OpenRaiser/Intern-Atlas huggingface dataset:
Combining prestige and relevance ranking for personalized recommendation
Authors: Xiao Yang, Zhaoxin Zhang
Venue: CIKM
Year: 2013

In this paper, we present an adaptive graph-based personalized recommendation method based on combining prestige and relevance ranking. By utilizing the unique network structure of n-partite heterogeneous graph, we attempt to address the problem of personalized recommendation in a two-layer ranking process with the help of reasonable measure of high and low order relationships by analyzing the representation of user's preference in the graph. With different initialization and surfing strategies, this graph-based ranking model can take different type of data into account to capture personal interests from multiple perspectives. The experiments show that this algorithm can achieve better performance than the traditional CF methods and some graph-based recommendation methods.
Chinese syntactic parsing based on linguistic entity-relationship model
Authors: Dechun Yin
Venue: CIKM
Year: 2013

In this paper, we present a new parsing method for Chinese based on a newly proposed linguistic entity relationship model. In the model, we extract and define the linguistic entity relationship modes to describe the most basic syntactic and semantic structures of Chinese, and use the relationship modes as the foundation to implement the parsing algorithm. Compared with the rule-based and corpus-based methods, we neither manually write a large number of rules as used in traditional rule-based methods nor use the corpus to train the model. We only use the few meta-rules to describe the grammars in the parsing procedure. The system performance of syntactic parsing based on the model outperforms the corpus-based baseline system.
Large-scale deep learning at Baidu
Authors: Kai Yu
Venue: CIKM
Year: 2013

In the past 30 years, tremendous progress has been achieved in building effective shallow classification models. Despite the success, we come to realize that, for many applications, the key bottleneck is not the qualify of classifiers but that of features. Not being able to automatically get useful features has become the main limitation for shallow models. Since 2006, learning high-level features using deep architectures from raw data has become a huge wave of new learning paradigms. In recent two years, deep learning has made many performance breakthroughs, for example, in the areas of image understanding and speech recognition. In this talk, I will walk through some of the latest technology advances of deep learning within Baidu, and discuss the main challenges, e.g., developing effective models for various applications, and scaling up the model training using many GPUs. In the end of the talk I will discuss what might be interesting future directions.
Efficient processing of streaming graphs for evolution-aware clustering
Authors: Mindi Yuan, Kun-Lung Wu, Gabriela Jacques-Silva, Yi Lu
Venue: CIKM
Year: 2013

The clustering of vertices often evolves with time in a streaming graph, where graph update events are given as a stream of edge (vertex) insertions and deletions. Although a sliding window in stream processing naturally captures some cluster evolution, it alone may not be adequate, especially if the window size is large and the clustering within the windowed stream is unstable. Prior graph clustering approaches are mostly insensitive to clustering evolution. In this paper, we present an efficient approach to processing streaming graphs for evolution-aware clustering (EAC) of vertices. We incrementally manage individual connected components as clusters subject to a constraint on the maximal cluster size. For each cluster, we keep the relative recency of edges in a sorted order and favor more recent edges in clustering. We evaluate the effectiveness of EAC and compare it with a previous state-of-the-art evolution-insensitive clustering (EIC) approach. The results show that EAC is both effective and efficient in capturing evolution in a streaming graph. Moreover, we implement EAC as a streaming graph operator on IBM's InfoSphere Streams, a large-scale distributed middleware for stream processing, and show snapshots of the user cluster evolution in a streaming Twitter mention graph.
Exploring XML data is as easy as using maps
Authors: Yong Zeng, Zhifeng Bao, Guoliang Li, Tok Wang Ling
Venue: CIKM
Year: 2013

For keyword search on XML data, traditionally, a list of query results in the form of subtrees will be returned to users. However, we find that it is still not sufficient to meet users' information needs because: (1) the search intention of a certain keyword query varies from person to person; (2) amongst the query results, they may have sibling or containment relationships (in the context of whole XML database), which could be important for users to digest the query results and should be shown to users. Therefore, we try to equip the traditional XML keyword search engine with our new exploration model XMAP, providing user an interactive yet novel way to explore the results with better user experience.
Web-KR 2013: the 4th international workshop on web-scale knowledge representation, retrieval and reasoning
Authors: Yi Zeng, Spyros Kotoulas, Zhisheng Huang
Venue: CIKM
Year: 2013

As a continuous effort for organizing discussions and providing possible theories and techniques to deal with the barriers for knowledge processing at Web scale, the 2013 International Workshop on Web-scale Knowledge Representation, Retrieval and Reasoning (Web-KR 2013) was held in conjunction with the 2013 ACM International Conference on Information and Knowledge Management (CIKM 2013) on November 1st, 2013 at Burlingame, CA, United States. This is the 4th version of the Web-KR workshop. As in previous workshops under the same title, accepted papers of this workshop cover many important topics in the field. This year, the contributions focus on multi-faceted understanding of Web knowledge sources, Web entity linking, deep Web knowledge acquisition, and Web-scale stream reasoning. Many new approaches are proposed to deal with these problems in the context of large scale Web resources. This summary introduces the major contributions of accepted papers in the Web-KR 2013 workshop.
Mining a search engine's corpus without a query pool
Authors: Mingyang Zhang, Nan Zhang, Gautam Das
Venue: CIKM
Year: 2013

Many websites (e.g., WedMD.com, CNN.com) provide keyword search interfaces over a large corpus of documents. Meanwhile, many third parties (e.g., investors, analysts) are interested in learning big-picture analytical information over such a document corpus, but have no direct way of accessing it other than using the highly restrictive web search interface. In this paper, we study how to enable third-party data analytics over a search engine's corpus without the cooperation of its owner - specifically, by issuing a small number of search queries through the web interface.
Probabilistic solutions of influence propagation on social networks
Authors: Miao Zhang, Chunni Dai, Chris H. Q. Ding, Enhong Chen
Venue: CIKM
Year: 2013

Given fixed budgets, companies attempt to obtain maximum coverage on a social network by targeting at influential individuals. This viral marketing is often modeled by the independent cascade model. However, identifying the most influential people by computing influence spread is NP-hard, and various approximate algorithms are developed. In this paper, we emphasize the probabilistic nature of influence propagation. We propose to use exact probabilistic solutions and prove an inclusion-exclusion principle for computing influence spread. Our probabilistic solutions can significantly speed up the computation of influence spread. We also give a probabilistic-additive incremental search strategy to solve the influence maximization problem, i.e., to find a subset of individuals that has the largest influence spread in the end. Experiments on real data sets demonstrated the effectiveness and efficiency of our methods.
Clustering-based transduction for learning a ranking model with limited human labels
Authors: Xin Zhang, Ben He, Tiejian Luo, Dongxing Li, Jungang Xu
Venue: CIKM
Year: 2013

Transductive learning is a semi-supervised learning paradigm that can leverage unlabeled data by creating pseudo labels for learning a ranking model, when there is only limited or no training examples available. However, the effectiveness of transductive learning in information retrieval (IR) can be hindered by the low quality pseudo labels. To this end, we propose to incorporate a two-step k-means clustering algorithm to select the high quality training queries for generating the pseudo labels. In particular, the first step selects the high-quality queries for which the relevant documents are highly coherent as indicated by the clustering results. The second step then selects the initial training examples for the transductive learning that iteratively aggregating the pseudo examples. Finally, the learning to rank (LTR) algorithms are applied to learn the ranking model using the pseudo training examples created by the transductive learning process. Our proposed approach is particularly suitable for applications where there is only little or no human labels available as it does not necessarily involve the use of relevance assessments information or human efforts. Experimental results on the standard TREC Tweets11 collection show that our proposed approach outperforms strong baselines, namely the conventional applications of learning to rank algorithms using human labels for the training and transductive learning using all the queries available.
Short text classification by detecting information path
Authors: Shitao Zhang, Xiaoming Jin, Dou Shen, Bin Cao, Xuetao Ding, Xiaochen Zhang
Venue: CIKM
Year: 2013

Short text is becoming ubiquitous in many modern information systems. Due to the shortness and sparseness of short texts, there are less informative word co-occurrences among them, which naturally pose great difficulty for classification tasks on such data. To overcome this difficulty, this paper proposes a new way for effectively classifying the short texts. Our method is based on a key observation that there usually exists ordered subsets in short texts, which is termed ``information path'' in this work, and classification on each subset based on the classification results of some pervious subsets can yield higher overall accuracy than classifying the entire data set directly. We propose a method to detect the information path and employ it in short text classification. Different from the state-of-art methods, our method does not require any external knowledge or corpus that usually need careful fine-tuning, which makes our method easier and more robust on different data sets. Experiments on two real world data sets show the effectiveness of the proposed method and its superiority over the existing methods.
Map search via a factor graph model
Authors: Qi Zhang, Jihua Kang, Yeyun Gong, Huan Chen, Yaqian Zhou, Xuanjing Huang
Venue: CIKM
Year: 2013

Map search has received considerable attention in recent years. With map search, users can specify target locations with textual queries. However, these queries do not always include well-formed addresses or place names. They may contain transpositions, misspellings, fragments and so on. Queries may significantly differ from items stored in the spatial database. In this paper, we propose to connect this task to the semi-structured retrieval problem. A novel factor graph-based semi-structured retrieval framework is introduced to incorporate concept weighting, attribute selection, and word-based similarity metrics together. We randomly sampled a number of queries from logs of a commercial map search engine and manually labeled their categories and relevant results for analysis and evaluation. The results of several experimental comparisons demonstrate that our method outperforms both state-of-the-art semi-structured retrieval methods and some commercial systems in retrieving freeform location queries.
An efficient and robust privacy protection technique for massive streaming choice-based information
Authors: Ji Zhang, Xuemei Liu, Yonglong Luo
Venue: CIKM
Year: 2013

Protecting users' privacy when transmitting a large amount of data over the Internet is becoming increasingly important nowadays. In this paper, we focus on the streaming choice-based information and propose a novel anonymization technique for providing a strong privacy protection to safeguard against privacy disclosure and information tampering. Our technique utilizes an innovative two-phase encoding-and-decoding approach which is very easy to implement, highly efficient in terms of speed and communication, and is robust against possible tampering from adversaries. The experimental evaluation demonstrates the promising performance of our technique.
Learning to detect task boundaries of query session
Authors: Zhenzhong Zhang, Le Sun, Xianpei Han
Venue: CIKM
Year: 2013

To accomplish a search task and satisfy a single information need, users usually submit a series of queries to web search engines. It is useful for web search engines to detect the task boundaries in a series of successive queries. Traditional task boundary detection methods are based on time gap and lexical comparisons, which often suffer from the vocabulary gap problem, that is, the topically related queries may not share any common words. In this paper we learn hidden topics from query log and leverage them to resolve the vocabulary gap problem. Unlike other external knowledge resources, such as WordNet and Wikipedia, the hidden topics discovered from query log cover long tail queries, which is useful to detect task boundaries. Experimental results on dataset from real world query log demonstrate that the proposed method achieves significant quality enhancement.
Community-based user recommendation in uni-directional social networks
Authors: Gang Zhao, Mong-Li Lee, Wynne Hsu, Wei Chen, Haoji Hu
Venue: CIKM
Year: 2013

Advances in Web 2.0 technology has led to the rising popularity of many social network services. For example, there are over 500 million active users in Twitter. Given the huge number of users, user recommendation has gained importance where the goal is to find a set of users whom a target user is likely to follow. Content-based approaches that rely on tweet content for user recommendation have low precision as tweet contents are typically short and noisy, while collaborative filtering approaches that utilize follower-followee relationships lead to higher precision but data sparsity remains a challenge. In this work, we propose a community-based approach to user recommendation in Twitter-style social networks. Forming communities enables us to reduce data sparsity as the focus is on discover the latent characteristics of communities instead of individuals. We employ an LDA-based method on the follower-followee relationships to discover communities before applying the state-of-the-art matrix factorization method on each of the communities. This approach proves effective in improving the conversion rate (by as much as 20%) as demonstrated by the results of extensive experiments on two real world data sets Twitter and Weibo. In addition, the community-based approach is scalable as the individual community can be analyzed separately.
Consumer-centric SLA manager for cloud-hosted databases
Authors: Liang Zhao, Sherif Sakr, Anna Liu
Venue: CIKM
Year: 2013

We present an end-to-end framework for consumer-centric SLA management of virtualized database servers. The framework facilitates adaptive and dynamic provisioning of the database tier of the software applications based on application-defined policies for satisfying their own SLA performance requirements, avoiding the cost of any SLA violation and controlling the monetary cost of the allocated computing resources. In this framework, the SLA of the consumer applications are declaratively defined in terms of goals which are subjected to a number of constraints that are specific to the application requirements. The framework continuously monitors the application-defined SLA and automatically triggers the execution of necessary corrective actions (scaling out/in the database tier) when required. The framework is database platform-agnostic, uses virtualization-based database replication mechanisms and requires zero source code changes of the cloud-hosted application.
Originator or propagator?: incorporating social role theory into topic models for twitter content analysis
Authors: Wayne Xin Zhao, Jinpeng Wang, Yulan He, Jian-Yun Nie, Xiaoming Li
Venue: CIKM
Year: 2013

A large number of studies have been devoted to modeling the contents and interactions between users on Twitter. In this paper, we propose a method inspired from Social Role Theory (SRT), which assumes that a user behaves differently with different roles in the generation process of Twitter content. We consider the two most distinctive social roles on Twitter: originator and propagator, who respectively posts original messages and retweets or forwards the messages from others. In addition, we also consider role-specific social interactions, especially implicit interactions between users who share some common interests. All the above elements are integrated into a novel regularized topic model. We evaluate the proposed method on real Twitter data. The results show that our method is more effective than the existing ones which do not distinguish social roles.
Interactive collaborative filtering
Authors: Xiaoxue Zhao, Weinan Zhang, Jun Wang
Venue: CIKM
Year: 2013

In this paper, we study collaborative filtering (CF) in an interactive setting, in which a recommender system continuously recommends items to individual users and receives interactive feedback. Whilst users enjoy sequential recommendations, the recommendation predictions are constantly refined using up-to-date feedback on the recommended items. Bringing the interactive mechanism back to the CF process is fundamental because the ultimate goal for a recommender system is about the discovery of interesting items for individual users and yet users' personal preferences and contexts evolve over time during the interactions with the system. This requires us not to distinguish between the stages of collecting information to construct the user profile and making recommendations, but to seamlessly integrate these stages together during the interactive process, with the goal of maximizing the overall recommendation accuracy throughout the interactions. This mechanism naturally addresses the cold-start problem as any user can immediately receive sequential recommendations without providing ratings beforehand. We formulate the interactive CF with the probabilistic matrix factorization (PMF) framework, and leverage several exploitation-exploration algorithms to select items, including the empirical Thompson sampling and upper confidence bound based algorithms. We conduct our experiment on cold-start users as well as warm-start users with drifting taste. Results show that the proposed methods have significant improvements over several strong baselines for the MovieLens, EachMovie and Netflix datasets.
Graph similarity search with edit distance constraint in large graph databases
Authors: Weiguo Zheng, Lei Zou, Xiang Lian, Dong Wang, Dongyan Zhao
Venue: CIKM
Year: 2013

Due to many real applications of graph databases, it has become increasingly important to retrieve graphs g (in graph database D) that approximately match with query graph q, rather than exact subgraph matches. In this paper, we study the problem of graph similarity search, which retrieves graphs that are similar to a given query graph under the constraint of the minimum edit distance. Specifically, we derive a lower bound, branch-based bound, which can greatly reduce the search space of the graph similarity search. We also propose a tree index structure, namely b-tree, to facilitate effective pruning and efficient query processing. Extensive experiments confirm that our proposed approach outperforms the existing approaches by orders of magnitude, in terms of both pruning power and query response time.
Improving entity search over linked data by modeling latent semantics
Authors: Nikita Zhiltsov, Eugene Agichtein
Venue: CIKM
Year: 2013

Entity ranking has become increasingly important, both for retrieving structured entities and for use in general web search applications. The most common format for linked data, RDF graphs, provide extensive semantic structure via predicate links. While the semantic information is potentially valuable for effective search, the resulting adjacency matrices are often sparse, which introduces challenges for representation and ranking. In this paper, we propose a principled and scalable approach for integrating of latent semantic information into a learning-to-rank model, by combining compact representation of semantic similarity, achieved by using a modified algorithm for tensor factorization, with explicit entity information. Our experiments show that the resulting ranking model scales well to the graphs with millions of entities, and outperforms the state-of-the-art baseline on realistic Yahoo! SemSearch Challenge data sets.
G-tree: an efficient index for KNN search on road networks
Authors: Ruicheng Zhong, Guoliang Li, Kian-Lee Tan, Lizhu Zhou
Venue: CIKM
Year: 2013

In this paper we study the problem of kNN search on road networks. Given a query location and a set of candidate objects in a road network, the kNN search finds the k nearest objects to the query location. To address this problem, we propose a balanced search tree index, called G-tree. The G-tree of a road network is constructed by recursively partitioning the road network into sub-networks and each G-tree node corresponds to a sub-network. Inspired by classical kNN search on metric space, we introduce a best-first search algorithm on road networks, and propose an elaborately-designed assembly-based method to efficiently compute the minimum distance from a G-tree node to the query location. G-tree only takes O(|V|log|V|) space, where |V| is the number of vertices in a network, and thus can easily scale up to large road networks with more than 20 millions vertices. Experimental results on eight real-world datasets show that our method significantly outperforms state-of-the-art methods, even by 2-3 orders of magnitude.
Semantic discovery from web comparison queries
Authors: Tingting Zhong, Wensheng Wu
Venue: CIKM
Year: 2013

Users frequently pose comparison queries (e.g., ibm vs apple) on web search engines. However, little research has been done on understanding these queries. To fill in this gap, this paper describes a first solution to discovering and mining comparison queries. We present a novel snowballing algorithm that "crawls" comparison queries from search engines via their query autocompletion services. We propose a novel modeling approach that represents comparison queries in a comparison graph and develop a novel algorithm that mines closely related concepts from comparison graphs via spectral clustering. Initial experiments indicate that our approach can reveal the inherent semantic relationship among the concepts and discover different senses of a concept, e.g., "toyota" as a car brand or a company name.