OpenRaiser/Intern-Atlas huggingface dataset:
Comprehension-based result snippets
Authors: Abhijith Kashyap, Vagelis Hristidis
Venue: CIKM
Year: 2012

Result snippets are used by most search interfaces to preview query results. Snippets help users quickly decide the relevance of the results, thereby reducing the overall search time and effort. Most work on snippets have focused on text snippets for Web pages in Web search. However, little work has studied the problem of snippets for structured data, e.g., product catalogs. Furthermore, all works have focused on the important goal of creating informative snippets, but have ignored the amount of user effort required to comprehend, i.e., read and digest, the displayed snippets. In particular, they implicitly assume that the comprehension effort or cost only depends on the length of the snippet, which we show is incorrect for structured data. We propose novel techniques to construct snippets of structured heterogeneous results, which not only select the most informative attributes for each result, but also minimize the expected user effort (time) to comprehend these snippets. We create a comprehension model to quantify the effort incurred by users in comprehending a list of result snippets. Our model is supported by an extensive user-study. A key observation is that the user effort for comprehending an attribute across multiple snippets only depends on the number of unique positions (e.g., indentations) where this attribute is displayed and not on the number of occurrences. We analyze the complexity of the snippet construction problem and show that the problem is NP-hard, even when we only consider the comprehension cost. We present efficient approximate algorithms, and experimentally demonstrate their effectiveness and efficiency.
Booksonline'12: 5th workshop on online books, complementary social media and their impact
Authors: Gabriella Kazai, Monica Landoni, Carsten Eickhoff, Peter Brusilovsky
Venue: CIKM
Year: 2012

BooksOnline'12, the fifth workshop in the series, aims to offer a forum for bringing together expertise from academia, industry and libraries to facilitate the exchange of research results and technology in the field of digital libraries with specific focus on online books and complementary social media. The focus of this year's workshop is "engaging reading experiences", starting from the act of deciding what to read, through the exploration and interpretation of a book's content, to sharing the overall experience. Within this overall umbrella theme, the accepted papers naturally showed three salient themes: (1) Search and Discovery, (2) Personalization and Recommendation, and Reading Experiences beyond Text. The contributions demonstrate a range of technologies, including a collaborative tabletop visual approach to support the searching and discovery of books, co-citation methods to enhance document retrieval; exploring open issues in audio-book production to support non-text based reading and improving e-book accessibility; new approaches to recommendation that take into account writing style as well as looking specifically to young readers and their needs in order to develop recommendation tools that consider both content and reading level and match these against the readers' specific interests and reading ability. Following in the theme of the reader playing a central role in the future of our digital era, we are honored to welcome Maribeth Back from FX Palo Alto and Natasa Milic-Frayling from Microsoft Research as our keynote speakers.
Diversity in blog feed retrieval
Authors: Mostafa Keikha, Fabio Crestani, W. Bruce Croft
Venue: CIKM
Year: 2012

Blog distillation (blog feed retrieval) is a task in blog retrieval where the goal is to rank blogs according to their recurrent relevance to a query topic. One of the main properties of blog feed retrieval is that the unit of retrieval is a collection of documents as opposed to a single document as in other IR tasks. This collection retrieval nature of blog distillation introduces new challenges and requires new investigations specific to this problem.
Entity resolution using search engine results
Authors: Madian Khabsa, Pucktada Treeratpituk, C. Lee Giles
Venue: CIKM
Year: 2012

Given a set of automatically extracted entities E of size n, we would like to cluster all the various names referring to the same canonical entity together. The variations of each entity include acronyms, full name, and informal naming conventions. We propose using search engine results to cluster variations of each entity based on the URLs appearing in those results. We create a cluster C for each top search result returned by querying for the entity e ∈ E assigning e to the cluster C. Our experiments on a manually created dataset shows that our approach achieves higher precision and recall than string matching algorithm and hierarchical clustering based disambiguation methods.
Demographic context in web search re-ranking
Authors: Eugene Kharitonov, Pavel Serdyukov
Venue: CIKM
Year: 2012

In this paper we study usefulness of user's demographical context for improving ranking of ambiguous queries. Context-aware relevance model is learnt from implicit user behaviour by using a simple yet general modification of a state-of-art click model which is capable to catch dependences from the search context. After that the machine learned click model is used in an offline re-ranking experiment and it is demonstrated that the demographical context ranking features provide improvements in ranking quality. Further, we perform a study to investigate the impact of different facets of demographical features (gender, age, and income) on search ranking performance and manually analyse queries which exhibit strong context dependences to get an additional understanding of the model behaviour.
Decomposition-by-normalization (DBN): leveraging approximate functional dependencies for efficient tensor decomposition
Authors: Mijung Kim, K. Selçuk Candan
Venue: CIKM
Year: 2012

For many multi-dimensional data applications, tensor operations as well as relational operations need to be supported throughout the data lifecycle. Although tensor decomposition is shown to be effective for multi-dimensional data analysis, the cost of tensor decomposition is often very high. We propose a novel decomposition-by-normalization scheme that first normalizes the given relation into smaller tensors based on the functional dependencies of the relation and then performs the decomposition using these smaller tensors. The decomposition and recombination steps of the decomposition-by- normalization scheme fit naturally in settings with multiple cores. This leads to a highly efficient, effective, and parallelized decomposition-by-normalization algorithm for both dense and sparse tensors. Experiments confirm the efficiency and effectiveness of the proposed decomposition-by-normalization scheme compared to the conventional nonnegative CP decomposition approach.
Impact neighborhood indexing (INI) in diffusion graphs
Authors: Jung Hyun Kim, K. Selçuk Candan, Maria Luisa Sapino
Venue: CIKM
Year: 2012

A graph neighborhood consists of a set of nodes that are nearby or otherwise related to each other. While existing definitions consider the structure (or topology) of the graph, we note that they fail to take into account the information propagation and diffusion characteristics, such as decay and reinforcement, common in many networks. In this paper, we first define the propagation efficiency of nodes and edges. We use this to introduce the novel concept of zero-erasure (or impact) neighborhood (ZEN) of a given node, n, consisting of the set of nodes that receive information from (or are impacted by) n without any decay. Based on this, we present an impact neighborhood indexing (INI) algorithm that creates data structures to help quickly identify impact neighborhood of any given node. Experiment results confirm the efficiency and effectiveness of the proposed INI algorithms.
Understanding book search behavior on the web
Authors: Jin Young Kim, Henry Allen Feild, Marc-Allen Cartright
Venue: CIKM
Year: 2012

With the increased availability of e-books and digitized book collections, more users are searching the web for information about books. There are many online digital libraries containing book, author and subject data, which are accessed via internal search services as well as external web sites, such as Google. Although this is a common yet complex information-seeking behavior involving multiple search systems with different characteristics, little is known about how users find information in this scenario.
Mathematical equation retrieval using plain words as a query
Authors: Shinil Kim, Seon Yang, Youngjoong Ko
Venue: CIKM
Year: 2012

This paper proposes how to effectively retrieve the mathematical equations when the plain words are given as a query. The proposed system requires no complicated mathematical symbols, no particular input tool and no constraint of query. Users can enter a query with plain words like the traditional Information Retrieval. For this, we extract features from the plain texts that are converted from the real math equations. Experimental results show an outstanding performance, a MRR of 0.6585.
InCaToMi: integrative causal topic miner between textual and non-textual time series data
Authors: Hyun Duk Kim, ChengXiang Zhai, Thomas A. Rietz, Daniel Diermeier, Meichun Hsu, Malú Castellanos, Carlos Ceja Limon
Venue: CIKM
Year: 2012

Topic modeling is popular for text mining tasks. Recently, topic modeling has been combined with time lines when textual data is related to external non-textual time series data such as stock prices. However, no previous work has used the external non-textual time series data in the process of topic modeling. In this paper, we describe a novel text mining system, Integrative Causal Topic Miner (InCaToMi) that integrates textual and non-textual time series data. InCaToMi automatically finds causal relationships and topics using text data and external non-textual time series data using Granger Testing. Moreover, InCaToMi considers the non-textual time series data in the topic modeling process, using the time series data to iteratively improve modeling results through interactions between it and the textual data at both topic and word levels.
Assessing the relationship between context, user preferences, and content in search behaviour
Authors: Hanna Knäusl, Bernd Ludwig
Venue: CIKM
Year: 2012

Searching information by using search engines and browsers is a tedious task for users. Navigational and informational search tasks are complicated by the fact that web servers always provide complete web pages and do not tailor their content to the user's current information need. In this paper, we present a proposal for the application of context-aware recommendation techniques to simulate human decision making when selecting elements of content to be included in an answer to an information need. As a first step towards live generation of content, we present results on our experimental study to capture decision criteria for this selection problem that web users apply in choosing content. These preferences could then later be formalized in terms of a knowledge-based context-aware and personalized model for recommending content during information search.
Efficient retrieval of recommendations in a matrix factorization framework
Authors: Noam Koenigstein, Parikshit Ram, Yuval Shavitt
Venue: CIKM
Year: 2012

Low-rank Matrix Factorization (MF) methods provide one of the simplest and most effective approaches to collaborative filtering. This paper is the first to investigate the problem of efficient retrieval of recommendations in a MF framework. We reduce the retrieval in a MF model to an apparently simple task of finding the maximum dot-product for the user vector over the set of item vectors. However, to the best of our knowledge the problem of efficiently finding the maximum dot-product in the general case has never been studied. To this end, we propose two techniques for efficient search -- (i) We index the item vectors in a binary spatial-partitioning metric tree and use a simple branch and-bound algorithm with a novel bounding scheme to efficiently obtain exact solutions. (ii) We use spherical clustering to index the users on the basis of their preferences and pre-compute recommendations only for the representative user of each cluster to obtain extremely efficient approximate solutions. We obtain a theoretical error bound which determines the quality of any approximate result and use it to control the approximation. Both these simple techniques are fairly independent of each other and hence are easily combined to further improve recommendation retrieval efficiency. We evaluate our algorithms on real-world collaborative-filtering datasets, demonstrating more than ×7 speedup (with respect to the naive linear search) for the exact solution and over ×250 speedup for approximate solutions by combining both techniques.
Predicting CTR of new ads via click prediction
Authors: Alexander Kolesnikov, Yury Logachev, Valeriy A. Topinskiy
Venue: CIKM
Year: 2012

Predicting CTR of ads on the search result page is an urgent topic. The reason for this is that choosing the right advertisement greatly affects revenue of the search engine and advertisers and user's satisfaction. For ads with the large click history it is quite clear how to predict CTR by utilizing statistical data. But for new ads with a poor click history such approach is not robust and reliable. We suggest a model for predicting CTR of such new ads. Contrary to the previous models of predicting CTR of new ads, our model uses events - clicks and skips1 instead of the observed CTR. In addition we have implemented several novel features, that resulted into the increase of the performance of our model. Offline and online experiments on the real search engine system demonstrated that our model outperforms the baseline and the approaches suggested in previous papers.
Ranking news events by influence decay and information fusion for media and users
Authors: Liang Kong, Shan Jiang, Rui Yan, Shize Xu, Yan Zhang
Venue: CIKM
Year: 2012

In many cases, people would like to read the news with great importance on the Internet. However, what users can grasp covers a very small part compared with the huge amount of news which never stops increasing. In this paper, we try to find what users are most likely to be interested in. We notice that media focus plays an essential role in distinguishing news topics and user attention is also an important factor. Therefore, we first propose five strategies which only exploit media focus to decide news influence impact. Then we provide three strategies to combine user attention with media focus. Meanwhile, we also take four types of interaction between user attention and media focus into consideration. To the best of our knowledge, this is the first work to establish different models for computing influence decay of news topics. Experiments show that better influence scores will be achieved by a decay algorithm based on Ebbinghaus forgetting curve and information fusion by considering interactions between user attention and media focus.
Meta path-based collective classification in heterogeneous information networks
Authors: Xiangnan Kong, Philip S. Yu, Ying Ding, David J. Wild
Venue: CIKM
Year: 2012

Collective classification approaches exploit the dependencies of a group of linked objects whose class labels are correlated and need to be predicted simultaneously. In this paper, we focus on studying the collective classification problem in heterogeneous networks, which involves multiple types of data objects interconnected by multiple types of links. Intuitively, two objects are correlated if they are linked by many paths in the network. By considering different linkage paths in the network, one can capture the subtlety of different types of dependencies among objects. We introduce the concept of meta-path based dependencies among objects, where a meta path is a path consisting a certain sequence of linke types. We show that the quality of collective classification results strongly depends upon the meta paths used. To accommodate the large network size, a novel solution, called HCC (meta-path based Heterogenous Collective Classification), is developed to effectively assign labels to a group of instances that are interconnected through different meta-paths. The proposed HCC model can capture different types of dependencies among objects with respect to different meta paths. Empirical studies on real-world networks demonstrate that effectiveness of the proposed meta path-based collective classification approach.
Social book search: comparing topical relevance judgements and book suggestions for evaluation
Authors: Marijn Koolen, Jaap Kamps, Gabriella Kazai
Venue: CIKM
Year: 2012

The Web and social media give us access to a wealth of information, not only different in quantity but also in character---traditional descriptions from professionals are now supplemented with user generated content. This challenges modern search systems based on the classical model of topical relevance and ad hoc search: How does their effectiveness transfer to the changing nature of information and to the changing types of information needs and search tasks? We use the INEX 2011 Books and Social Search Track's collection of book descriptions from Amazon and social cataloguing site LibraryThing. We compare classical IR with social book search in the context of the LibraryThing discussion forums where members ask for book suggestions. Specifically, we compare book suggestions on the forum with Mechanical Turk judgements on topical relevance and recommendation, both the judgements directly and their resulting evaluation of retrieval systems. First, the book suggestions on the forum are a complete enough set of relevance judgements for system evaluation. Second, topical relevance judgements result in a different system ranking from evaluation based on the forum suggestions. Although it is an important aspect for social book search, topical relevance is not sufficient for evaluation. Third, professional metadata alone is often not enough to determine the topical relevance of a book. User reviews provide a better signal for topical relevance. Fourth, user-generated content is more effective for social book search than professional metadata. Based on our findings, we propose an experimental evaluation that better reflects the complexities of social book search.
An evaluation of corpus-driven measures of medical concept similarity for information retrieval
Authors: Bevan Koopman, Guido Zuccon, Peter Bruza, Laurianne Sitbon, Michael Lawley
Venue: CIKM
Year: 2012

Measures of semantic similarity between medical concepts are central to a number of techniques in medical informatics, including query expansion in medical information retrieval. Previous work has mainly considered thesaurus-based path measures of semantic similarity and has not compared different corpus-driven approaches in depth. We evaluate the effectiveness of eight common corpus-driven measures in capturing semantic relatedness and compare these against human judged concept pairs assessed by medical professionals. Our results show that certain corpus-driven measures correlate strongly (approx 0.8) with human judgements. An important finding is that performance was significantly affected by the choice of corpus used in priming the measure, i.e., used as evidence from which corpus-driven similarities are drawn. This paper provides guidelines for the implementation of semantic similarity measures for medical informatics and concludes with implications for medical information retrieval.
The 2012 international workshop on web-scale knowledge representation, retrieval, and reasoning
Authors: Spyros Kotoulas, Yi Zeng, Zhisheng Huang
Venue: CIKM
Year: 2012

The rapid and perpetual growth of knowledge on the Web has given rise to many grand challenges (such as scalability, inconsistency, uncertainty, distribution and dynamics) for traditional knowledge processing methods and systems. Knowledge representation, retrieval and reasoning methods need to evolve and adapt to the Web to face these challenges and make this vast, heterogenous knowledge useful and accessible. In this light, the International Workshop on Web-scale Knowledge Representation, Retrieval, and Reasoning (Web-KR) is initiated. This workshop serves as the third one in this workshop series. This summary discusses the scope of Web-KR and introduces the advances in this field through the accepted papers in the Web-KR 2012 workshop, co-located with CIKM 2012.
GPU acceleration of probabilistic frequent itemset mining from uncertain databases
Authors: Yusuke Kozawa, Toshiyuki Amagasa, Hiroyuki Kitagawa
Venue: CIKM
Year: 2012

Uncertain databases have been widely developed to deal with the vast amount of data that contain uncertainty. To extract valuable information from the uncertain databases, several methods of frequent itemset mining, one of the major data mining techniques, have been proposed. However, their performance is not satisfactory because handling uncertainty incurs high processing costs. In order to address this problem, we utilize GPGPU (General-Purpose computation on GPU). GPGPU implies using a GPU (Graphics Processing Unit), which is originally designed for processing graphics, to accelerate general purpose computation. In this paper, we propose a method of frequent itemset mining from uncertain databases using GPGPU. The main idea is to speed up probability computations by making the best use of GPU's high parallelism and low-latency memory. We also employ an algorithm to manipulate a bitstring and data-parallel primitives to improve performance in the other parts of the method. Extensive experiments show that our proposed method is up to two orders of magnitude faster than existing methods.
A tool for automated evaluation of algorithms
Authors: Philipp Kranen, Stephan Wels, Tim Rohlfs, Sebastian Raubach, Thomas Seidl
Venue: CIKM
Year: 2012

Testing algorithms and systems involves trying different sets of parameter values on different domains or data sets. Even for a moderate number of parameters and domains the number of possible experiments can get very large due to the combinatorial explosion. Evaluating the outcome of these experiments requires comparing the results, which is often done by writing a script or inspecting the result files manually. For a new algorithm or version, the work has to be done over again. With hundreds, thousands, or even more possible experiments, both the preparation and the evaluation can become complex and tedious. In this demonstrator we present a software tool, called ET, for evaluating the parameters of an algorithm or system, either automatically or controlled by the user. It allows to launch large numbers of experiments in just a few clicks, visually explore the results and analyze the performance of the algorithm.
Predicting the performance of passage retrieval for question answering
Authors: Eyal Krikon, David Carmel, Oren Kurland
Venue: CIKM
Year: 2012

We present a novel approach to predicting the performance of passage retrieval for question answering. That is, estimating the effectiveness, for answer extraction, of a list of passages retrieved in response to a question when relevance judgments are not available. Our prediction model integrates two types of estimates. The first estimates the probability that the information need expressed by the question is satisfied by the passages. This estimate is devised by adapting query-performance predictors developed for the document retrieval task. The second type estimates the probability that the passages contain the answers. This estimate relies on the occurrences of named entities that are likely to answer the question. Empirical evaluation demonstrates the merits of our prediction approach. For example, the prediction quality is much better than that of the only previous prediction method devised for the task at hand.