waashk/dblp huggingface dataset:
semi-nonnegative matrix factorization with global statistical consistency for collaborative filtering. collaborative filtering, considered by many researchers as the most important technique for information filtering, has been extensively studied by both academic and industrial communities. one of the most popular approaches to collaborative filtering recommendation algorithms is based on low-dimensional factor models. the assumption behind such models is that a user's preferences can be modeled by linearly combining item factor vectors using user-specific coefficients. in this paper, aiming at several aspects ignored by previous work, we propose a semi-nonnegative matrix factorization method with global statistical consistency. the major contribution of our work is twofold: (1) we endow a new understanding on the generation or latent compositions of the user-item rating matrix. under the new interpretation, our work can be formulated as the semi-nonnegative matrix factorization problem. (2) moreover, we propose a novel method of imposing the consistency between the statistics given by the predicted values and the statistics given by the data. we further develop an optimization algorithm to determine the model complexity automatically. the complexity of our method is linear with the number of the observed ratings, hence it is scalable to very large datasets. finally, comparing with other state-of-the-art methods, the experimental analysis on the eachmovie dataset illustrates the effectiveness of our approach.
named entity disambiguation by leveraging wikipedia semantic knowledge. name ambiguity problem has raised an urgent demand for efficient, high-quality named entity disambiguation methods. the key problem of named entity disambiguation is to measure the similarity between occurrences of names. the traditional methods measure the similarity using the bag of words (bow) model. the bow, however, ignores all the semantic relations such as social relatedness between named entities, associative relatedness between concepts, polysemy and synonymy between key terms. so the bow cannot reflect the actual similarity. some research has investigated social networks as background knowledge for disambiguation. social networks, however, can only capture the social relatedness between named entities, and often suffer the limited coverage problem. to overcome the previous methods' deficiencies, this paper proposes to use wikipedia as the background knowledge for disambiguation, which surpasses other knowledge bases by the coverage of concepts, rich semantic information and up-to-date content. by leveraging wikipedia's semantic knowledge like social relatedness between named entities and associative relatedness between concepts, we can measure the similarity between occurrences of names more accurately. in particular, we construct a large-scale semantic network from wikipedia, in order that the semantic knowledge can be used efficiently and effectively. based on the constructed semantic network, a novel similarity measure is proposed to leverage wikipedia semantic knowledge for disambiguation. the proposed method has been tested on the standard weps data sets. empirical results show that the disambiguation performance of our method gets 10.7% improvement over the traditional bow based methods and 16.7% improvement over the traditional social network based methods.
caofes: an ontological framework for web service retrieval. semantic web search involves retrieval of user-specific web artifacts by utilizing their semantic descriptions. a very specific web artifact that has evolved in recent times is web services. web services can be semantically described in languages like owl-s. however, such languages are limited with respect to their expressivity of context. they also lack a formal ontological framework where efficient web service retrieval can be conducted. in this paper we model a service request scenario within the web as an event-driven system. services as well as user requests are modeled as events. we propose an ontological framework called context-aware ontology framework for events and services (caofes) where such event-driven web service retrieval can be efficiently executed by a novel reasoning technique.
product feature categorization with multilevel latent semantic association. in recent years, the number of freely available online reviews is increasing at a high speed. aspect-based opinion mining technique has been employed to find out reviewers' opinions toward different product aspects. such finer-grained opinion mining is valuable for the potential customers to make their purchase decisions. product-feature extraction and categorization is very important for better mining aspect-oriented opinions. since people usually use different words to describe the same aspect in the reviews, product-feature extraction and categorization becomes more challenging. manually product-feature extraction and categorization is tedious and time consuming, and practically infeasible for the massive amount of products. in this paper, we propose an unsupervised product-feature categorization method with multilevel latent semantic association. after extracting product-features from the semi-structured reviews, we construct the first latent semantic association (lasa) model to group words into a set of concepts according to their virtual context documents. it generates the latent semantic structure for each product-feature. the second lasa model is constructed to categorize the product-features according to their latent semantic structures and context snippets in the reviews. experimental results demonstrate that our method achieves better performance compared with the existing approaches. moreover, the proposed method is language- and domain-independent.
a general magnitude-preserving boosting algorithm for search ranking. traditional boosting algorithms for the ranking problems usually employ the pairwise approach and convert the document rating preference into a binary-value label, like rankboost. however, such a pairwise approach ignores the information about the magnitude of preference in the learning process. in this paper, we present the directed distance function (ddf) as a substitute for binary labels in pairwise approach to preserve the magnitude of preference and propose a new boosting algorithm called mpboost, which applies gentleboost optimization and directly incorporates ddf into the exponential loss function. we give the boundedness property of mpboost through theoretic analysis. experimental results demonstrate that mpboost not only leads to better ndcg accuracy as compared to state-of-the-art ranking solutions in both public and commercial datasets, but also has good properties of avoiding the overfitting problem in the task of learning ranking functions.
constrained multi-aspect expertise matching for committee review assignment. automatic review assignment can significantly improve the productivity of many people such as conference organizers, journal editors and grant administrators. most previous works have set the problem up as using a paper as a query to independently "retrieve" a set of reviewers that should review the paper. a more appropriate formulation of the problem would be to simultaneously optimize the assignments of all the papers to an entire committee of reviewers under constraints such as the review quota. in this paper, we solve the problem of committee review assignment with multi-aspect expertise matching by casting it as an integer linear programming problem. the proposed algorithm can naturally accommodate any probabilistic or deterministic method for modeling multiple aspects to automate committee review assignments. evaluation using an existing data set shows that the proposed algorithm is effective for committee review assignments based on multi-aspect expertise matching.
detecting topic evolution in scientific literature: how can citations help? understanding how topics in scientific literature evolve is an interesting and important problem. previous work simply models each paper as a bag of words and also considers the impact of authors. however, the impact of one document on another as captured by citations, one important inherent element in scientific literature, has not been considered. in this paper, we address the problem of understanding topic evolution by leveraging citations, and develop citation-aware approaches. we propose an iterative topic evolution learning framework by adapting the latent dirichlet allocation model to the citation network and develop a novel inheritance topic model. we evaluate the effectiveness and efficiency of our approaches and compare with the state of the art approaches on a large collection of more than 650,000 research papers in the last 16 years and the citation network enabled by citeseerx. the results clearly show that citations can help to understand topic evolution better.
utilizing inter-passage and inter-document similarities for re-ranking search results. we present a novel language-model-based approach to re-ranking an initially retrieved list so as to improve precision at top ranks. our model integrates whole-document information with that induced from passages. specifically, inter-passage, inter-document, and query-based similarities are integrated in our model. empirical evaluation demonstrates the effectiveness of our approach.
robust record linkage blocking using suffix arrays. record linkage is an important data integration task that has many practical uses for matching, merging and duplicate removal in large and diverse databases. however, a quadratic scalability for the brute force approach necessitates the design of appropriate indexing or blocking techniques. we design and evaluate an efficient and highly scalable blocking approach based on suffix arrays. our suffix grouping technique exploits the ordering used by the index to merge similar blocks at marginal extra cost, resulting in a much higher accuracy while retaining the high scalability of the base suffix array method. efficiently grouping similar suffixes is carried out with the use of a sliding window technique. we carry out an in-depth analysis of our method and show results from experiments using real and synthetic data, which highlights the importance of using efficient indexing and blocking in real world applications where data sets contain millions of records.
adaptive relevance feedback in information retrieval. relevance feedback has proven very effective for improving retrieval accuracy. a difficult yet important problem in all relevance feedback methods is how to optimally balance the original query and feedback information. in the current feedback methods, the balance parameter is usually set to a fixed value across all the queries and collections. however, due to the difference in queries and feedback documents, this balance parameter should be optimized for each query and each set of feedback documents. in this paper, we present a learning approach to adaptively predict the optimal balance coefficient for each query and each collection. we propose three heuristics to characterize the balance between query and feedback information. taking these three heuristics as a road map, we explore a number of features and combine them using a regression approach to predict the balance coefficient. our experiments show that the proposed adaptive relevance feedback is more robust and effective than the regular fixed-coefficient feedback.
topic analysis for topic-focused multi-document summarization. topic-focused multi-document summarization has been a challenging task because the created summary is required to be biased to the given topic or query. existing methods consider the given topic as a single coarse unit and then directly incorporate the relevance between each sentence and the single topic into the sentence evaluation process. however, the given topic is usually not well-defined and it consists of a few explicit or implicit subtopics. in this study, the related subtopics are discovered from the topic's narrative text or document set through topic analysis techniques. then, the sentence relationships against each subtopic are considered as an individual modality and the multi-modality manifold-ranking method is proposed to evaluate and rank sentences by fusing the multiple modalities. experimental results on the duc benchmark datasets show the promising results of our proposed methods.
feature selection for ranking using boosted trees. modern search engines have to be fast to satisfy users, so there are hard back-end latency requirements. the set of features useful for search ranking functions, though, continues to grow, making feature computation a latency bottleneck. as a result, not all available features can be used for ranking, and in fact, much of the time, only a small percentage of these features can be used. thus, it is crucial to have a feature selection mechanism that can find a subset of features that both meets latency requirements and achieves high relevance. to this end, we explore different feature selection methods using boosted regression trees, including both greedy approaches (selecting the features with highest relative importance as computed by boosted trees; discounting importance by feature similarity and a randomized approach. we evaluate and compare these approaches using data from a commercial search engine. the experimental results show that the proposed randomized feature selection with feature-importance-based backward elimination outperforms greedy approaches and achieves a comparable relevance with 30 features to a full-feature model trained with 419 features and the same modeling parameters.
the influence of the document ranking in expert search. the retrieval effectiveness of the underlying document search component of an expert search engine can have an important impact on the effectiveness of the generated expert search results. in this large-scale study, we perform novel experiments in the context of the document search and expert search tasks of the trec enterprise track, to measure the influence that the performance of the document ranking has on the ranking of candidate experts. in particular, we show, using real and simulated document rankings, that while the expert search system performance is related to the relevance of the retrieved documents, surprisingly, it is not always the case that increasing document search effectiveness causes an increase in expert search performance.
incident threading for news passages. with an overwhelming volume of news reports currently available, there is an increasing need for automatic techniques to analyze and present news to a general reader in a meaningful and efficient manner. we explore incident threading as a possible solution to this problem. all text that describes the occurrence of a real-world happening is merged into a news incident, and incidents are organized in a network with dependencies of predefined types. earlier attempts at this problem have assumed that a news story covers a single topic. we move beyond that limitation to introduce passage threading, which processes news at the passage level. first we develop a new testbed for this research and extend the evaluation methods to address new granularity issues. then a three-stage algorithm is described that identifies on-subject passages, groups them into incidents, and establishes links between related incidents. finally, we observe significant improvement over earlier work when we optimize the harmonic mean of the appropriate evaluation measures. the resulting performance exceeds the level that a calibration study shows is necessary to support a reading comprehension task.
time sequence summarization to scale up chronology-dependent applications. in this paper, we present the concept of time sequence summarization to support chronology-dependent applications on massive data sources. time sequence summarization takes as input a time sequence of events that are chronologically ordered. each event is described by a set of descriptors. time sequence summarization produces a concise time sequence that can be substituted for the original time sequence in chronology-dependent applications. we propose an algorithm that achieves time sequence summarization based on a generalization, grouping and concept formation process. generalization expresses event descriptors at higher levels of abstraction using taxonomies while grouping gathers similar events. concept formation is responsible for reducing the size of the input time sequence of events by representing each group created by one concept. the process is performed in a way such that the overall chronology of events is preserved. the algorithm computes the summary incrementally and has reduced algorithmic complexity. the resulting output is a concise representation, yet, informative enough to directly support chronology-dependent applications. we validate our approach by summarizing one year of financial news provided by reuters.
building domain-oriented sentiment lexicon by improved information bottleneck. this paper describes an adapted information bottleneck approach for construction of domain-oriented sentiment lexicon. the basic idea is to use three kinds of relationships (wwinter, wdinter and wdintra,) to infer the semantic orientation of the out-of-domain words. the experimental results demonstrate that proposed method could dramatically improve the accuracy of the baseline approach on the construction of out-of-domain sentiment lexicon.
extraction of a latent blog community based on subject. in the blogosphere, there exist posts relevant to a particular subject and blogs that show interests in the subject. in this paper, we define a set of such posts and blogs as "blog community" and propose a method for extracting the blog community associated with a particular subject. the proposed method is based on the idea that the blogs who have performed actions to the posts of a particular subject are the ones that have interests in the subject, and that the posts which have received actions from such blogs are the ones that contain the subject. the proposed method selects a small number of seed posts that contain the subject. then, it selects the blogs that perform actions to the seed posts over some threshold and the posts that have received actions over some threshold. by repeating these two steps, it gradually expands the blog community. the experimental results show that the proposed method exhibits a higher level of accuracy than the methods proposed in prior research.
completing wikipedia's hyperlink structure through dimensionality reduction. wikipedia is the largest monolithic repository of human knowledge. in addition to its sheer size, it represents a new encyclopedic paradigm by interconnecting articles through hyperlinks. however, since these links are created by human authors, links one would expect to see are often missing. the goal of this work is to detect such gaps automatically. in this paper, we propose a novel method for augmenting the structure of hyperlinked document collections such as wikipedia. it does not require the extraction of any manually defined features from the article to be augmented. instead, it is based on principal component analysis, a well-founded mathematical generalization technique, and predicts new links purely based on the statistical structure of the graph formed by the existing links. our method does not rely on the textual content of articles; we are exploiting only hyperlinks. a user evaluation of our technique shows that it improves the quality of top link suggestions over the state of the art and that the best predicted links are significantly more valuable than the 'average' link already present in wikipedia. beyond link prediction, our algorithm can potentially be used to point out topics an article misses to cover and to cluster articles semantically.
multidimensional routing indices for efficient distributed query processing. traditional routing indices in peer-to-peer (p2p) networks are mainly designed for document retrieval applications and maintain aggregated one-dimensional values representing the number of documents that can be obtained in a certain direction in the network. in this paper, we introduce the concept of multidimensional routing indices (mris), which are suitable for handling multidimensional data represented by minimum bounding regions (mbrs). depending on data distribution on peers, the aggregation of the mbrs may lead to mris that exhibit extremely poor performance, which renders them ineffective. thus, focusing on a hybrid unstructured p2p network, we analyze the parameters for building mris of high selectivity. we present techniques that boost the query routing performance by detecting similar peers and grouping and reassigning these peers to other parts of the hybrid network in a distributed and scalable way. we demonstrate the advantages of our approach using large-scale simulations.
multidimensional political spectrum identification and analysis. in this work, we show the importance of multidimensional opinion representation in the political context combining domain knowledge and results from principal component analysis. we discuss the differences of feature selection between political spectrum analysis and normal opinion mining tasks. we build regression models on each opinion dimension for scoring and placing new opinion entities, e.g. personal blogs or politicians, onto the political opinion spectrum. we apply our methods on the floor statement records of the united states senate and evaluate it against the uni-dimensional representation of political opinion space. the experimental results show the effectiveness of the proposed model in explaining the voting records of the senate.
using negative voting to diversify answers in non-factoid question answering. we propose a ranking model to diversify answers of non-factoid questions based on an inverse notion of graph connectivity. by representing a collection of candidate answers as a graph, we posit that novelty, a measure of diversity, is inversely proportional to answer vertices' connectivity. hence, unlike the typical graph ranking models, which score vertices based on the degree of connectedness, our method assigns a penalty score for a candidate answer if it is strongly connected to other answers. that is, any redundant answers, indicated by a higher inter-sentence similarity, will be ranked lower than those with lower inter-sentence similarity. at the end of the ranking iterations, many redundant answers will be moved toward the bottom on the ranked list. the experimental results show that our method helps diversify answer coverage of non-factoid questions according to f-scores from nugget pyramid evaluation.
efficient itemset generator discovery over a stream sliding window. mining generator patterns has raised great research interest in recent years. the main purpose of mining itemset generators is that they can form equivalence classes together with closed itemsets, and can be used to generate simple classification rules according to the mdl principle. in this paper, we devise an efficient algorithm called streamgen to mine frequent itemset generators over a stream sliding window. we adopt a novel enumeration tree structure to help keep the information of mined generators and the border between generators and non-generators, and propose some optimization techniques to speed up the mining process. we further extend the algorithm to directly mine a set of high quality classification rules over stream sliding windows while keeping high performance. the extensive performance study shows that our algorithm outperforms other state-of-the-art algorithms which perform similar tasks in terms of both runtime and memory usage efficiency, and has high utility in terms of classification.