The task of expert finding has been getting increasing attention in information retrieval literature. However, the current state-of-the-art is still lacking in principled approaches for combining different sources of evidence. This paper explores the usage of unsupervised rank aggregation methods as a principled approach for combining multiple estimators of expertise, derived from the textual contents, from the graph-structure of the citation patterns for the community of experts, and from profile information about the experts. We specifically experimented two unsupervised rank aggregation approaches well known in the information retrieval literature, namely CombSUM and CombMNZ. Experiments made over a dataset of academic publications for the area of Computer Science attest for the adequacy of these methods.
Many different authors have proposed sophisticated probabilistic retrieval models, specific to the expert finding task, with basis on the document-centric approach @cite_22 @cite_26 @cite_3 . For instance proposed a two-stage language model combining document relevance and co-occurrence between experts and query terms @cite_24 . Fang and Zhai derived a generative probabilistic model from the probabilistic ranking principle and extend it with query expansion and non-uniform candidate priors @cite_23 . proposed a multiple window based approach for integrating multiple levels of associations between experts and query topics in expert finding @cite_16 . More recently, proposed a unified language model integrating many document features for expert finding @cite_8 . Although the above models are capable of employing different types of associations among query terms, documents and experts, they mostly ignore other important sources of evidence, such as the importance of individual documents, or the co-citation patterns between experts available from citation graphs. In this paper, we offer a principled approach for combining a much larger set of expertise estimates.
@cite_26 @cite_22 @cite_8 @cite_3 @cite_24 @cite_23 @cite_16
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Recently expertise retrieval has received increasing interests in both academia and industry. Finding experts with demonstrated expertise for a given query is a nontrivial task especially from a large-scale Web 2.0 systems, such as question answering and bibliography data, where users are actively publishing useful content online, interacting with each other, and forming social networks in various ways, leading to heterogeneous networks in addition to the large amounts of textual content information. Many approaches have been proposed and shown to be useful for expertise ranking. However, most of these methods only consider the textual documents while ignoring heterogeneous network structures or can merely integrate with one additional kind of information. None of them can fully exploit the characteristics of heterogeneous networks. In this paper, we propose a joint regularization framework to enhance expertise retrieval by modeling heterogeneous networks as regularization constraints on top of document-centric model. We argue that multi-typed linking edges reveal valuable information which should be treated differently. Motivated by this intuition, we formulate three hypotheses to capture unique characteristics for different graphs, and mathematically model those hypotheses jointly with the document and other information. To illustrate our methodology, we apply the framework to expert finding applications using a bibliography dataset with 1.1 million papers and 0.7 million authors. The experimental results show that our proposed approach can achieve significantly better results than the baseline and other enhanced models. A novel probabilistic retrieval model is presented. It forms a basis to interpret the TF-IDF term weights as making relevance decisions. It simulates the local relevance decision-making for every location of a document, and combines all of these “local” relevance decisions as the “document-wide” relevance decision for the document. The significance of interpreting TF-IDF in this way is the potential to: (1) establish a unifying perspective about information retrieval as relevance decision-making; and (2) develop advanced TF-IDF-related term weights for future elaborate retrieval models. Our novel retrieval model is simplified to a basic ranking formula that directly corresponds to the TF-IDF term weights. In general, we show that the term-frequency factor of the ranking formula can be rendered into different term-frequency factors of existing retrieval systems. In the basic ranking formula, the remaining quantity - log p(rvt ∈ d) is interpreted as the probability of randomly picking a nonrelevant usage (denoted by r) of term t. Mathematically, we show that this quantity can be approximated by the inverse document-frequency (IDF). Empirically, we show that this quantity is related to IDF, using four reference TREC ad hoc retrieval data collections. We propose a new probabilistic approach to information retrieval based upon the ideas and methods of statistical machine translation. The central ingredient in this approach is a statistical model of how a user might distill or "translate" a given document into a query. To assess the relevance of a document to a user's query, we estimate the probability that the query would have been generated as a translation of the document, and factor in the user's general preferences in the form of a prior distribution over documents. We propose a simple, well motivated model of the document-to-query translation process, and describe an algorithm for learning the parameters of this model in an unsupervised manner from a collection of documents. As we show, one can view this approach as a generalization and justification of the "language modeling" strategy recently proposed by Ponte and Croft. In a series of experiments on TREC data, a simple translation-based retrieval system performs well in comparison to conventional retrieval techniques. This prototype system only begins to tap the full potential of translation-based retrieval. This paper reports on a novel technique for literature indexing and searching in a mechanized library system. The notion of relevance is taken as the key concept in the theory of information retrieval and a comparative concept of relevance is explicated in terms of the theory of probability. The resulting technique called “Probabilistic Indexing,” allows a computing machine, given a request for information, to make a statistical inference and derive a number (called the “relevance number”) for each document, which is a measure of the probability that the document will satisfy the given request. The result of a search is an ordered list of those documents which satisfy the request ranked according to their probable relevance. The paper goes on to show that whereas in a conventional library system the cross-referencing (“see” and “see also”) is based solely on the “semantical closeness” between index terms, statistical measures of closeness between index terms can be defined and computed. Thus, given an arbitrary request consisting of one (or many) index term(s), a machine can elaborate on it to increase the probability of selecting relevant documents that would not otherwise have been selected. Finally, the paper suggests an interpretation of the whole library problem as one where the request is considered as a clue on the basis of which the library system makes a concatenated statistical inference in order to provide as an output an ordered list of those documents which most probably satisfy the information needs of the user.