Popular online publishers produce huge amount of news articles every day, so it is important to summarize the most up-to-the-minute information to help users quickly know the progresses of their interested news events. In this paper, we develop NewsCubeSum, a novel personalized news summarization system utilizing OLAP and supervised sentence selection techniques to generate brief summaries delivering news updates in multiple dimensions (such as time, entity, and topic). An illustrative case study and experimental results on summarization performance comparisons are provided to show the effectiveness of NewsCubeSum.

Patent document retrieval, as a recall-orientated search task, does not allow missing relevant patent documents due to the great commercial value of patents and significant costs of processing a patent application or patent infringement case. Thus, it is important to retrieve all possible relevant documents rather than only a small subset of patents from the top ranked results. However, patents are often lengthy and rich in technical terms, and it often requires enormous human efforts to compare a given document with retrieved results. In this paper, we formulate the problem of comparing patent documents as a comparative summarization problem, and explore automatic strategies that generate comparative summaries to assist patent analysts in quickly reviewing any given patent document pairs. To this end, we present a novel approach, named PatentCom, which first extracts discriminative terms from each patent document, and then connects the dots on a term co-occurrence graph. In this way, we are able to comprehensively extract the gists of the two patent documents being compared, and meanwhile highlight their relationship in terms of commonalities and differences. Extensive quantitative analysis and case studies on real world patent documents demonstrate the effectiveness of our proposed approach.

Personalized recommendation services have gained increasing popularity and attention in recent years as most useful information can be accessed online in real-time. Most online recommender systems try to address the information needs of users by virtue of both user and content information. Despite extensive recent advances, the problem of personalized recommendation remains challenging for at least two reasons. First, the user and item repositories undergo frequent changes, which makes traditional recommendation algorithms ineffective. Second, the so-called cold-start problem is difficult to address, as the information for learning a recommendation model is limited for new items or new users. Both challenges are formed by the dilemma of exploration and exploitation. In this paper, we formulate personalized recommendation as a contextual bandit problem to solve the exploration/exploitation dilemma. Specifically in our work, we propose a parameter-free bandit strategy, which employs a principled resampling approach called online bootstrap, to derive the distribution of estimated models in an online manner. Under the paradigm of probability matching, the proposed algorithm randomly samples a model from the derived distribution for every recommendation. Extensive empirical experiments on two real-world collections of web data (including online advertising and news recommendation) demonstrate the effectiveness of the proposed algorithm in terms of the click-through rate. The experimental results also show that this proposed algorithm is robust in the cold-start situation, in which there is no sufficient data or knowledge to tune the hyper-parameters.

Social media has become increasingly prevalent in the last few years, not only enabling people to connect with each other by social links, but also providing platforms for people to share information and interact over diverse topics. Rich user-generated information, for example, users’ relationships and daily posts, are often available in most social media service websites. Given such information, a challenging problem is to provide reasonable user and community recommendation for a target user, and consequently, help the target user engage in the daily discussions and activities with his/her friends or like-minded people. In this article, we propose a unified framework of recommending users and communities that utilizes the information in social media. Given a user’s profile or a set of keywords as input, our framework is capable of recommending influential users and topic-cohesive interactive communities that are most relevant to the given user or keywords. With the proposed framework, users can find other individuals or communities sharing similar interests, and then have more interaction with these users or within the communities. We present a generative topic model to discover user-oriented and community-oriented topics simultaneously, which enables us to capture the exact topical interests of users, as well as the focuses of communities. Extensive experimental evaluation and case studies on a dataset collected from Twitter demonstrate the effectiveness of our proposed framework compared with other probabilistic-topic-model-based recommendation methods.

Inventory management refers to tracing inventory levels, orders and sales of a retailing business. In the current retailing market, a tremendous amount of data regarding stocked goods (items) in an inventory will be generated everyday. Due to the increasing volume of transaction data and the correlated relations of items, it is often a non-trivial task to efficiently and effectively manage stocked goods. In this demo, we present an intelligent system, called iMiner, to ease the management of enormous inventory data. We utilize distributed computing resources to process the huge volume of inventory data, and incorporate the latest advances of data mining technologies into the system to perform the tasks of inventory management, e.g., forecasting inventory, detecting abnormal items, and analyzing inventory aging. Since 2014, iMiner has been deployed as the major inventory management platform of ChangHong Electric Co., Ltd, one of the world's largest TV selling companies in China.

The fast growth of technologies has driven the advancement of our society. It is often necessary to quickly grasp the linkage between different technologies in order to better understand the technical trend. The availability of huge volumes of granted patent documents provides a reasonable basis for analyzing the relationships between technologies. In this paper, we propose a unified framework, named PatentDom, to identify important patents related to key techniques from a large number of patent documents. The framework integrates different types of patent information, including patent content, citations of patents, and temporal relations, and provides a concise yet comprehensive technology summary. The identified key patents enable a variety of patent-related analytical applications, e.g., outlining the technology evolution of a particular domain, tracing a given technique to prior technologies, and mining the technical connection of two given patent documents. Empirical analysis and extensive case studies on a collection of US patent documents demonstrate the efficacy of our proposed framework.

Advanced manufacturing such as aerospace, semi-conductor, and flat display device often involves complex production processes, and generates large volume of production data. In general, the production data comes from products with different levels of quality, assembly line with complex flows and equipments, and processing craft with massive controlling parameters. The scale and complexity of data is beyond the analytic power of traditional IT infrastructures. To achieve better manufacturing performance, it is imperative to explore the underlying dependencies of the production data and exploit analytic insights to improve the production process. However, few research and industrial efforts have been reported on providing manufacturers with integrated data analytical solutions to reveal potentials and optimize the production process from data-driven perspectives.

The cold-start problem has attracted extensive attention among various online services that provide personalized recommendation. Many online vendors employ contextual bandit strategies to tackle the so-called exploration/exploitation dilemma rooted from the cold-start problem. However, due to high-dimensional user/item features and the underlying characteristics of bandit policies, it is often difficult for service providers to obtain and deploy an appropriate algorithm to achieve acceptable and robust economic profit.

The fast growth of technologies has driven the advancement of our society. It is often necessary to quickly grab the evolution of technologies in order to better understand the technology trend. The availability of huge volumes of granted patent documents provides a reasonable basis for analyzing technology evolution. In this paper, we propose a unified framework, named PatentLine, to generate a technology evolution tree for a given topic or a classification code related to granted patents. The framework integrates different types of patent information, including patent content, citations of patents, temporal relations, etc., and provides a concise yet comprehensive evolution summary. The generated summary enables a variety of patent-related analyses such as identifying relevant prior art and detecting technology gap. A case study on a collection of US patents demonstrates the efficacy of our proposed framework.

Domain ontology, as a conceptual model, provides a meaningful framework for semantic representation of textual information. In this paper, we explore the feasibility of using the ontology in solving multi-document summarization problems in the domain of disaster management. We provide an empirical study of different approaches in which the ontology has been used for summarization tasks. Extensive experiments on a collection of press releases relevant to Hurricane Wilma in 2005 demonstrate that ontology-based multi-document summarization methods outperform other baselines in terms of the summary quality.

In this paper, we propose a framework of recommending users and communities in social media. Given a user's profile, our framework is capable of recommending influential users and topic-cohesive interactive communities that are most relevant to the given user. In our framework, we present a generative topic model to discover user-oriented and community-oriented topics simultaneously, which enables us to capture the exact topic interests of users, as well as the focuses of communities. Extensive evaluation on a data set obtained from Twitter has demonstrated the effectiveness of our proposed framework compared with other probabilistic topic model based recommendation methods.

Online recruiting systems have gained immense attention in the wake of more and more job seekers searching jobs and enterprises finding candidates on the Internet. A critical problem in a recruiting system is how to maximally satisfy the desires of both job seekers and enterprises with reasonable recommendations or search results. In this paper, we investigate and compare various online recruiting systems from a product perspective. We then point out several key functions that help achieve a win-win situation between job seekers and enterprises for a successful recruiting system. Based on the observations and key functions, we design, implement and deploy a web-based application of recruiting system, named iHR, for Xiamen Talent Service Center. The system utilizes the latest advances in data mining and recommendation technologies to create a user-oriented service for a myriad of audience in job marketing community. Empirical evaluation and online user studies demonstrate the efficacy and effectiveness of our proposed system. Currently, iHR has been deployed at http://i.xmrc.com.cn/XMRCIntel.

The advent of Big Data era drives data analysts from different domains to use data mining techniques for data analysis. However, performing data analysis in a specific domain is not trivial; it often requires complex task configuration, onerous integration of algorithms, and efficient execution in distributed environments.Few efforts have been paid on developing effective tools to facilitate data analysts in conducting complex data analysis tasks.

Personalized news recommender systems have gained increasing attention in recent years. Within a news reading community, the implicit correlations among news readers, news articles, topics and named entities, e.g., what types of named entities in articles are preferred by users, and why users like the articles, could be valuable for building an effective news recommender. In this paper, we propose a novel news personalization framework by mining such correlations. We use hypergraph to model various high-order relations among different objects in news data, and formulate news recommendation as a ranking problem on fine-grained hypergraphs. In addition, by transductive inference, our proposed algorithm is capable of effectively handling the so-called cold-start problem. Extensive experiments on a data set collected from various news websites have demonstrated the effectiveness of our proposed algorithm.

Reciprocal recommender systems refer to systems from which users can obtain recommendations of other individuals by satisfying preferences of both parties being involved. Different from the traditional user-item recommendation, reciprocal recommenders focus on the preferences of both parties simultaneously, as well as some special properties in terms of "reciprocal". In this paper, we propose MEET -- a generalized framework for reciprocal recommendation, in which we model the correlations of users as a bipartite graph that maintains both local and global "reciprocal" utilities. The local utility captures users' mutual preferences, whereas the global utility manages the overall quality of the entire reciprocal network. Extensive empirical evaluation on two real-world data sets (online dating and online recruiting) demonstrates the effectiveness of our proposed framework compared with existing recommendation algorithms. Our analysis also provides deep insights into the special aspects of reciprocal recommenders that differentiate them from user-item recommender systems.

A variety of news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing the implicit "social" factors (i.e., the potential influential experts in news reading community) among news readers to facilitate news personalization. In this paper, we investigate the feasibility of integrating content-based methods, collaborative filtering and information diffusion models by employing probabilistic matrix factorization techniques. We propose PRemiSE, a novel Personalized news Recommendation framework via implicit Social Experts, in which the opinions of potential influencers on virtual social networks extracted from implicit feedbacks are treated as auxiliary resources for recommendation. Empirical results demonstrate the efficacy and effectiveness of our method, particularly, on handling the so-called cold-start problem.

Multimedia information plays an increasingly important role in humans daily activities. Given a set of web multimedia objects (images with corresponding texts), a challenging problem is how to group these images into several clusters using the available information. Previous researches focus on either adopting individual information, or simply combining image and text information together for clustering. In this paper, we propose a novel approach (Dynamic Weighted Clustering) to separate images under the "supervision" of text descriptions, Also, we provide a comparative experimental investigation on utilizing text and image information to tackle web image clustering. Empirical experiments on a manually collected web multimedia object (related to the events after disasters) dataset are conducted to demonstrate the efficacy of our proposed method.

In this paper, we initially provide an experimental study on the evolution of user interests in real-world news recommender systems, and then propose a novel recommendation approach, in which the long-term and short-term reading preferences of users are seamlessly integrated when recommending news items. Given a hierarchy of newly-published news articles, news groups that the user might prefer are differentiated using the long-term profile, and then in each selected news group, a list of news items are chosen based on the short-term user profile. Extensive empirical experiments on a collection of news articles obtained from various popular news websites demonstrate the efficacy of our method.

Multi-document summarization aims to distill the most representative information from a set of documents to generate a summary. Given a set of documents as input, most of existing multi-document summarization approaches utilize different sentence selection techniques to extract a set of sentences from the document set as the summary. The submodularity hidden in textual-unit similarity motivates us to incorporate this property into our solution to multi-document summarization tasks. In this poster, we propose a new principled and versatile framework for different multi-document summarization tasks using the submodular function [8].

Recommending news articles has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world. Traditional news recommendation systems strive to adapt their services to individual users by virtue of both user and news content information. However, the latent relationships among different news items, and the special properties of new articles, such as short shelf lives and value of immediacy, render the previous approaches inefficient.

In this poster, we propose a novel document summarization approach named Ontology-enriched Multi-Document Summarization(OMS) for utilizing background knowledge to improve summarization results. OMS first maps the sentences of input documents onto an ontology, then links the given query to a specific node in the ontology, and finally extracts the summary from the sentences in the subtree rooted at the query node. By using the domain-related ontology, OMS can better capture the semantic relevance between the query and the sentences, and thus lead to better summarization results. As a byproduct, the final summary generated by OMS can be represented as a tree showing the hierarchical relationships of the extracted sentences. Evaluation results on the collection of press releases by Miami-Dade County Department of Emergency Management during Hurricane Wilma in 2005 demonstrate the efficacy of OMS.