privacy breach risk and the query utility for hierarchical user profile. We develop two simple but effective generalization algorithms for user profiles allowing for query-level customization using our proposed metrics. We also provide an online prediction mechanism based on query utility for deciding whether to personalize a query in UPS. Extensive experiments demonstrate the efficiency and effectiveness of our framework.
Recommending ephemeral items at web scale
Authors: Ye Chen, John F. Canny
Venue: SIGIR
Year: 2011

We describe an innovative and scalable recommendation system successfully deployed at eBay. To build recommenders for long-tail marketplaces requires projection of volatile items into a persistent space of latent products. We first present a generative clustering model for collections of unstructured, heterogeneous, and ephemeral item data, under the assumption that items are generated from latent products. An item is represented as a vector of independently and distinctly distributed variables, while a latent product is characterized as a vector of probability distributions, respectively. The probability distributions are chosen as natural stochastic models for different types of data. The learning objective is to maximize the total intra-cluster coherence measured by the sum of log likelihoods of items under such a generative process. In the space of latent products, robust recommendations can then be derived using naive Bayes for ranking, from historical transactional data. Item-based recommendations are achieved by inferring latent products from unseen items. In particular, we develop a probabilistic scoring function of recommended items, which takes into account item-product membership, product purchase probability, and the important auction-end-time factor. With the holistic probabilistic measure of a prospective item purchase, one can further maximize the expected revenue and the more subjective user satisfaction as well. We evaluated the latent product clustering and recommendation ranking models using real-world e-commerce data from eBay, in both forms of offline simulation and online A/B testing. In the recent production launch, our system yielded 3-5 folds improvement over the existing production system in click-through, purchase-through and gross merchandising value; thus now driving 100% related recommendation traffic with billions of items at eBay. We believe that this work provides a practical yet principled framework for recommendation in the domains with affluent user self-input data.
iMecho: a context-aware desktop search system
Authors: Jidong Chen, Hang Guo, Wentao Wu, Wei Wang
Venue: SIGIR
Year: 2011

In this demo, we present iMecho, a context-aware desktop search system to help users get more relevant results. Different from other desktop search engines, iMecho ranks results not only by the content of the query, but also the context of the query. It employs an Hidden Markov Model (HMM)-based user model, which is learned from user's activity logs, to estimate the query context when he submits the query. The results from keyword search are re-ranked by their relevances to the context with acceptable overhead.
Diversifying product search results
Authors: Xiangru Chen, Haofen Wang, Xinruo Sun, Junfeng Pan, Yong Yu
Venue: SIGIR
Year: 2011

In recent years, online shopping is becoming more and more popular. Users type keyword queries on product search systems to find relevant products, accessories, and even related products. However, existing product search systems always return very similar products on the first several pages instead of taking diversity into consideration. In this paper, we propose a novel approach to address the diversity issue in the context of product search. We transform search result diversification into a combination of diversifying product categories and diversifying product attribute values within each category. The two sub-problems are optimization problems which can be reduced into well-known NP-hard problems respectively. We further leverage greedy-based approximation algorithms for efficient product search results re-ranking.
Knowledge effects on document selection in search results pages
Authors: Michael J. Cole, Xiangmin Zhang, Chang Liu, Nicholas J. Belkin, Jacek Gwizdka
Venue: SIGIR
Year: 2011

Click through events in search results pages (SERPs) are not reliable implicit indicators of document relevance. A user's task and domain knowledge are key factors in recognition and link selection and the most useful SERP document links may be those that best match the user's domain knowledge. User study participants rated their knowledge of genomics MeSH terms before conducting 2004 TREC Genomics Track tasks. Each participant's document knowledge was represented by their knowledge of the indexing MeSH terms. Results show high, intermediate, and low domain knowledge groups had similar document selection SERP rank distributions. SERP link selection distribution varied when participant knowledge of the available documents was analyzed. High domain knowledge participants usually selected a document with the highest personal knowledge rating. Low domain knowledge participants were reasonably successful at selecting available documents of which they had the most knowledge, while intermediate knowledge participants often failed to do so. This evidence for knowledge effects on SERP link selection may contribute to understanding the potential for personalization of search results ranking based on user domain knowledge.
Learning to rank for freshness and relevance
Authors: Na Dai, Milad Shokouhi, Brian D. Davison
Venue: SIGIR
Year: 2011

Freshness of results is important in modern web search. Failing to recognize the temporal aspect of a query can negatively affect the user experience, and make the search engine appear stale. While freshness and relevance can be closely related for some topics (e.g., news queries), they are more independent in others (e.g., time insensitive queries). Therefore, optimizing one criterion does not necessarily improve the other, and can even do harm in some cases.
Multi-objective optimization in learning to rank
Authors: Na Dai, Milad Shokouhi, Brian D. Davison
Venue: SIGIR
Year: 2011

Supervised learning to rank algorithms typically optimize for high relevance and ignore other facets of search quality, such as freshness and diversity. Prior work on multi-objective ranking trained rankers focused on using hybrid labels that combine overall quality of documents, and implicitly incorporate multiple criteria into quantifying ranking risks. However, these hybrid scores are usually generated based on heuristics without considering potential correlations between individual facets (e.g., freshness versus relevance). In this poster, we empirically demonstrate that the correlation between objective facets in multi-criteria ranking optimization may significantly influence the effectiveness of trained rankers with respect to each objective.
Tossing coins to trim long queries
Authors: Sudip Datta, Vasudeva Varma
Venue: SIGIR
Year: 2011

Verbose web queries are often descriptive in nature where a term based search engine is unable to distinguish between the essential and noisy words, which can result in a drift from the user intent. We present a randomized query reduction technique that builds on an earlier learning to rank based approach. The proposed technique randomly picks only a small set of samples, instead of the exponentially many sub-queries, thus being fast enough to be useful for web search engines, while still covering wide sub-query space.
Collective topic modeling for heterogeneous networks
Authors: Hongbo Deng, Bo Zhao, Jiawei Han
Venue: SIGIR
Year: 2011

In this paper, we propose a joint probabilistic topic model for simultaneously modeling the contents of multi-typed objects of a heterogeneous information network. The intuition behind our model is that different objects of the heterogeneous network share a common set of latent topics so as to adjust the multinomial distributions over topics for different objects collectively. Experimental results demonstrate the effectiveness of our approach for the tasks of topic modeling and object clustering.
Indexing strategies for graceful degradation of search quality
Authors: Shuai Ding, Sreenivas Gollapudi, Samuel Ieong, Krishnaram Kenthapadi, Alexandros Ntoulas
Venue: SIGIR
Year: 2011

Large web search engines process billions of queries each day over tens of billions of documents with often very stringent requirements for a user's search experience, in particular, low latency and highly relevant search results. Index generation and serving are key to satisfying both these requirements. For example, the load to search engines can vary drastically when popular events happen around the world. In the case when the load is exceeding what the search engine can serve, queries will get dropped. This results in an un- graceful degradation in search quality. Another example that could increase the query load and affect the user's search experience are ambiguous queries which often result in the execution of multiple query alterations in the back end.
Cognitive coordinating behaviors in multitasking web search
Authors: Jia Tina Du
Venue: SIGIR
Year: 2011

This paper investigates how users cognitively coordinate multitasking Web search across different information search problems. The analysis suggests that (1) multitasking is a prevalent Web search behavior including both sequential multitasking (31%) and parallel multitasking (69%); (2) multitasking is performed through a task switching process; and (3) such a process is supported and underpinned by cognitive coordination mechanisms and strategy coordination.
Decayed DivRank: capturing relevance, diversity and prestige in information networks
Authors: Pan Du, Jiafeng Guo, Xueqi Cheng
Venue: SIGIR
Year: 2011

Many network-based ranking approaches have been proposed to rank objects according to different criteria, including relevance, prestige and diversity. However, existing approaches either only aim at one or two of the criteria, or handle them with additional heuristics in multiple steps. Inspired by DivRank, we propose a unified ranking model, Decayed DivRank (DDRank), to meet the three criteria simultaneously. Empirical experiments on paper citation network show that DDRank can outperform existing algorithms in capturing relevance, diversity and prestige simultaneously in ranking.
Estimation methods for ranking recent information
Authors: Miles Efron, Gene Golovchinsky
Venue: SIGIR
Year: 2011

Temporal aspects of documents can impact relevance for certain kinds of queries. In this paper, we build on earlier work of modeling temporal information. We propose an extension to the Query Likelihood Model that incorporates query-specific information to estimate rate parameters, and we introduce a temporal factor into language model smoothing and query expansion using pseudo-relevance feedback. We evaluate these extensions using a Twitter corpus and two newspaper article collections. Results suggest that, compared to prior approaches, our models are more effective at capturing the temporal variability of relevance associated with some topics.
Understanding re-finding behavior in naturalistic email interaction logs
Authors: David Elsweiler, Morgan Harvey, Martin Hacker
Venue: SIGIR
Year: 2011

In this paper we present a longitudinal, naturalistic study of email behavior (n=47) and describe our efforts at isolating re-finding behavior in the logs through various qualitative and quantitative analyses. The presented work underlines the methodological challenges faced with this kind of research, but demonstrates that it is possible to isolate re-finding behavior from email interaction logs with reasonable accuracy. Using the approaches developed we uncover interesting aspects of email re-finding behavior that have so far been impossible to study, such as how various features of email-clients are used in re-finding and the difficulties people encounter when using these. We explain how our findings could influence the design of email-clients and outline our thoughts on how future, more in depth analyses, can build on the work presented here to achieve a fuller understanding of email behavior and the support that people need.
Seeding simulated queries with user-study data forpersonal search evaluation
Authors: David Elsweiler, David E. Losada, José Carlos Toucedo, Ronald T. Fernández
Venue: SIGIR
Year: 2011

In this paper we perform a lab-based user study (n=21) of email re-finding behaviour, examining how the characteristics of submitted queries change in different situations. A number of logistic regression models are developed on the query data to explore the relationship between user- and contextual- variables and query characteristics including length, field submitted to and use of named entities. We reveal several interesting trends and use the findings to seed a simulated evaluation of various retrieval models. Not only is this an enhancement of existing evaluation methods for Personal Search, but the results show that different models are more effective in different situations, which has implications both for the design of email search tools and for the way algorithms for Personal Search are evaluated.
CrowdLogging: distributed, private, and anonymous search logging
Authors: Henry Allen Feild, James Allan, Joshua Glatt
Venue: SIGIR
Year: 2011

We describe CrowdLogging, an approach for distributed search log collection, storage, and mining, with the dual goals of preserving privacy and making the mined information broadly available. Most search log mining approaches and most privacy enhancing schemes have focused on centralized search logs and methods for disseminating them to third parties. In our approach, a user's search log is encrypted and shared in such a way that (a) the source of a search behavior artifact, such as a query, is unknown and (b) extremely rare artifacts---that is, artifacts more likely to contain private information---are not revealed. The approach works with any search behavior artifact that can be extracted from a search log, including queries, query reformulations, and query-click pairs. In this work, we: (1) present a distributed search log collection, storage, and mining framework; (2) compare several privacy policies, including differential privacy, showing the trade-offs between strong guarantees and the utility of the released data; (3) demonstrate the impact of our approach using two existing research query logs; and (4) describe a pilot study for which we implemented a version of the framework.
A site oriented method for segmenting web pages
Authors: David Fernandes de Oliveira, Edleno Silva de Moura, Altigran Soares da Silva, Berthier A. Ribeiro-Neto, Edisson Braga Araújo
Venue: SIGIR
Year: 2011

Information about how to segment a Web page can be used nowadays by applications such as segment aware Web search, classification and link analysis. In this research, we propose a fully automatic method for page segmentation and evaluate its application through experiments with four separate Web sites. While the method may be used in other applications, our main focus in this article is to use it as input to segment aware Web search systems. Our results indicate that the proposed method produces better segmentation results when compared to the best segmentation method we found in literature. Further, when applied as input to a segment aware Web search method, it produces results close to those produced when using a manual page segmentation method.
Improved video categorization from text metadata and user comments
Authors: Katja Filippova, Keith B. Hall
Venue: SIGIR
Year: 2011

We consider the task of assigning categories (e.g., howto/cooking, sports/basketball, pet/dogs) to YouTube videos from video and text signals. We show that two complementary views on the data -- from the video and text perspectives -- complement each other and refine predictions. The contributions of the paper are threefold: (1) we show that a text-based classifier trained on imperfect predictions of the weakly supervised video content-based classifier is not redundant; (2) we demonstrate that a simple model which combines the predictions made by the two classifiers outperforms each of them taken independently; (3) we analyse such sources of text information as video title, description, user tags and viewers' comments and show that each of them provides valuable clues to the topic of the video.
Learning features through feedback for blog distillation
Authors: Dehong Gao, Renxian Zhang, Wenjie Li, Raymond Yiu-Keung Lau, Kam-Fai Wong
Venue: SIGIR
Year: 2011

The paper is focused on blogosphere research based on the TREC blog distillation task, and aims to explore unbiased and significant features automatically and efficiently. Feedback from faceted feeds is introduced to harvest relevant features and information gain is used to select discriminative features. The evaluation result shows that the selected feedback features can greatly improve the performance and adapt well to the terabyte data.
Statistical feature extraction for cross-language web content quality assessment
Authors: Guanggang Geng, Xiaodong Li, Li-Ming Wang, Wei Wang, Shuo Shen
Venue: SIGIR
Year: 2011

Web content quality assessment is a typical static ranking problem. Heuristic content and TFIDF features based statistical systems have proven effective for Web content quality assessment. But they are all language dependent features, which are not suitable for cross-language ranking. In this paper, we fuse a series of language-independent features including hostname features, domain registration features, two-layer hyperlink analysis features and third-party Web service features to assess the Web content quality. The experiments on ECML/PKDD 2010 Discovery Challenge cross-language datasets show that the assessment is effective.