User interest as a very dynamic information need is often ignored in most existing information retrieval systems. In this research, we present the results of experiments designed to evaluate the performance of a real‐time interest model (RIM) that attempts to identify the dynamic and changing query level interests regarding social media outputs. Unlike most existing ranking methods, our ranking approach targets calculation of the probability that user interest in the content of the document is subject to very dynamic user interest change. We describe 2 formulations of the model (real‐time interest vector space and real‐time interest language model) stemming from classical relevance ranking methods and develop a novel methodology for evaluating the performance of RIM using Amazon Mechanical Turk to collect (interest‐based) relevance judgments on a daily basis. Our results show that the model usually, although not always, performs better than baseline results obtained from commercial web search engines. We identify factors that affect RIM performance and outline plans for future research.

Previous research into the efficiency of text retrieval systems has dealt primarily with methods that consider inverted lists in sequence; these methods are known as term-at-a-time methods. However, the literature for optimizing document-at-a-time systems remains sparse.We present an improvement to the max_score optimization, which is the most efficient known document-at-a-time scoring method. Like max_score, our technique, called term bounded max_score, is guaranteed to return exactly the same scores and documents as an unoptimized evaluation, which is particularly useful for query model research. We simulated our technique to explore the problem space, then implemented it in Indri, our large scale language modeling search engine. Tests with the GOV2 corpus on title queries show our method to be 23% faster than max_score alone, and 61% faster than our document-at-a-time baseline. Our optimized query times are competitive with conventional term-at-a-time systems on this year's TREC Terabyte task.

The inference network model of information retrieval allows a probabilistic interpretation of query operators. In particular, Boolean query operators are conveniently modeled as link matrices of the Bayesian Network. Prior work has shown, however, that these operators do not perform as well as the pnorm operators used for modeling query operators in the context of the vector space model. This motivates the search for alternative probabilistic formulations for these operators. The design of such alternatives must contend with the issue of computational tractability, since the evaluation of an arbitrary operator requires exponential time. We define a flexible class of link matrices that are natural candidates for the implementation of query operators and an O ( n 2 ) algorithm ( n = the number of parent nodes) for the computation of probabilities involving link matrices of this class. We present experimental results indicating that Boolean operators implemented in terms of link matrices from this class perform as well as pnorm operators in the context of the INQUERY inference network.

article Free Access Share on Computationally tractable probabilistic modeling of Boolean operators Authors: Warren R. Greiff Computer Science Department, University of Masaachusetts, Amherst, Amherst, Massachusetts Computer Science Department, University of Masaachusetts, Amherst, Amherst, MassachusettsView Profile , W. Bruce Croft Computer Science Department, University of Masaachusetts, Amherst, Amherst, Massachusetts Computer Science Department, University of Masaachusetts, Amherst, Amherst, MassachusettsView Profile , Howard Turtle West Publishing Co., Eagan, Minnesota West Publishing Co., Eagan, MinnesotaView Profile Authors Info & Claims ACM SIGIR ForumVolume 31Issue SIDecember 1997 pp 119–128https://doi.org/10.1145/278459.258547Published:01 July 1997Publication History 4citation480DownloadsMetricsTotal Citations4Total Downloads480Last 12 Months23Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Document management systems are needed for many business applications. This type of system would combine the functionality of a database system, (for describing, storing and maintaining documents with complex structure and relationships) with a text retrieval system (for effective retrieval based on full text). The retrieval model for a document management system is complicated by the variety and complexity of the objects that are represented. In this paper, we describe an approach to complex object retrieval using a probabilistic inference net model, and an implementation of this approach using a loose coupling of an object-oriented database system (IRIS) and a text retrieval system based on inference nets (INQUERY). The resulting system is used to store long, structured documents and can retrieve document components (sections, figures, etc.) based on their contents or the contents of related components. The lessons learnt from the implementation are discussed.

Many retrieval models have been proposed as the basis of text retrieval systems. The three main classes that have been investigated are the exact-match, vector space and probabilistic models. The retrieval effectiveness of strategies based on these models has been evaluated experimentally, but there has been little in the way of comparison in terms of their formal properties. In this paper we introduce a recent form of the probabilistic model based on inference networks, and show how the vector space and exact-match can be described in this framework. Differences between these models can be explained as differences in the estimation of probabilities, both in the initial search and during relevance feedback.

Both phrases and Boolean queries have a long history in information retrieval, particularly in commercial systems.In previous work, Boolean queries have been used as a source of phrases for a statistical retrieval model, This work, like the majority of research on phrases, resulted in little improvement in retrieval effectiveness, In this paper, we describe an approach where phrases identified in natural language queries are used to build structured queries for a probabilistic retrieval model.Our results show that using phrases in this way can improve performance, and that phrases that are automatically extracted from a natural language query perform nearly as well as manually selected phrases.

The use of inference networks to support document retrieval is introduced. A network-based retrieval model is described and compared to conventional probabilistic and Boolean models.