OpenRaiser/Intern-Atlas huggingface dataset:
Location-aware click prediction in mobile local search
Authors: Dimitrios Lymberopoulos, Peixiang Zhao, Arnd Christian König, Klaus Berberich, Jie Liu
Venue: CIKM
Year: 2011

Users increasingly rely on their mobile devices to search, locate and discover places and activities around them while on the go. Their decision process is driven by the information displayed on their devices and their current context (e.g. traffic, driving or walking etc.). Even though recent research efforts have already examined and demonstrated how different context parameters such as weather, time and personal preferences affect the way mobile users click on local businesses, little has been done to study how the location of the user affects the click behavior. In this paper we follow a data-driven methodology where we analyze approximately 2 million local search queries submitted by users across the US, to visualize and quantify how differently mobile users click across locations. Based on the data analysis, we propose new location-aware features for improving local search click prediction and quantify their performance on real user query traces. Motivated by the results, we implement and evaluate a data-driven technique where local search models at different levels of location granularity (e.g. city, state, and country levels) are combined together at run-time to further improve click prediction accuracy. By applying the location-aware features and the multiple models at different levels of location granularity on real user query streams from a major, commercially available search engine, we achieve anywhere from 5% to 47% higher Precision than a single click prediction model across the US can achieve.
Selecting related terms in query-logs using two-stage SimRank
Authors: Yunlong Ma, Hongfei Lin, Yuan Lin
Venue: CIKM
Year: 2011

It is commonly believed that query logs from Web search are a gold mine for search business, because they reflect users' preference over Web pages presented by search engines, so a lot of studies based on query logs have been carried out in the last few years. In this study, we assume that two queries are relevant to each other when they have same clicked page in their result lists, and we also consider the queries' topics of user's need. Thus, we propose a Two-Stage SimRank (called TSS in this paper) algorithm based on SimRank and some clustering algorithms to compute the similarity among queries, and then use it to discover relevant terms for query expansion, considering the information of topics and the global relationships of queries concurrently, with a query log collected by a practical search engine. Experimental results on two TREC test collections show that our approach can discover qualified terms effectively and improve retrieval performance.
Matching query processing in high-dimensional space
Authors: Chunyang Ma, Yongluan Zhou, Lidan Shou, Dan Dai, Gang Chen
Venue: CIKM
Year: 2011

In many applications, such as online dating or job hunting websites, users often need to search for potential matches based on the requirements or preferences imposed by both sides.We refer to this type of queries as matching queries. In spite of their wide applicabilities, there has been little attention devoted to improve their performance. As matching queries often appear in various forms even within a single application, we, in this paper, propose a general processing framework, which can efficiently process various forms of matching queries. Moreover, we elaborate the detailed processing algorithms for two particular forms of matching queries to illustrate the applicability of this framework. We conduct an extensive experimental study with both synthetic and real datasets. The results indicate that, for various matching queries, our techniques can dramatically improve the query performance, especially when the dimensionality is high.
Privacy protected knowledge management in services with emphasis on quality data
Authors: Debapriyo Majumdar, Rose Catherine, Shajith Ikbal, Karthik Visweswariah
Venue: CIKM
Year: 2011

Improving productivity of practitioners through effective knowledge management and delivering high quality service in Application Management Services (AMS) domain, are key focus areas for all IT services organizations. One source of historical knowledge in AMS is the large amount of resolved problem ticket data which are often confidential, immensely valuable, but majority of it is of very bad quality. In this paper, we present a knowledge management tool that detects the quality of information present in problem tickets and enables effective knowledge search in tickets by prioritizing quality data in the search ranking. The tool facilitates leveraging of knowledge across different AMS accounts, while preserving data privacy, by masking client confidential information. It also extracts several relevant entities contained in the noisy unstructured text entered in the tickets and presents them to the users. We present several experimental evaluations and a pilot study conducted with an AMS account which show that our tool is effective and leads to substantial improvement in productivity of the practitioners.
Context seeking with social tags
Authors: Brandeis Marshall
Venue: CIKM
Year: 2011

Users intend for their image tags to be helpful in image searching and browsing. However, image tags have added a new layer of complexity, while the evaluation measures remain primarily binary in nature. Obviously, tags are related to each other, but we need to form models that measures the bias between tags.
Information extraction from pathology reports in a hospital setting
Authors: David Martínez, Yue Li
Venue: CIKM
Year: 2011

As more health data becomes available, information extraction aims to make an impact on the workflows of hospitals and care centers. One of the targeted areas is the management of pathology reports, which are employed for cancer diagnosis and staging. In this work we integrate text mining tools in the workflow of the Royal Melbourne Hospital, to extract information from pathology reports with minimal expert intervention. Our framework relies on coarse-grained annotation (at document level), making it highly portable. Our evaluation shows that the kind of language used in these reports makes it feasible to extract information with high precision and recall, by means of state-of-the-art classification methods, and feature engineering.
Entity timelines: visual analytics and named entity evolution
Authors: Arturas Mazeika, Tomasz Tylenda, Gerhard Weikum
Venue: CIKM
Year: 2011

The constantly evolving Web reflects the evolution of society. Knowledge about entities (people, companies, political parties, etc.) evolves over time. Facts add up (e.g., awards, lawsuits, divorces), change (e.g., spouses, CEOs, political positions), and even cease to exist (e.g., countries split into smaller or join into bigger ones). Analytics of the evolution of the entities poses many challenges including extraction, disambiguation, and canonization of entities from large text collections as well as introduction of specific analysis and interactivity methods for the evolving entity data.
A geographic study of tie strength in social media
Authors: Jeffrey McGee, James Caverlee, Zhiyuan Cheng
Venue: CIKM
Year: 2011

In this paper, we investigate the interplay of distance and tie strength through an examination of 20 million geo-encoded tweets collected from Twitter and 6 million user profiles. Concretely, we investigate the relationship between the strength of the tie between a pair of users, and the distance between the pair. We identify several factors -- including following, mentioning, and actively engaging in conversations with another user -- that can strongly reveal the distance between a pair of users. We find a bimodal distribution in Twitter, with one peak around 10 miles from people who live nearby, and another peak around 2500 miles, further validating Twitter's use as both a social network (with geographically nearby friends) and as a news distribution network (with very distant relationships).
Simultaneously improving CSAT and profit in a retail banking organization
Authors: Sameep Mehta, Ullas Nambiar, Vishal S. Batra, Sumit Negi, Prasad Deshpande, Gyana R. Parija
Venue: CIKM
Year: 2011

Customer satisfaction (CSAT) is the key driver for retention and growth in retail banking and several techniques have been applied by banks to achieve this. For instance, banks in emerging markets with high footfall in branches have gone beyond the traditional approach of segmenting customers and services to optimizing the wait time for customers visiting the bank's branch. While this approach has significantly improved service quality, it has also added a new dimension in the service quality metric : pro-actively identify and address customer needs for (i) efficient banking experience and (ii) enhancing profit by selling additional services to existing customer. In this paper we present a system that addresses the challenge involved in providing better service to retail banking customer while ensuring that a larger share of customer's wallet comes to the branch. We do this by combining predictive analytics, scheduling and process optimization techniques.
Interactive reasoning in uncertain RDF knowledge bases
Authors: Timm Meiser, Maximilian Dylla, Martin Theobald
Venue: CIKM
Year: 2011

Recent advances in Web-based information extraction have allowed for the automatic construction of large, semantic knowledge bases, which are typically captured in RDF format. The very nature of the applied extraction techniques however entails that the resulting RDF knowledge bases may face a significant amount of incorrect, incomplete, or even inconsistent (i.e., uncertain) factual knowledge, which makes query answering over this kind of data a challenge. Our reasoner, coined URDF, supports SPARQL queries along with rule-based, first-order predicate logic to infer new facts and to resolve data uncertainty over millions of RDF triplets directly at query time. We demonstrate a fully interactive reasoning engine, combining a Java-based reasoning backend and a Flash-based visualization frontend in a dynamic client-server architecture. Our visualization frontend provides interactive access to the reasoning backend, including tasks like exploring the knowledge base, rule-based and statistical reasoning, faceted browsing of large query graphs, and explaining answers through lineage.
S3K: seeking statement-supporting top-K witnesses
Authors: Steffen Metzger, Shady Elbassuoni, Katja Hose, Ralf Schenkel
Venue: CIKM
Year: 2011

Traditional information retrieval techniques based on keyword search help to identify a ranked set of relevant documents, which often contains many documents in the top ranks that do not meet the user's intention. By considering the semantics of the keywords and their relationships, both precision and recall can be improved. Using an ontology and mapping keywords to entities/concepts and identifying the relationship between them that the user is interested in, allows for retrieving documents that actually meet the user's intention. In this paper, we present a framework that enables semantic-aware document retrieval. User queries are mapped to semantic statements based on entities and their relationships. The framework searches for documents expressing these statements in different variations, e.g., synonymous names for entities or different textual expressions for relations between them. The size of potential result sets makes ranking documents according to their relevance to the user an essential component of such a system. The ranking model proposed in this paper is based on statistical language-models and considers aspects such as the authority of a document and the confidence in the textual pattern representing the queried information.
Switch detector: an activity spotting system for desktop
Authors: Hamid Turab Mirza, Ling Chen, Gencai Chen, Ibrar Hussain, Xufeng He
Venue: CIKM
Year: 2011

An average white-collar worker deals with enormous amount of digital information on daily basis. Recently, there has been a growing interest to support their work. However, in order to be really supportive there is a need to know the current activity of the user at all times. In this paper we present a new technique that takes advantage of temporal aspects of user activity behavior to infer when it is most likely that an activity switch is occurring. We then describe "Activity Switch Detector" an interactive switch notification system embodying these ideas, and an extensive user study by ten participants to test the validity of the approach and present its results.
Review recommendation: personalized prediction of the quality of online reviews
Authors: Samaneh Moghaddam, Mohsen Jamali, Martin Ester
Venue: CIKM
Year: 2011

The problem of identifying high quality and helpful reviews automatically has attracted many attention recently. Current methods assume that the helpfulness of a review is independent from the readers of that review. However, we argue that the quality of a review may not be the same for different users. In this paper, we employ latent factor models to address this problem. We evaluate the proposed models using a real life database from Epinions.com. The experiments demonstrate that the latent factor models outperform the state-of-the-art approaches and confirms that the helpfulness of a review is indeed not the same for all users.
Trained trigger language model for sentence retrieval in QA: bridging the vocabulary gap
Authors: Saeedeh Momtazi, Dietrich Klakow
Venue: CIKM
Year: 2011

We propose a novel language model for sentence retrieval in Question Answering (QA) systems called trained trigger language model. This model addresses the word mismatch problem in information retrieval. The proposed model captures pairs of trigger and target words while training on a large corpus. The word pairs are extracted based on both unsupervised and supervised approaches while different notions of triggering are used. In addition, we study the impact of corpus size and domain for a supervised model. All notions of the trained trigger model are finally used in a language model-based sentence retrieval framework. Our experiments on TREC QA collection verify that the proposed model significantly improves the sentence retrieval performance compared to the state-of-the-art translation model and class model which address the same problem.
MEMSCALE: in-cluster-memory databases
Authors: Héctor Montaner, Federico Silla, Holger Fröning, José Duato
Venue: CIKM
Year: 2011

We have developed a new memory architecture for clusters that allows automatic access from any processor to any memory module in the cluster completely by hardware. Thus, with a single assembly instruction a processor can retrieve (or update) a memory location in a remote node. The efficiency of this new paradigm makes it possible to speed-up the execution of shared-memory applications with very large memory footprints by running them across the entire cluster, thus providing them a true shared-memory environment (contrary to the emulation typically carried out by software-based distributed shared memory).
Scalable density-based subspace clustering
Authors: Emmanuel Müller, Ira Assent, Stephan Günnemann, Thomas Seidl
Venue: CIKM
Year: 2011

For knowledge discovery in high dimensional databases, subspace clustering detects clusters in arbitrary subspace projections. Scalability is a crucial issue, as the number of possible projections is exponential in the number of dimensions. We propose a scalable density-based subspace clustering method that steers mining to few selected subspace clusters. Our novel steering technique reduces subspace processing by identifying and clustering promising subspaces and their combinations directly. Thereby, it narrows down the search space while maintaining accuracy. Thorough experiments on real and synthetic databases show that steering is efficient and scalable, with high quality results. For future work, our steering paradigm for density-based subspace clustering opens research potential for speeding up other subspace clustering approaches as well.
People search using NDC classification system
Authors: Harumi Murakami, Yoshinobu Ura
Venue: CIKM
Year: 2011

To help users select and understand people during searches for them, we present a method of assigning Nippon Decimal Classification (NDC), which is a system of library classification numbers, to people on the Web. By assigning NDC numbers to people, we can assign not only labels to people but also build a NDC-based people directory. We developed a prototype based on this approach.
Computational geography
Authors: Vanessa Murdock, Gary Gale
Venue: CIKM
Year: 2011

As the industry moves to personalization and mobility, users expect their applications to be location savvy, and relevant to their lives in increasing detail. While we can pinpoint a user at a location within 700 meters with just their IP address, and within a meter with their GPS-enabled mobile phone, we fall short when it comes to understanding their geographic context. A person's geographic context includes their current and previous location, the things that surround them, their activity in a given place, as well as their thoughts and feelings in that place. Understanding this context allows us to personalize their experience and refine their interactions with an application, on a hyper-local level.
Discovering customer intent in real-time for streamlining service desk conversations
Authors: Ullas Nambiar, Tanveer A. Faruquie, L. Venkata Subramaniam, Sumit Negi, Ganesh Ramakrishnan
Venue: CIKM
Year: 2011

Businesses require the contact center agents to meet pre-specified customer satisfaction levels while keeping the cost of operations low or meeting sales targets, objectives that end up being complementary and difficult to achieve in real-time. In this paper, we describe a speech enabled real-time conversation management system that tracks customer-agent conversations to detect user intent (e.g. gathering information, likely to buy, etc.) that can help agents to then decide the best sequence of actions for that call. We present an entropy based decision support system that parses a text stream generated in real-time during a audio conversation and identifies the first instance at which the intent becomes distinct enough for the agent to then take subsequent actions. We provide evaluation results displaying the efficiency and effectiveness of our system.
Extracting semantic annotations from twitter
Authors: Sascha Narr, Ernesto William De Luca, Sahin Albayrak
Venue: CIKM
Year: 2011

Tweets contain mentions of numerous entities, persons and events, and often additional information, like an opinion, that can be viewed as an annotation of that entity. However, this information is currently being accumulated only by specific applications without being made available in a generic format. We discuss a natural language processing approach to extract information about entities and their annotations from tweets and transform them into a semantic, reusable knowledge base. We believe this will greatly facilitate access to user-generated Twitter data for many applications.
Two birds with one stone: learning semantic models for text categorization and word sense disambiguation
Authors: Roberto Navigli, Stefano Faralli, Aitor Soroa, Oier Lopez de Lacalle, Eneko Agirre
Venue: CIKM
Year: 2011

In this paper we present a novel approach to learning semantic models for multiple domains, which we use to categorize Wikipedia pages and to perform domain Word Sense Disambiguation (WSD). In order to learn a semantic model for each domain we first extract relevant terms from the texts in the domain and then use these terms to initialize a random walk over the WordNet graph. Given an input text, we check the semantic models, choose the appropriate domain for that text and use the best-matching model to perform WSD. Our results show considerable improvements on text categorization and domain WSD tasks. © 2011 ACM.
Does word sense disambiguation improve information retrieval?
Authors: Hwee Tou Ng
Venue: CIKM
Year: 2011

A basic form of semantic annotation is to label a word in a document with its correct sense based on the context in which the word occurs, thus providing the disambiguated sense of the word. Performing this task automatically is known as word sense disambiguation, which has been extensively studied in the natural language processing literature. Will semantic annotation of word senses improve information retrieval? This paper provides some thoughts on this question, which lies at the intersection of natural language processing and information retrieval.
PIKM 2011: the 4th ACM workshop for Ph.D. students in information and knowledge management
Authors: Anisoara Nica, Fabian M. Suchanek
Venue: CIKM
Year: 2011

The PIKM workshop gives Ph.D. students an opportunity to present their dissertation proposals at a global stage. Similarly to the CIKM, the PIKM workshop covers a wide range of topics in the areas of databases, information retrieval and knowledge management. Interdisciplinary work across these tracks is particularly encouraged.
DIGRank: using global degree to facilitate ranking in an incomplete graph
Authors: Xiang Niu, Lusong Li, Ke Xu
Venue: CIKM
Year: 2011

PageRank has been broadly applied to get credible rank sequences of nodes in many networks such as the web, citation networks, or online social networks. However, in the real world, it is usually hard to ascertain a complete structure of a network, particularly a large-scale one. Some researchers have begun to explore how to get a relatively accurate rank more efficiently. They have proposed some local approximation methods, which are especially designed for quickly estimating the PageRank value of a new node, after it is just added to the network. Yet, these local approximation methods rely on the link server too much, and it is difficult to use them to estimate rank sequences of nodes in a group. So we propose a new method called DIGRank, which uses global Degree to facilitate Ranking in an Incomplete Graph and which takes into account the frequent need for applications to rank users in a community, retrieve pages in a particular area, or mine nodes in a fractional or limited network. Based on experiments in small-world and scale-free networks generated by models, the DIGRank method performs better than other local estimation methods on ranking nodes in a given subgraph. In the models, it tends to perform best in graphs that have low average shortest path length, high average degree, or weak community structure. Besides, compared with an local PageRank and an advanced local approximation method, it significantly reduces the computational cost and error rate.
WikiLabel: an encyclopedic approach to labeling documents en masse
Authors: Tadashi Nomoto
Venue: CIKM
Year: 2011

This paper presents a particular approach to collective labeling of multiple documents, which works by associating the documents with Wikipedia pages and labeling them with headings the pages carry. The approach has an obvious advantage over past approaches in that it is able to produce fluent labels, as they are hand-written by human editors. We carried out some experiments on the TDT5 dataset, which found that the approach works rather robustly for an arbitrary set of documents in the news domain. Comparisons were made with some baselines, including the state of the art, with results strongly in favor of our approach.
Fu-Finder: a game for studying querying behaviours
Authors: Carly O&apos, Neil, James Purvis, Leif Azzopardi
Venue: CIKM
Year: 2011

Usually the focus of evaluation within Information Retrieval has been placed largely upon the system. However, the individual user and their submitted queries are typically the greatest source of variation in the search process. This demonstration paper presents Fu-Finder, a fun and enjoyable game that measures the user's querying abilities (or search-fu). This game provides useful data for the study of user querying behaviour and assesses how well users can find specific web pages using different search engines.