OpenRaiser/Intern-Atlas huggingface dataset:
Informational friend recommendation in social media
Authors: Shengxian Wan, Yanyan Lan, Jiafeng Guo, Chaosheng Fan, Xueqi Cheng
Venue: SIGIR
Year: 2013

It is well recognized that users rely on social media (e.g. Twitter or Digg) to fulfill two common needs (i.e. social need and informational need) that is to keep in touch with their friends in the real world and to have access to information they are interested in. Traditional friend recommendation methods in social media mainly focus on a user's social need, but seldom address their informational need (i.e. suggesting friends that can provide information one may be interested in but have not been able to obtain so far). In this paper, we propose to recommend friends according to the informational utility, which stands for the degree to which a friend satisfies the target user's unfulfilled informational need, called informational friend recommendation. In order to capture users' informational need, we view a post in social media as an item and utilize collaborative filtering techniques to predict the rating for each post. The candidate friends are then ranked according to their informational utility for recommendation. In addition, we also show how to further consider diversity in such recommendations. Experiments on benchmark datasets demonstrate that our approach can significantly outperform the traditional friend recommendation methods under informational evaluation measures.
Personalized ranking model adaptation for web search
Authors: Hongning Wang, Xiaodong He, Ming-Wei Chang, Yang Song, Ryen W. White, Wei Chu
Venue: SIGIR
Year: 2013

Search engines train and apply a single ranking model across all users, but searchers' information needs are diverse and cover a broad range of topics. Hence, a single user-independent ranking model is insufficient to satisfy different users' result preferences. Conventional personalization methods learn separate models of user interests and use those to re-rank the results from the generic model. Those methods require significant user history information to learn user preferences, have low coverage in the case of memory-based methods that learn direct associations between query-URL pairs, and have limited opportunity to markedly affect the ranking given that they only re-order top-ranked items.
Learning to name faces: a multimodal learning scheme for search-based face annotation
Authors: Dayong Wang, Steven C. H. Hoi, Pengcheng Wu, Jianke Zhu, Ying He, Chunyan Miao
Venue: SIGIR
Year: 2013

Automated face annotation aims to automatically detect human faces from a photo and further name the faces with the corresponding human names. In this paper, we tackle this open problem by investigating a search-based face annotation (SBFA) paradigm for mining large amounts of web facial images freely available on the WWW. Given a query facial image for annotation, the idea of SBFA is to first search for top-n similar facial images from a web facial image database and then exploit these top-ranked similar facial images and their weak labels for naming the query facial image. To fully mine those information, this paper proposes a novel framework of Learning to Name Faces (L2NF) -- a unified multimodal learning approach for search-based face annotation, which consists of the following major components: (i) we enhance the weak labels of top-ranked similar images by exploiting the "label smoothness" assumption; (ii) we construct the multimodal representations of a facial image by extracting different types of features; (iii) we optimize the distance measure for each type of features using distance metric learning techniques; and finally (iv) we learn the optimal combination of multiple modalities for annotation through a learning to rank scheme. We conduct a set of extensive empirical studies on two real-world facial image databases, in which encouraging results show that the proposed algorithms significantly boost the naming accuracy of search-based face annotation task.
The impact of solid state drive on search engine cache management
Authors: Jianguo Wang, Eric Lo, Man Lung Yiu, Jiancong Tong, Gang Wang, Xiaoguang Liu
Venue: SIGIR
Year: 2013

Caching is an important optimization in search engine architectures. Existing caching techniques for search engine optimization are mostly biased towards the reduction of random accesses to disks, because random accesses are known to be much more expensive than sequential accesses in traditional magnetic hard disk drive (HDD). Recently, solid state drive (SSD) has emerged as a new kind of secondary storage medium, and some search engines like Baidu have already used SSD to completely replace HDD in their infrastructure. One notable property of SSD is that its random access latency is comparable to its sequential access latency. Therefore, the use of SSDs to replace HDDs in a search engine infrastructure may void the cache management of existing search engines. In this paper, we carry out a series of empirical experiments to study the impact of SSD on search engine cache management. The results give insights to practitioners and researchers on how to adapt the infrastructure and how to redesign the caching policies for SSD-based search engines.
ProductSeeker: entity-based product retrieval for e-commerce
Authors: Hongzhi Wang, Xiaodong Zhang, Jianzhong Li, Hong Gao
Venue: SIGIR
Year: 2013

The retrieval results of online products information in e-commerce web sites are often difficult for users to use because of different descriptions for the same product. This paper proposes ProductSeeker, a product retrieval system organizing results according to their referring real-world entities for the conveniences of users. In the demonstration, we will present our system providing friendly interface to retrieve fresh product information and refining results according to feedback.
Sequential testing in classifier evaluation yields biased estimates of effectiveness
Authors: William Webber, Mossaab Bagdouri, David D. Lewis, Douglas W. Oard
Venue: SIGIR
Year: 2013

It is common to develop and validate classifiers through a process of repeated testing, with nested training and/or test sets of increasing size. We demonstrate in this paper that such repeated testing leads to biased estimates of classifier effectiveness. Experiments on a range of text classification tasks under three sequential testing frameworks show all three lead to optimistic estimates of effectiveness. We calculate empirical adjustments to unbias estimates on our data set, and identify directions for research that could lead to general techniques for avoiding bias while reducing labeling costs.
Assessor disagreement and text classifier accuracy
Authors: William Webber, Jeremy Pickens
Venue: SIGIR
Year: 2013

Text classifiers are frequently used for high-yield retrieval from large corpora, such as in e-discovery. The classifier is trained by annotating example documents for relevance. These examples may, however, be assessed by people other than those whose conception of relevance is authoritative. In this paper, we examine the impact that disagreement between actual and authoritative assessor has upon classifier effectiveness, when evaluated against the authoritative conception. We find that using alternative assessors leads to a significant decrease in binary classification quality, though less so ranking quality. A ranking consumer would have to go on average 25% deeper in the ranking produced by alternative-assessor training to achieve the same yield as for authoritative-assessor training.
Here and there: goals, activities, and predictions about location from geotagged queries
Authors: Robert West, Ryen W. White, Eric Horvitz
Venue: SIGIR
Year: 2013

A significant portion of Web search is performed in mobile settings. We explore the links between users' queries on mobile devices and their locations and movement, with a focus on interpreting queries about addresses. We find that users tend to have a primary location, likely corresponding to home or workplace, and that a user's location relative to this primary location systematically influences the patterns of address searches. We apply our findings to construct a statistical model that can predict with high accuracy whether a user will be soon observed at an address that had been recently retrieved via search. Such an ability to predict that a user will transition to a location can be harnessed for multiple uses including provision of directions and traffic information, the rendering of competitive advertising, and guiding the opportunistic completion of pending tasks that can be accomplished en route to a target location.
Workshop on health search and discovery: helping users and advancing medicine
Authors: Ryen W. White, Elad Yom-Tov, Eric Horvitz, Eugene Agichtein, William R. Hersh
Venue: SIGIR
Year: 2013

This workshop brings together researchers and practitioners from industry and academia to discuss search and discovery in the medi-cal domain. The event focuses on ways to make medical and health information more accessible to laypeople (including enhancements to ranking algorithms and search interfaces), and how we can dis-cover new medical facts and phenomena from information sought online, as evidenced in query streams and other sources such as social media. This domain also offers many opportunities for appli-cations that monitor and improve quality of life of those affected by medical conditions, by providing tools to support their health-related information behavior.
Temporal variance of intents in multi-faceted event-driven information needs
Authors: Stewart Whiting, Ke Zhou, Joemon M. Jose, Mounia Lalmas
Venue: SIGIR
Year: 2013

Time is often important for understanding user intent during search activity, especially for information needs related to event-driven topics. Diversity for multi-faceted information needs ensures that ranked documents optimally cover multiple facets when a user's intent is uncertain. Effective diversity is reliant on methods to (i) discover and represent facets, and (ii) determine how likely each facet is the user's intent (i.e., its popularity). Past work has developed several techniques addressing these issues, however, they have concentrated on static approaches which do not consider the temporal nature of new and evolving intents and their popularity. In many cases, what a user expects may change dramatically over time as events develop. In this work we study the temporal variance of search intents for event-driven information needs using Wikipedia. First, we model intents based upon the structure represented by the section hierarchy of Wikipedia articles closely related to the information need. Using this technique, we investigate whether temporal changes in the content structure, i.e. in a section's text, reflect the temporal popularity of the intent. We map intents taken from a query-log (as ground-truth) to Wikipedia article sections and found that a large proportion are indeed reflected in topic-related article structure. By correlating the change activity of each section with the use of the intent query over time, we found that section change activity does reflect temporal popularity of many intents. Furthermore, we show that popularity between intents changes over time for event-driven topics.
EuroHCIR2013: the 3rd European workshop on human-computer interaction and information retrieval
Authors: Max L. Wilson, Birger Larsen, Preben Hansen, Kristian Norling, Tony Russell-Rose
Venue: SIGIR
Year: 2013

A proposal summary for the EuroHCIR workshop at SIGIR2013.
An incremental approach to efficient pseudo-relevance feedback
Authors: Hao Wu, Hui Fang
Venue: SIGIR
Year: 2013

Pseudo-relevance feedback is an important strategy to improve search accuracy. It is often implemented as a two-round retrieval process: the first round is to retrieve an initial set of documents relevant to an original query, and the second round is to retrieve final retrieval results using the original query expanded with terms selected from the previously retrieved documents. This two-round retrieval process is clearly time consuming, which could arguably be one of main reasons that hinder the wide adaptation of the pseudo-relevance feedback methods in real-world IR systems.
Leveraging viewer comments for mood classification of music video clips
Authors: Takehiro Yamamoto, Satoshi Nakamura
Venue: SIGIR
Year: 2013

This short paper proposes a method to classify music video clips uploaded to a video sharing service into music mood categories such as 'cheerful,' 'wistful,' and 'aggressive.' The method leverages viewer comments posted to the music video clips for the music mood classification. It extracts specific features from the comments: (1) adjectives in comments, (2) lengthened words in comments, and (3) comments in chorus sections. Our experimental results classifying 695 video clips into six mood categories showed that our method outperformed the baseline in terms of macro and micro averaged F-measures. In addition, our method outperformed the existing approaches that utilize lyrics and audio signals of songs.
Pursuing insights about healthcare utilization via geocoded search queries
Authors: Shuang-Hong Yang, Ryen W. White, Eric Horvitz
Venue: SIGIR
Year: 2013

Mobile devices provide people with a conduit to the rich infor-mation resources of the Web. With consent, the devices can also provide streams of information about search activity and location that can be used in population studies and real-time assistance. We analyzed geotagged mobile queries in a privacy-sensitive study of potential transitions from health information search to in-world healthcare utilization. We note differences in people's health infor-mation seeking before, during, and after the appearance of evidence that a medical facility has been visited. We find that we can accu-rately estimate statistics about such potential user engagement with healthcare providers. The findings highlight the promise of using geocoded search for sensing and predicting activities in the world.
Interoperability ranking for mobile applications
Authors: Dragomir Yankov, Pavel Berkhin, Rajen Subba
Venue: SIGIR
Year: 2013

At present, most major app marketplaces perform ranking and recommendation based on search relevance features or marketplace ``popularity'' statistics. For instance, they check similarity between app descriptions and user search queries, or rank-order the apps according to statistics such as number of downloads, user ratings etc. Rankings derived from such signals, important as they are, are insufficient to capture the dynamics of the apps ecosystem. Consider for example the questions: In a particular user context, is app A more likely to be launched than app B? Or does app C provide complementary functionality to app D-- Answering these questions requires identifying and analyzing the dependencies between apps in the apps ecosystem. Ranking mechanisms that reflect such interdependences are thus necessary.
Effective approaches to retrieving and using expertise in social media
Authors: Reyyan Yeniterzi
Venue: SIGIR
Year: 2013

Expert retrieval has been widely studied especially after the introduction of Expert Finding task in the TREC's Enterprise Track in 2005 [3]. This track provided two different test collections crawled from two organizations' public-facing websites and internal emails which led to the development of many state-of-the-art algorithms on expert retrieval [1]. Until recently, these datasets were considered good representatives of the information resources available within enterprise. However, the recent growth of social media also influenced the work environment, and social media became a common communication and collaboration tool within organizations. According to a recent survey by McKinsey Global Institute [2], 29% of the companies use at least one social media tool for matching their employees to tasks, and 26% of them assess their employees' performance by using social media. This shows that intra-organizational social media became an important resource to identify expertise within organizations.
Accurate and robust text detection: a step-in for text retrieval in natural scene images
Authors: Xu-Cheng Yin, Xuwang Yin, Kaizhu Huang, Hong-Wei Hao
Venue: SIGIR
Year: 2013

We propose and implement a robust text detection system, which is a prominent step-in for text retrieval in natural scene images or videos. Our system includes several key components: (1) A fast and effective pruning algorithm is designed to extract Maximally Stable Extremal Regions as character candidates using the strategy of minimizing regularized variations. (2) Character candidates are grouped into text candidates by the single-link clustering algorithm, where distance weights and threshold of clustering are learned automatically by a novel self-training distance metric learning algorithm. (3) The posterior probabilities of text candidates corresponding to non-text are estimated with an character classifier; text candidates with high probabilities are then eliminated and finally texts are identified with a text classifier. The proposed system is evaluated on the ICDAR 2011 Robust Reading Competition dataset and a publicly available multilingual dataset; the f measures are over 76% and 74% which are significantly better than the state-of-the-art performances of 71% and 65%, respectively.
IRWR: incremental random walk with restart
Authors: Weiren Yu, Xuemin Lin
Venue: SIGIR
Year: 2013

Random Walk with Restart (RWR) has become an appealing measure of node proximities in emerging applications \eg recommender systems and automatic image captioning. In practice, a real graph is typically large, and is frequently updated with small changes. It is often cost-inhibitive to recompute proximities from scratch via \emph{batch} algorithms when the graph is updated. This paper focuses on the incremental computations of RWR in a dynamic graph, whose edges often change over time. The prior attempt of RWR [1] deploys \kdash to find top-$k$ highest proximity nodes for a given query, which involves a strategy to incrementally \emph{estimate} upper proximity bounds. However, due to its aim to prune needless calculation, such an incremental strategy is \emph{approximate}: in $O(1)$ time for each node. The main contribution of this paper is to devise an \emph{exact} and fast incremental algorithm of RWR for edge updates. Our solution, \IRWR\!, can incrementally compute any node proximity in $O(1)$ time for each edge update without loss of exactness. The empirical evaluations show the high efficiency and exactness of \IRWR for computing proximities on dynamic networks against its batch counterparts.
Time-aware point-of-interest recommendation
Authors: Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, Nadia Magnenat-Thalmann
Venue: SIGIR
Year: 2013

The availability of user check-in data in large volume from the rapid growing location based social networks (LBSNs) enables many important location-aware services to users. Point-of-interest (POI) recommendation is one of such services, which is to recommend places where users have not visited before. Several techniques have been recently proposed for the recommendation service. However, no existing work has considered the temporal information for POI recommendations in LBSNs. We believe that time plays an important role in POI recommendations because most users tend to visit different places at different time in a day, \eg visiting a restaurant at noon and visiting a bar at night. In this paper, we define a new problem, namely, the time-aware POI recommendation, to recommend POIs for a given user at a specified time in a day. To solve the problem, we develop a collaborative recommendation model that is able to incorporate temporal information. Moreover, based on the observation that users tend to visit nearby POIs, we further enhance the recommendation model by considering geographical information. Our experimental results on two real-world datasets show that the proposed approach outperforms the state-of-the-art POI recommendation methods substantially.
Removing the mismatch headache in XML keyword search
Authors: Yong Zeng, Zhifeng Bao, Tok Wang Ling, Guoliang Li
Venue: SIGIR
Year: 2013

In this demo, we study one category of query refinement problems in the context of XML keyword search, where what users search for do not exist in the data while useless results are returned by the search engine. It is a hidden but important problem. We refer to it as the MisMatch problem. We propose a practical yet efficient way to detect the MisMatch problem and generate helpful suggestions to users, namely MisMatch detector and suggester. Our approach can be viewed as a post-processing job of query evaluation. An online XML keyword search engine embedding the MisMatch detector and suggester has been built and is available at [1].
Optimizing top-n collaborative filtering via dynamic negative item sampling
Authors: Weinan Zhang, Tianqi Chen, Jun Wang, Yong Yu
Venue: SIGIR
Year: 2013

Collaborative filtering techniques rely on aggregated user preference data to make personalized predictions. In many cases, users are reluctant to explicitly express their preferences and many recommender systems have to infer them from implicit user behaviors, such as clicking a link in a webpage or playing a music track. The clicks and the plays are good for indicating the items a user liked (i.e., positive training examples), but the items a user did not like (negative training examples) are not directly observed. Previous approaches either randomly pick negative training samples from unseen items or incorporate some heuristics into the learning model, leading to a biased solution and a prolonged training period. In this paper, we propose to dynamically choose negative training samples from the ranked list produced by the current prediction model and iteratively update our model. The experiments conducted on three large-scale datasets show that our approach not only reduces the training time, but also leads to significant performance gains.
Query change as relevance feedback in session search
Authors: Sicong Zhang, Dongyi Guan, Hui Yang
Venue: SIGIR
Year: 2013

Session search is the Information Retrieval (IR) task that performs document retrieval for an entire session. During a session, users often change queries to explore and investigate the information needs. In this paper, we propose to use query change as a new form of relevance feedback for better session search. Evaluation conducted over TREC 2012 Session Track shows that query change is a highly effective form of feedback as compared with existing relevance feedback methods. The proposed method outperforms the state-of-the-art relevance feedback methods for the TREC 2012 Session Track by a significant improvement of >25%.
Bias-variance decomposition of ir evaluation
Authors: Peng Zhang, Dawei Song, Jun Wang, Yuexian Hou
Venue: SIGIR
Year: 2013

It has been recognized that, when an information retrieval (IR) system achieves improvement in mean retrieval effectiveness (e.g. mean average precision (MAP)) over all the queries, the performance (e.g., average precision (AP)) of some individual queries could be hurt, resulting in retrieval instability. Some stability/robustness metrics have been proposed. However, they are often defined separately from the mean effectiveness metric. Consequently, there is a lack of a unified formulation of effectiveness, stability and overall retrieval quality (considering both). In this paper, we present a unified formulation based on the bias-variance decomposition. Correspondingly, a novel evaluation methodology is developed to evaluate the effectiveness and stability in an integrated manner. A case study applying the proposed methodology to evaluation of query language modeling illustrates the usefulness and analytical power of our approach.