OpenRaiser/Intern-Atlas huggingface dataset:
Probabilistic first pass retrieval for search advertising: from theory to practice
Authors: Hema Raghavan, Rukmini Iyer
Venue: CIKM
Year: 2010

Information retrieval in search advertising, as in other ad-hoc retrieval tasks, aims to find the most appropriate ranking of the ad documents of a corpus for a given query. In addition to ranking the ad documents, we also need to filter or threshold irrelevant ads from participating in the auction to be displayed alongside search results. In this work, we describe our experience in implementing a successful ad retrieval system for a commercial search engine based on the Language Modeling (LM) framework for retrieval. The LM demonstrates significant performance improvements over the baseline vector space model (TF-IDF) system that was in production at the time. From a modeling perspective, we propose a novel approach to incorporate query segmentation and phrases in the LM framework, discuss impact of score normalization for relevance filtering, and present preliminary results of incorporating query expansions using query rewriting techniques. From an implementation perspective, we also discuss real-time latency constraints of a production search engine and how we overcome them by adapting the WAND algorithm to work with language models. In sum, our LM formulation is considerably better in terms of accuracy metrics such as Precision-Recall (10% improvement in AUC) and nDCG (8% improvement in [email protected]) on editorial data and also demonstrates significant improvements in clicks in live user tests (0.787% improvement in Click Yield, with 8% coverage increase). Finally, we hope that this paper provides the reader with adequate insights into the challenges of building a system that serves millions of users every day.
Leveraging natural language processing of clinical narratives for phenotype modeling
Authors: Preethi Raghavan, Albert M. Lai
Venue: CIKM
Year: 2010

This paper will explore methods for effectively extracting information from clinical narratives. The proposed research will investigate the application of state of the art natural language processing techniques to clinical narratives, such as medical admission notes, discharge summaries, progress notes, and pathology reports to extract information of interest. The objective of this knowledge discovery task is the ability to generate a chronology of events, for a given patient, ultimately leading to patient cohort discovery. This in turn facilitates efficient information retrieval and enables patient specific question answering.
Yet another write-optimized DBMS layer for flash-based solid state storage
Authors: Hongchan Roh, Daewook Lee, Sanghyun Park
Venue: CIKM
Year: 2010

Flash-based Solid State Storage (flashSSS) has write-oriented problems such as low write throughput, and limited life-time. Especially, flashSSDs have a characteristic vulnerable to random-writes, due to its control logic utilizing parallelism between the flash memory chips. In this paper, we present a write-optimized layer of DBMSs to address the write-oriented problems of flashSSS in on-line transaction processing environments. The layer consists of a write-optimized buffer, a corresponding log space, and an in-memory mapping table, closely associated with a novel logging scheme called InCremental Logging (ICL). The ICL scheme enables DBMSs to reduce page-writes at the least expense of additional page-reads, while replacing random-writes into sequential-writes. Through experiments, our approach demonstrated up-to an order of magnitude performance enhancement in I/O processing time compared to the original DBMS, increasing the longevity of flashSSS by approximately a factor of two.
Automated interaction in social networks with datalog
Authors: Royi Ronen, Oded Shmueli
Venue: CIKM
Year: 2010

The Query Network [12] is a model for query-based social networks automation features, motivated by the rise of social networks as a central internet application. This work generalizes the model to consist of a proposal query and an acceptance query for each participant. As a result, addition of edges is done by coordination between participants, simulating interactions between participants. We designed, implemented and experimented with evaluation algorithms for this new model. Experiments with both synthetic and real datasets show the high effectiveness of our methods.
Concurrent atomic protocols for making and changing decisions in social networks
Authors: Royi Ronen, Oded Shmueli
Venue: CIKM
Year: 2010

We study a novel data management scenario, in which social networks participants use protocols in order to manage their activities and the ever-growing data available to them in the network. In particular, we study protocols which operate on a consistent network (that we define), and transform it into another consistent state by atomically performing a set of changes. Multiple protocol instances, which work on intersecting parts of the network graphs are able to operate concurrently.
Automatic metadata extraction from multilingual enterprise content
Authors: Melike Sah, Vincent Wade
Venue: CIKM
Year: 2010

Enterprises provide professionally authored content about their products/services in different languages for use in web sites and customer care. For customer care, personalization/personalized information delivery is becoming important since it re-encourages users to return to the service provider. Personalization usually requires both contextual and descriptive metadata. But current metadata authored by content developers is usually quite simple. In this paper, we introduce an automatic metadata extraction framework, which can extract multilingual metadata from the enterprise content, for a personalized information retrieval system. We introduce two new ontologies for metadata creation and a novel semi-automatic topic vocabulary extraction algorithm. We demonstrate and evaluate our approach on the English and German Symantec Norton 360 technical content. Evaluations indicate that the proposed approach produces rich and high quality metadata for a personalized information retrieval system.
Exploiting hierarchical tags for context-awareness
Authors: Alan Said, Jérôme Kunegis, Ernesto William De Luca, Sahin Albayrak
Venue: CIKM
Year: 2010

Context-awareness for information retrieval is a challenging problem as information about the user's current situation is rarely available. However, if such information is available, retrieval and recommendation systems could use it to find information more relevant to their users' current context. We present a model for creating contextualized recommendations based on implicitly expressed context via keywords assigned to previously seen items and other available information. When employed, the model can create ad hoc recommendation with improved accuracy.
Selectively diversifying web search results
Authors: Rodrygo L. T. Santos, Craig Macdonald, Iadh Ounis
Venue: CIKM
Year: 2010

Search result diversification is a natural approach for tackling ambiguous queries. Nevertheless, not all queries are equally ambiguous, and hence different queries could benefit from different diversification strategies. A more lenient or more aggressive diversification strategy is typically encoded by existing approaches as a trade-off between promoting relevance or diversity in the search results. In this paper, we propose to learn such a trade-off on a per-query basis. In particular, we examine how the need for diversification can be learnt for each query - given a diversification approach and an unseen query, we predict an effective trade-off between relevance and diversity based on similar previously seen queries. Thorough experiments using the TREC ClueWeb09 collection show that our selective approach can significantly outperform a uniform diversification for both classical and state-of-the-art diversification approaches.
Result-size estimation for information-retrieval subqueries
Authors: Guido Sautter, Klemens Böhm, Andranik Khachatryan
Venue: CIKM
Year: 2010

Estimating the approximate result size of a query before its execution based on small summary statistics is important for query optimization in database systems and for other facets of query processing. This also holds for queries over text databases. Research on selectivity estimation for such queries has focused on Boolean retrieval, i.e., a document may be relevant for the query or not. But with the coalescence of database and information retrieval (IR) technology, selectivity estimation for other, more sophisticated relevance functions is gaining importance as well. These models generate a query-specific distribution of the documents over the [0, 1]-interval. With document distributions, selectivity estimation means estimating how many documents are how similar to a given query. The problem is much more complex than selectivity estimation in the Boolean context: Beside document frequency, query results also depend on other characteristics such as term frequencies and document lengths. Selectivity estimation must take them into account as well. This paper proposes and evaluates a technique for estimating the result of retrieval queries with non-Boolean relevance functions. It estimates discretized document distributions over the range of the relevance function. Despite the complexity, compared to Boolean selectivity estimation, it requires little additional data, and the additional data can be stored in existing data structures with little extensions. Our evaluation demonstrates the effectiveness of our technique.
Challenges in personalized authority flow based ranking of social media
Authors: Hassan Sayyadi, John Edmonds, Vagelis Hristidis, Louiqa Raschid
Venue: CIKM
Year: 2010

As the social interaction of Internet users increases, so does the need to effectively rank social media. We study the challenges of personalized ranking of blog posts. Web search techniques are inadequate since social media lack many of the characteristics of the Web such as rich document content and an extensive hyperlink graph. Further, user behavior in social media has moved beyond keyword based search and must support users who follow a particular blog or theme. In this research, we extend a social media dataset to exploit the associations between authors, blog posts, and categories (topics) of the posts. We then apply personalized authority flow based ranking algorithms based on the random surfer model. We evaluate our personalization approaches through an extensive study on a range of virtual users whose preferences are defined based on intuitive criteria. Our evaluation shows that the accuracy of our personalized recommendations ranges from good to very good for a majority of users, and outperforms reasonable baseline approaches.
Mining networks with shared items
Authors: Jun Sese, Mio Seki, Mutsumi Fukuzaki
Venue: CIKM
Year: 2010

Recent advances in data processing have enabled the generation of large and complex graphs. Many researchers have developed techniques to investigate informative structures within these graphs. However, the vertices and edges of most real-world graphs are associated with its features, and only a few studies have considered their combination. In this paper, we specifically examine a large graph in which each vertex has associated items. From the graph, we extract subgraphs with common itemsets, which we call itemset-sharing subgraphs (ISSes). The problem has various potential applications such as the detection of gene networks affected by drugs or the findings of popular research areas of contributing researchers. We propose an efficient algorithm to enumerate ISSes in large graphs. This algorithm enumerates ISSes with two efficient data structures: a DFS itemset tree and a visited itemset table. In practive, the combination of these two structures enables us to compute optimal solutions efficiently. We demonstrate the efficiency of our algorithm in mining ISSes from synthetic graphs with more than one million edges. We also present experiments performed using two real biological networks and a citation network. The experiments show that our algorithm can find interesting patterns in real datasets
Computing the top-k maximal answers in a join of ranked lists
Authors: Mirit Shalem, Yaron Kanza
Venue: CIKM
Year: 2010

Complex search tasks that utilize information from several data sources, are answered by integrating the results of distinct basic search queries. In such integration, each basic query returns a ranked list of items, and the main task is to compute the join of these lists, returning the top-k combinations. Computing the top-k join of ranked lists has been studied extensively for the case where the answer comprises merely complete combinations. However, a join is a lossy operation, and over heterogeneous data sources some highly-ranked items, from the results of the basic queries, may not appear in any combination. Yet, such items and the partial combinations in which they appear may still be relevant answers and should not be discarded categorically.
(k, P)-anonymity: towards pattern-preserving anonymity of time-series data
Authors: Xuan Shang, Ke Chen, Lidan Shou, Gang Chen, Tianlei Hu
Venue: CIKM
Year: 2010

The challenges with privacy protection of time series are mainly due to the complex nature of the data and the queries performed on them. We study the anonymization of time series while trying to support complex queries, such as range and pattern similarity queries, on the published data. The conventional k-anonymity cannot effectively address this problem as it may suffer severe pattern loss. We propose a novel anonymization model called (k,P)-anonymity for pattern-rich time series. This model publishes both the attribute values and the patterns of time series in separate data forms. We demonstrate that our model can prevent linkage attacks on the published data while effectively support a wide variety of queries on the anonymized data. We also design an efficient algorithm for enforcing (k,P)-anonymity on time series data.
Building recommendation systems using peer-to-peer shared content
Authors: Yuval Shavitt, Ela Weinsberg, Udi Weinsberg
Venue: CIKM
Year: 2010

Peer-to-Peer (p2p) networks are used for sharing content by millions of users. Often, meta-data used for searching is missing or wrong, making it difficult for users to find content. Moreover, searching for new content is almost impossible. Recommender systems are unable to handle p2p data due to inherent difficulties, such as implicit ranking, noise and the extreme dimensions and sparseness of the network.
Topic aspect analysis for multi-document summarization
Authors: Chao Shen, Dingding Wang, Tao Li
Venue: CIKM
Year: 2010

Query-based multi-document summarization aims to create a short summary given a collection of documents and a query. Most of the existing methods treat the query as one single sentence and rank the sentences in the documents based on their similarities with the query sentence. However, these methods lack of intensive analysis on the given query which typically consist of several topic aspects. In this paper, we propose a topic aspect extraction method to discover the aspect words and sentences contained in the query narrative texts and the input documents, and then incorporate these aspect words and sentences into a cross propagation model based on the sentence-term bipartite graph for document summarization. Experiments on DUC benchmark data show the effectiveness of our proposed approach on the topic-driven document summarization task.
Using various term dependencies according to their utilities
Authors: Lixin Shi, Jian-Yun Nie
Venue: CIKM
Year: 2010

In this paper, we propose a model to integrate term dependencies. Different from previous studies, each pair of terms is assigned a different weight of dependency according to their utility to IR. The experiments show that our model can significantly outperform the previous dependency models using fixed weights.
Anonymizing data with quasi-sensitive attribute values
Authors: Pu Shi, Li Xiong, Benjamin C. M. Fung
Venue: CIKM
Year: 2010

We study the problem of anonymizing data with quasi-sensitive attributes. Quasi-sensitive attributes are not sensitive by themselves, but certain values or their combinations may be linked to external knowledge to reveal indirect sensitive information of an individual. We formalize the notion of l-diversity and t-closeness for quasi-sensitive attributes, which we call QS l-diversity and QS t-closeness, to prevent indirect sensitive attribute disclosure. We propose a two-phase anonymization algorithm that combines quasi-identifying value generalization and quasi-sensitive value suppression to achieve QS l-diversity and QS t-closeness.
Combining link and content for collective active learning
Authors: Lixin Shi, Yuhang Zhao, Jie Tang
Venue: CIKM
Year: 2010

In this paper, we study a novel problem Collective Active Learning, in which we aim to select a batch set of "informative" instances from a networking data set to query the user in order to improve the accuracy of the learned classification model. We perform a theoretical investigation of the problem and present three criteria (i.e., minimum redundancy, maximum uncertainty and maximum impact) to quantify the informativeness of a set of selected instances. We define an objective function based on the three criteria and present an efficient algorithm to optimize the objective function with a bounded approximation rate. Experimental results on a real-world data sets demonstrate the effectiveness of our proposed approach.
Generating document summaries from user annotations
Authors: Karen Shiells, Omar Alonso, Ho John Lee
Venue: CIKM
Year: 2010

In this paper we analyze tweets that share the same link as another form of social annotation. By extracting all tweets that contain the same link over a period of time, we can generate a summary of what the crowd is writing about that particular link.
i-SEE: integrated stream execution environment over on-line data streams
Authors: Se Jung Shin, Hong Kyu Park, Ho Jin Woo, Won Suk Lee
Venue: CIKM
Year: 2010

The primary objective of various data stream applications is to monitor the on-going variations of data elements newly generated in data streams, so that appropriate services can be delivered to users timely. Basically, such monitoring activities can be divided into three categories: instant event monitoring, frequent behavior monitoring and analytic OLAP measures monitoring. Several prototype systems for data streams have been introduced but they are designated to support only the operations of one or two categories. However, many real-world data stream applications often require mixed operations of the three categories. This demonstration introduces an Integrated Stream Execution Environment (i-SEE) that can support the mixed execution of the operations of the three categories seamlessly, so that an i-SEE system can provide the multifaceted analytic results of on-line data streams continuously for various emerging applications.
VSEncoding: efficient coding and fast decoding of integer lists via dynamic programming
Authors: Fabrizio Silvestri, Rossano Venturini
Venue: CIKM
Year: 2010

Encoding lists of integers efficiently is important for many applications in different fields. Adjacency lists of large graphs are usually encoded to save space and to improve decoding speed. Inverted indexes of Information Retrieval systems keep the lists of postings compressed in order to exploit the memory hierarchy. Secondary indexes of DBMSs are stored similarly to inverted indexes in IR systems. In this paper we propose Vector of Splits Encoding (VSEncoding), a novel class of encoders that work by optimally partitioning a list of integers into blocks which are efficiently compressed by using simple encoders. In previous works heuristics were applied during the partitioning step. Instead, we find the optimal solution by using a dynamic programming approach. Experiments show that our class of encoders outperform all the existing methods in literature by more than 10% (with the exception of Binary Interpolative Coding with which they, roughly, tie) still retaining a very fast decompression algorithm.
PROSPECT: a system for screening candidates for recruitment
Authors: Amit Singh, Rose Catherine, Karthik Visweswariah, Vijil Chenthamarakshan, Nandakishore Kambhatla
Venue: CIKM
Year: 2010

Companies often receive thousands of resumes for each job posting and employ dedicated screeners to short list qualified applicants. In this paper, we present PROSPECT, a decision support tool to help these screeners shortlist resumes efficiently. Prospect mines resumes to extract salient aspects of candidate profiles like skills, experience in each skill, education details and past experience. Extracted information is presented in the form of facets to aid recruiters in the task of screening. We also employ Information Retrieval techniques to rank all applicants for a given job opening. In our experiments we show that extracted information improves our ranking by 30% there by making screening task simpler and more efficient.
Injecting domain knowledge into a granular database engine: a position paper
Authors: Dominik Slezak, Graham Toppin
Venue: CIKM
Year: 2010

We discuss how to use techniques from such fields as text processing and knowledge management to better handle text attributes in the Infobright's RDBMS engine. Our approach leads to a rich interface for domain experts who wish to share their knowledge about data content and, on the other hand, it remains unnoticeable to data users. It enables to improve data storage, data access, and data compression, with no changes required at the database schema level.
Learning naïve bayes transfer classifier throughclass-wise test distribution estimation
Authors: Jeong Woo Son, Seong-Bae Park, Hyun-Je Song
Venue: CIKM
Year: 2010

Text classification is a well-known problem for various applications. For last decades, it is beleived that a large corpus is one of the most important aspects for better classification. However, even though a great number of documents is available for training a classifier, it is practically impossible to achieve an ideal performance, since the distributions of labeled and unlabeled documents are often different. To overcome this problem, this paper describes a novel Naïve Bayes classifier for text classification under distribution difference between training and test data. The proposed method approximates test distribution by weighting labeled documents to cope with the distribution difference. Unlike other transfer learning which estimates the weights of labeled documents, the proposed method considerd both the documents and their estimated class labels. Therefore, the proposed method naturally combines the advantage of semi-supervised learning with those of transfer learning.