Literature search is one of the most important steps of academic research. With more than 100,000 papers published each year just in computer science, performing a complete literature search becomes a Herculean task. Some of the existing approaches and tools for literature search cannot compete with the characteristics of today’s literature, and they suffer from ambiguity and homonymy. Techniques based on citation information are more robust to the mentioned issues. Thus, we recently built a Web service called the advisor, which provides personalized recommendations to researchers based on their papers of interest. Since most recommendation methods may return redundant results, diversifying the results of the search process is necessary to increase the amount of information that one can reach via an automated search. This article targets the problem of result diversification in citation-based bibliographic search, assuming that the citation graph itself is the only information available and no categories or intents are known. The contribution of this work is threefold. We survey various random walk--based diversification methods and enhance them with the direction awareness property to allow users to reach either old, foundational (possibly well-cited and well-known) research papers or recent (most likely less-known) ones. Next, we propose a set of novel algorithms based on vertex selection and query refinement. A set of experiments with various evaluation criteria shows that the proposed γ-RLM algorithm performs better than the existing approaches and is suitable for real-time bibliographic search in practice.

Literature search is an integral part of the academic research. Academic recommendation services have been developed to help researchers with their literature search, many of which only provide a text-based search functionality. Such services are suitable for a first-level bibliographic search; however, they lack the benefits of today's recommendation engines. In this paper, we identify three important properties that an academic recommendation service could provide for better literature search: personalization, scalability, and exploratory search. With these objectives in mind, we present a web service called theadvisor which helps the users build a strong bibliography by extending the document set obtained after a first-level search. Along with an efficient and personalized recommendation algorithm, the service also features result diversification, relevance feedback, visualization for exploratory search. We explain the design criteria and rationale we employed to make the theadvisor a useful and scalable web service with a thorough evaluation.

Result diversification has gained a lot of attention as a way to answer ambiguous queries and to tackle the redundancy problem in the results. In the last decade, diversification has been applied on or integrated into the process of PageRank- or eigenvector-based methods that run on various graphs, including social networks, collaboration networks in academia, web and product co-purchasing graphs. For these applications, the diversification problem is usually addressed as a bicriteria objective optimization problem of relevance and diversity. However, such an approach is questionable since a query-oblivious diversification algorithm that recommends most of its results without even considering the query may perform the best on these commonly used measures. In this paper, we show the deficiencies of popular evaluation techniques of diversification methods, and investigate multiple relevance and diversity measures to understand whether they have any correlations. Next, we propose a novel measure called expanded relevance which combines both relevance and diversity into a single function in order to measure the coverage of the relevant part of the graph. We also present a new greedy diversification algorithm called BestCoverage, which optimizes the expanded relevance of the result set with (1-1/e)-approximation. With a rigorous experimentation on graphs from various applications, we show that the proposed method is efficient and effective for many use cases.

The need to analyze high-dimension biological data is driving the development of new data mining methods. Biclustering algorithms have been successfully applied to gene expression data to discover local patterns, in which a subset of genes exhibit similar expression levels over a subset of conditions. However, it is not clear which algorithms are best suited for this task. Many algorithms have been published in the past decade, most of which have been compared only to a small number of algorithms. Surveys and comparisons exist in the literature, but because of the large number and variety of biclustering algorithms, they are quickly outdated. In this article we partially address this problem of evaluating the strengths and weaknesses of existing biclustering methods. We used the BiBench package to compare 12 algorithms, many of which were recently published or have not been extensively studied. The algorithms were tested on a suite of synthetic data sets to measure their performance on data with varying conditions, such as different bicluster models, varying noise, varying numbers of biclusters and overlapping biclusters. The algorithms were also tested on eight large gene expression data sets obtained from the Gene Expression Omnibus. Gene Ontology enrichment analysis was performed on the resulting biclusters, and the best enrichment terms are reported. Our analyses show that the biclustering method and its parameters should be selected based on the desired model, whether that model allows overlapping biclusters, and its robustness to noise. In addition, we observe that the biclustering algorithms capable of finding more than one model are more successful at capturing biologically relevant clusters.

Traditional search methods try to obtain the most relevant information and rank it according to the degree of similarity to the queries. Diversity in query results is also preferred by a variety of applications since results very similar to each other cannot capture all aspects of the queried topic. In this paper, we focus on the \lambda-diverse k-nearest neighbor search problem on spatial and multidimensional data. Unlike the approach of diversifying query results in a postprocessing step, we naturally obtain diverse results with the proposed geometric and index-based methods. We first make an analogy with the concept of Natural Neighbors (NatN) and propose a natural neighbor-based method for 2D and 3D data and an incremental browsing algorithm based on Gabriel graphs for higher dimensional spaces. We then introduce a diverse browsing method based on the distance browsing feature of spatial index structures, such as R-trees. The algorithm maintains a Priority Queue with mindivdist of the objects depending on both relevancy and angular diversity and efficiently prunes nondiverse items and nodes. We experiment with a number of spatial and high-dimensional data sets, including Factual's (http://www.factual.com/) US points-of-interest data set of 13M entries. On the experimental setup, the diverse browsing method is shown to be more efficient (regarding disk accesses) than k-NN search on R-trees, and more effective (regarding Maximal Marginal Relevance (MMR)) than the diverse nearest neighbor search techniques found in the literature.

Graphs and matrices are widely used in algorithms for social network analyses. Since the number of interactions is much less than the possible number of interactions, the graphs and matrices used in the analyses are usually sparse. In this paper, we propose an efficient implementation of a sparse-matrix computation which arises in our publicly available citation recommendation service called the advisor. The recommendation algorithm uses a sparse matrix generated from the citation graph. We observed that the nonzero pattern of this matrix is highly irregular and the computation suffers from high number of cache misses. We propose techniques for storing the matrix in memory efficiently and reducing the number of cache misses. Experimental results show that our techniques are highly efficient on reducing the query processing time which is highly crucial for a web service.

Sentiment extraction from online web documents has recently been an active research topic due to its potential use in commercial applications. By sentiment analysis, we refer to the problem of assigning a quantitative positive/negative mood to a short bit of text. Most studies in this area are limited to the identification of sentiments and do not investigate the interplay between sentiments and other factors. In this work, we use a sentiment extraction tool to investigate the influence of factors such as gender, age, education level, the topic at hand, or even the time of the day on sentiments in the context of a large online question answering site. We start our analysis by looking at direct correlations, e.g., we observe more positive sentiments on weekends, very neutral ones in the Science & Mathematics topic, a trend for younger people to express stronger sentiments, or people in military bases to ask the most neutral questions. We then extend this basic analysis by investigating how properties of the (asker, answerer) pair affect the sentiment present in the answer. Among other things, we observe a dependence on the pairing of some inferred attributes estimated by a user's ZIP code. We also show that the best answers differ in their sentiments from other answers, e.g., in the Business & Finance topic, best answers tend to have a more neutral sentiment than other answers. Finally, we report results for the task of predicting the attitude that a question will provoke in answers. We believe that understanding factors influencing the mood of users is not only interesting from a sociological point of view, but also has applications in advertising, recommendation, and search.