OpenRaiser/Intern-Atlas huggingface dataset:
Scalable collaborative filtering using incremental update and local link prediction
Authors: Xiao Yang, Zhaoxin Zhang, Ke Wang
Venue: CIKM
Year: 2012

The traditional collaborative filtering approaches have been shown to suffer from two fundamental problems: data sparsity and difficulty in scalability. To address these problems, we present a novel scalable item-based collaborative filtering method by using incremental update and local link prediction. By subdividing the computations and analyzing the factors in different cases of item-to-item similarity, we design the incremental update strategies in item-based CF, which can make the recommender system more efficient and scalable. Based on the transitive structure of item similarity graph, we use the local link prediction method to find implicit candidates to alleviate the lack of neighbors in predictions and recommendations caused by the sparsity of data. The experiment results validate that our algorithm can improve the performance of traditional CF, and can increase the efficiency in recommendations.
Accelerating locality preserving nonnegative matrix factorization
Authors: Guanhong Yao, Deng Cai
Venue: CIKM
Year: 2012

Matrix factorization techniques have been frequently applied in information retrieval, computer vision and pattern recognition. Among them, Non-negative Matrix Factorization (NMF) has received considerable attention due to its psychological and physiological interpretation of naturally occurring data whose representation may be parts-based in the human brain. Locality Preserving Non-negative Matrix Factorization (LPNMF) is a recently proposed graph-based NMF extension which tries to preserves the intrinsic geometric structure of the data. Compared with the original NMF, LPNMF has more discriminating power on data representa- tion thanks to its geometrical interpretation and outstanding ability to discover the hidden topics. However, the computa- tional complexity of LPNMF is O(n3), where n is the number of samples. In this paper, we propose a novel approach called Accelerated LPNMF (A-LPNMF) to solve the com- putational issue of LPNMF. Specifically, A-LPNMF selects p (p j n) landmark points from the data and represents all the samples as the sparse linear combination of these landmarks. The non-negative factors which incorporates the geometric structure can then be efficiently computed. Experimental results on the real data sets demonstrate the effectiveness and efficiency of our proposed method.
Exploiting latent relevance for relational learning of ubiquitous things
Authors: Lina Yao, Quan Z. Sheng
Venue: CIKM
Year: 2012

With recent advances in radio-frequency identification (RFID), wireless sensor networks, and Web services, physical things are becoming an integral part of the emerging ubiquitous Web. While this integration offers many exciting opportunities such as efficient supply chains and improved environmental monitoring, it also presents many significant challenges. One such challenge lies in how to classify, discover, and manage ubiquitous things, which is critical for efficient and effective object search, recommendation, and composition. In this paper, we focus on automatically classifying ubiquitous things into manageable semantic category labels by exploiting the information hidden in interactions between users and ubiquitous things. We develop a novel approach to extract latent relevance by building a relational network of ubiquitous things (RNUbiT) where similar things are linked via virtual edges according to their latent relevance. A discriminative learning algorithm is also developed to automatically determine category labels for ubiquitous things. We conducted experiments using real-world data and the experimental results demonstrate the feasibility and validity of our proposed approach.
A tag-centric discriminative model for web objects classification
Authors: Lina Yao, Quan Z. Sheng
Venue: CIKM
Year: 2012

This paper studies web object classification problem with the novel exploration of social tags. More and more web objects are increasingly annotated with human interpretable labels (i.e., tags), which can be considered as an auxiliary attribute to assist the object classification. Automatically classifying web objects into manageable semantic categories has long been a fundamental pre-process for indexing, browsing, searching, and mining heterogeneous web objects. However, such heterogeneous web objects often suffer from a lack of easy-extractable and uniform descriptive features. In this paper, we propose a discriminative tag-centric model for web object classification by jointly modeling the objects category labels and their corresponding social tags and un-coding the relevance among social tags. Our approach is based on recent techniques for learning large-scale discriminative models. We conduct experiments to validate our approach using real-life data. The results show the feasibility and good performance of our approach.
Finding influential products on social domination game
Authors: Jinyoung Yeo, Jin-Woo Park, Seung-won Hwang
Venue: CIKM
Year: 2012

In this paper, we propose a new type of market model called the social domination game model. Given a set C of customers and a set P of products, this model simulates market competition among P and estimates market shares, considering both the dominance relation between C and P and the influence relation among the members of C. With this model, we propose a greedy product positioning algorithm for designing a new product that approximately maximizes market share. Our experimental results show that the proposed algorithm creates a new product gaining up to 97.5% market share of the best product's market share obtained by the exact method, while significantly outperforming the exact method in terms of running time, i.e., by up to two orders of magnitude.
ESA: emergency situation awareness via microbloggers
Authors: Jie Yin, Sarvnaz Karimi, Bella Robinson, Mark A. Cameron
Venue: CIKM
Year: 2012

During a disastrous event, such as an earthquake or river flooding, information on what happened, who was affected and how, where help is needed, and how to aid people who were affected, is crucial. While communication is important in such times of crisis, damage to infrastructure such as telephone lines makes it difficult for authorities and victims to communicate. Microblogging has played a critical role as an important communication platform during crises when other media has failed. We demonstrate our ESA (Emergency Situation Awareness) system that mines microblogs in real-time to extract and visualise useful information about incidents and their impact on the community in order to equip the right authorities and the general public with situational awareness.
Query-focused multi-document summarization based on query-sensitive feature space
Authors: Wenpeng Yin, Yulong Pei, Fan Zhang, Lian&apos, en Huang
Venue: CIKM
Year: 2012

Query-oriented relevance, information richness and novelty are important requirements in query-focused summarization, which, to a considerable extent, determine the summary quality. Previous work either rarely took into account all above demands simultaneously or dealt with part of them in the dynamic process of choosing sentences to generate a summary. In this paper, we propose a novel approach that integrates all these requirements skillfully by treating them as sentence features, making that the finally generated summary could fully reflect the combinational effect of these properties. Experimental results on the DUC2005 and DUC2006 datasets demonstrate the effectiveness of our approach.
Multifaceted analysis of news articles by using semantic annotated information
Authors: Masaharu Yoshioka, Noriko Kando
Venue: CIKM
Year: 2012

We propose a novel framework that enables multifaceted analysis of news articles. This system uses semantic annotated information (e.g., person, place) as facets and can be used to construct structured queries for comparing the differences between sets of articles.
Role-explicit query identification and intent role annotation
Authors: Haitao Yu, Fuji Ren
Venue: CIKM
Year: 2012

Understanding the information need or intent encoded within a query has long been regarded as an essential factor of effective information retrieval. For better query representation and understanding, two intent roles (kernel-object and modifier) are introduced to structurally parse a class of role-explicit queries, which constitute a majority of common user queries. Furthermore, we focus on two research problems: RP-1: Given a role-explicit query, how to identify the kernel-object and modifier, namely intent role annotation; RP-2: How to determine whether an arbitrary query is role-explicit or not. To solve RP-1, we propose a simplified word n-gram role model (SWNR), which quantifies the generating probability of a role-explicit query and performs intent role annotation effectively. Using a set of discriminative features, we build classifiers to address RP-2 in a supervised manner. The experimental results show that: (1) SWNR can achieve a satisfactory performance, more than 73% in terms of different metrics; (2) The classifiers can achieve more than 90% precision in identifying role-explicit queries; (3) Compared with traditional techniques for query representation and understanding, e.g., name entity recognition in query and class-level query intent inference, intent role annotation provides a more flexible framework and a number of applications can benefit from annotating role-explicit queries, such as intent mining and diversified document ranking.
User guided entity similarity search using meta-path selection in heterogeneous information networks
Authors: Xiao Yu, Yizhou Sun, Brandon Norick, Tiancheng Mao, Jiawei Han
Venue: CIKM
Year: 2012

With the emergence of web-based social and information applications, entity similarity search in information networks, aiming to find entities with high similarity to a given query entity, has gained wide attention. However, due to the diverse semantic meanings in heterogeneous information networks, which contain multi-typed entities and relationships, similarity measurement can be ambiguous without context. In this paper, we investigate entity similarity search and the resulting ambiguity problems in heterogeneous information networks. We propose to use a meta-path-based ranking model ensemble to represent semantic meanings for similarity queries, exploit the possibility of using using user-guidance to understand users query. Experiments on real-world datasets show that our framework significantly outperforms competitor methods.
Search tactics as means of examining search processes in collaborative exploratory web search
Authors: Zhen Yue, Shuguang Han, Jiepu Jiang, Daqing He
Venue: CIKM
Year: 2012

Investigations on search processes can either be at macro-level or micro-level. The macro-level investigation focuses on establishing theoretical models while micro-level investigation focuses on the user actions. In this paper, we propose using search tactics as means of examining search processes in collaborative exploratory search, which can be seen as a middle layer between the macro-level and micro-level. We reported a user study in which participants worked on exploratory Web search tasks using our CollabSearch system under two conditions: collaborative and individual. Using the two methods - action pairs and Hidden Markov Model (HMM) to model search tactics, we discovered some insights about the different patterns of search tactics that participants explored under collaborative and individual search conditions. In the future, we plan to improve the HMM model as a semi-automatic way to detect search tactics. In this study, we further analyze a particular search tactic - query reformulation in collaborative search and discovered the possible sources of new query terms. In the future, we plan to implement a query suggestion algorithm to better support team users' query reformulation tactics. This study provides some guidance for designers on the range of behaviors and activities that a collaborative search system should support. Further studies are needed to fully understand them.
Where do the query terms come from?: an analysis of query reformulation in collaborative web search
Authors: Zhen Yue, Jiepu Jiang, Shuguang Han, Daqing He
Venue: CIKM
Year: 2012

This paper presents a user study aiming to investigate the query reformulation in collaborative Web search. 7 pairs of participants were recruited and each pair worked as a team on two collaborative exploratory Web search tasks. Through the log analysis, we compared possible sources for participants to draw query terms from. The results show that both search and collaborative actions are possible resources for new query terms. Traditional resources for query expansion such as previous search histories and relevant documents are still important resources for new query terms. The content in chat and workspace generated by participants themselves seems more likely to be the resource for new query terms than that of their partners. Task types also affect the influences on query reformulations. For the academic task, previously saved relevance documents are the most important resources for new query terms while chat histories are the most important resources for the leisure task.
Ranking and combining social network data for web personalization
Authors: Yi Zeng, Hongwei Hao, Ning Zhong, Xu Ren, Yan Wang
Venue: CIKM
Year: 2012

Various Web-based social network data reflect user interests from multiple perspectives in a distributed environment. They need to be integrated for better user modelling and personalized services. We argue that in different scenarios, different social networks play different roles and their degrees of importance are not equivalent. Hence, ranking strategies among different social network data sources are needed. In addition, combining different social network data can produce interesting subsets of these data with different levels of importance. In this paper, we propose social network data ranking and composition strategies, we validate the proposed methods by collaboration network data (Semantic Web Dog Food) and micro-blogging data (from Twitter), then we use the ranked and composed results for developing a Web-based personalized academic visit recommendation system to show their potential effectiveness.
Finding top k most influential spatial facilities over uncertain objects
Authors: Liming Zhan, Ying Zhang, Wenjie Zhang, Xuemin Lin
Venue: CIKM
Year: 2012

Uncertainty is inherent in many important applications, such as location-based services (LBS), sensor monitoring and radio-frequency identification (RFID). Recently, considerable research efforts have been put into the field of uncertainty-aware spatial query processing. In this paper, we study the problem of finding top k most influential facilities over a set of uncertain objects, which is an important spatial query in the above applications. Based on the maximal utility principle, we propose a new ranking model to identify the top k most influential facilities, which carefully captures influence of facilities on the uncertain objects. By utilizing two uncertain object indexing techniques, R-tree and U-Quadtree, effective and efficient algorithms are proposed following the filtering and verification paradigm, which significantly improves the performance of the algorithms in terms of CPU and I/O costs. Comprehensive experiments on real datasets demonstrate the effectiveness and efficiency of our techniques.
Query-biased learning to rank for real-time twitter search
Authors: Xin Zhang, Ben He, Tiejian Luo, Baobin Li
Venue: CIKM
Year: 2012

By incorporating diverse sources of evidence of relevance, learning to rank has been widely applied to real-time Twitter search, where users are interested in fresh relevant messages. Such approaches usually rely on a set of training queries to learn a general ranking model, which we believe that the benefits brought by learning to rank may not have been fully exploited as the characteristics and aspects unique to the given target queries are ignored. In this paper, we propose to further improve the retrieval performance of learning to rank for real-time Twitter search, by taking the difference between queries into consideration. In particular, we learn a query-biased ranking model with a semi-supervised transductive learning algorithm so that the query-specific features, e.g. the unique expansion terms, are utilized to capture the characteristics of the target query. This query-biased ranking model is combined with the general ranking model to produce the final ranked list of tweets in response to the given target query. Extensive experiments on the standard TREC Tweets11 collection show that our proposed query-biased learning to rank approach outperforms strong baseline, namely the conventional application of the state-of-the-art learning to rank algorithms.
Relation regularized subspace recommending for related scientific articles
Authors: Qing Zhang, Jianwu Li, Zhiping Zhang, Li Wang
Venue: CIKM
Year: 2012

Recommending related scientific articles for a researcher is very important and useful in practice but also is full of challenges due to the latent complex semantic relations among scientific literatures. To deal with these challenges, this paper proposes a novel framework with link-missing data adaption, which casts the recommendation task to subspace embedding and similarity ranking problems. The relation regularized subspace in this framework is constructed via Relation Regularized Matrix Factorization (RRMF) for well modeling both content and link structure simultaneously. However, the link structure for an article is not always available in practical recommending. To solve this problem, we further propose two alternative approaches based on Latent Dirichlet Allocation (LDA) for link-missing articles recommendation as an extension of RRMF. Experiments on CiteSeer dataset demonstrate our method is more effective in comparison with some state-of-the-art approaches and is able to handle the link-missing case which the link-based methods never can fit.
Evaluating geo-social influence in location-based social networks
Authors: Chao Zhang, Lidan Shou, Ke Chen, Gang Chen, Yijun Bei
Venue: CIKM
Year: 2012

The emerging location-based social network (LBSN) services not only allow people to maintain cyber links with their friends, but also enable them to share the events happening on them at different locations. The geo-social correlations among event participants make it possible to quantify mutual user influence for various events. Such a quantification of influence could benefit a wide spectrum of real-life applications such as targeted advertising and viral marketing.
Credibility-based product ranking for C2C transactions
Authors: Rong Zhang, Chaofeng Sha, Minqi Zhou, Aoying Zhou
Venue: CIKM
Year: 2012

A fundamental issue for C2C transactions is how to rank the products based on the reviews written by the previous customers. In this paper, we present an approach to improve products ranking by tackling the noisy ratings that exist in the practical systems. The first problem is the credibility of the customers. We design an iterative algorithm to measure the customer credibility. In the algorithm, we use a feedback strategy to increase or decrease the customer credibility. We increase the credibility for a customer if the customer gives a high (low) score to a good (bad) product and decrease the value if the customer gives a low (high) score to a good (bad) product. The second problem is the inconsistency between the review comments and scores. To deal with it, we train a classifier on a training data that is constructed automatically. The trained classifier is used to predict the scores of the comments. Finally, we calculate the scores of products by considering the customer credibility and the predicted scores. The experimental results show that our proposed approach provides better products ranking than the baseline systems.
Selecting expansion terms as a set via integer linear programming
Authors: Qi Zhang, Yan Wu, Xuanjing Huang
Venue: CIKM
Year: 2012

Pseudo-relevance feedback via query expansion has been widely studied from various perspectives in the past decades. Its effectiveness in improving retrieval effectiveness has been shown in many tasks. A variety of criteria were proposed to select additional terms for the original queries. However, most of the existing methods weight and select terms individually and do not consider the impact of term-to-term relationship. In this paper, we first examine the influence of combinations of terms through data analysis, which demonstrate the significant effect of term-to-term relationship on retrieval effectiveness. Then, to address this problem, we formalize the query expansion task as an integer linear programming (ILP) problem. The model combines the weights learned from a supervised method for individual terms, and integrates constraints to capture relations between terms. Finally, three standard TREC collections are used to evaluate the proposed method. Experimental results demonstrate that the proposed method can significantly improve the effectiveness of retrieval.
An interaction framework of service-oriented ontology learning
Authors: Jingsong Zhang, Yinglin Wang, Hao Wei
Venue: CIKM
Year: 2012

Ontology plays a very important role in supporting knowledge-based applications. In cloud computing, ontology learning technology is facing new challenges in dealing with heterogeneous data sources from different domains and researchers, which may contain various particular concepts and relations. Traditional ontology learning frameworks usually focus only on the extraction of concepts and taxonomic relations from the multi-structured corpus. However, former researches rarely studied the interactions during ontology learning process among different researchers. Lack of interactions among people who build ontology in different domains may cause inconsistent ontology. Besides, lack of incentive during the ontology building process will also result in low efficiency. To address these challenges, this paper specifies a novel solution to perform ontology learning. The solution includes a service-oriented ontology interaction framework, a service-oriented ontology learning strategy. It shows that it advances ontology learning to a higher level of performance and portability with a number of experiments in demo system.
A model-based approach for RFID data stream cleansing
Authors: Zhou Zhao, Wilfred Ng
Venue: CIKM
Year: 2012

In recent years, RFID technologies have been used in many applications, such as inventory checking and object tracking. However, raw RFID data are inherently unreliable due to physical device limitations and different kinds of environmental noise. Currently, existing work mainly focuses on RFID data cleansing in a static environment (e.g. inventory checking). It is therefore difficult to cleanse RFID data streams in a mobile environment (e.g. object tracking) using the existing solutions, which do not address the data missing issue effectively.
Exploiting concept hierarchy for result diversification
Authors: Wei Zheng, Hui Fang, Conglei Yao
Venue: CIKM
Year: 2012

The goal of result diversification is to maximize the coverage of query subtopics while minimizing the redundancy in the search results. Intuitively, it is more desirable for a diversification system to cover independent subtopics since it would retrieve sets of non-overlapped relevant documents, which leads to less redundancy in the search results. Unfortunately, existing diversification methods assume that query subtopics are independent and ignore their relations in the diversification process. To overcome this limitation, we propose to exploit concept hierarchies to extract query subtopics and infer their relations. We then apply axiomatic approaches to derive a structural diversification method that can leverage the subtopic relations in result diversification. Experimental results over an enterprise collection show that the relations among query subtopics are useful to improve the diversification performance.