Paul B. Kantor

Recommender Systems Handbook

The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments. Recommender Systems Handbook, an edited volume, is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included. Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.

Combining the evidence of multiple query representations for information retrieval

Abstract We report on two studies in the TREC-2 program that investigated the effect on retrieval performance of combination of multiple representations of TREC topics. In one of the projects, five separate Boolean queries for each of the 50 TREC routing topics and 25 of the TREC ad hoc topics were generated by 75 experienced online searchers. Using the INQUERY retrieval system, these queries were both combined into single queries, and used to produce five separate retrieval results for each topic. In the former case, progressive combination of queries led to progressively improving retrieval performance, significantly better than that of single queries, and at least as good as the best individual single-query formulations. In the latter case, data fusion of the ranked lists also led to performance better than that of any single list. In the second project, two automatically produced vector queries and three versions of a manually produced P-norm extended Boolean query for each routing and ad hoc topic were compared and combined. This project investigated six different methods of combination of queries, and the combination of the same queries on different databases. As in the first project, progressive combination led to progressively improving results, with the best results, on average, being achieved by combination through summing of retrieval status values. Both projects found that the best method of combination often led to results that were better than the best performing single query. The combined results from the two projects have also been combined by data fusion. The results of this procedure show that combining evidence from completely different systems also leads to performance improvement.

A study of information seeking and retrieving. I. background and methodology

The objectives of the study were to conduct a series of observations and experiments under as real-life a situation as possible related to: (i) user context of questions in information retrieval; (ii) the structure and classification of questions; (iii) cognitive traits and decision making of searchers; and (iv) different searches of the same question. The study is presented in three parts: Part I presents the background ot the study and describes the models, measures, methods, procedures, and statistical analyses used. Part II is devoted to results related to users, questions, and effectiveness measures, and Part III to results related to searchers, searches, and overlap studies. A concluding summary of all results is presented in Part III.

A study of information seeking and retrieving. iii. searchers, searches and overlap

The objectives of the study were to conduct a series of observations and experiments under as real-life situation as possible related to: (1) user context of questions in information retrieval; (2) the structure and classification of questions; (3) cognitive traits and decision making of searchers; and (4) different searches of the same question. The study is presented in three parts: Part I presents the background of the study and describes the models, measures, methods, procedures and statistical analyses used. Part II is devoted to results related to users, questions and effectiveness measures, and Part III to results related to searchers, searches and overlap studies. A concluding summary of all results is presented in Part III.

A study of information seeking and retrieving. II. Users, questions, and effectiveness

The objectives of the study were to conduct a series of observations and experiments under as real‐life a situation as possible related to: (1) user context of questions in information retrieval; (2) the structure and classification of questions; (3) cognitive traits and decision making of searchers; and (4) different searches of the same question. The study is presented in three parts: Part I presents the background of the study and describes the models, measures, methods, procedures and statistical analyses used. Part II is devoted to results related to users, questions and effectiveness measures, and Part III to results related to searchers, searches and overlap studies. A concluding summary of all results is presented in Part III.

Studying the value of library and information services. Part I. Establishing a theoretical framework

This report is derived from a large study sponsored by the Council on Library Resources. Two of the objectives of the study were to develop a taxonomy of value-in-use of library and information services based on users assessments and to propose methods and instruments for similar studies of library and information services in general. The corresponding results are reported in two parts. In this, the first part, we discuss underlying concepts related to value that must be clarified in order to proceed with any pragmatic study of value, and we establish a theory of use-oriented value of information and information services. We examine the notion of “value” in philosophy and economics and in relation to library and information services as well as the connection between value and relevance. We develop two models: One related to use of information and the other to use of library and information services. They are a theoretical framework for pragmatic study of value and a guide for the development of a Derived Taxonomy of Value in Using Library and Information Services. In the second part of this report, we present the methodology employed in development of the Taxonomy, the Taxonomy itself, and the results of testing the Taxonomy. We believe that the Taxonomy covers most dimensions of value related to use of library and information services. In the second part we also present suggestions for pragmatic applications of the Taxonomy.

3D object retrieval using many-to-many matching of curve skeletons

We present a 3D matching framework based on a many-to-many matching algorithm that works with skeletal representations of 3D volumetric objects. We demonstrate the performance of this approach on a large database of 3D objects containing more than 1000 exemplars. The method is especially suited to matching objects with distinct part structure and is invariant to part articulation. Skeletal matching has an intuitive quality that helps in defining the search and visualizing the results. In particular, the matching algorithm produces a direct correspondence between two skeletons and their parts, which can be used for registration and juxtaposition.

Predicting the effectiveness of Naïve data fusion on the basis of system characteristics

Effective automation of the information retrieval task has long been an active area of research, leading to sophisticated retrieval models. With many IR schemes available, researchers have begun to investigate the benefits of combining the results of different IR schemes to improve performance, in the process called “data fusion.” There are many successful data fusion experiments reported in IR literature, but there are also cases in which it did not work well. Thus, if would be quite valuable to have a theory that can predict, in advance, whether fusion of two or more retrieval schemes will be worth doing. In previous study (Ng & Kantor, 1998), we identified two predictive variables for the effectiveness of fusion: (a) a list‐based measure of output dissimilarity, and (b) a pair‐wise measure of the similarity of performance of the two schemes. In this article we investigate the predictive power of these two variables in simple symmetrical data fusion. We use the IR systems participating in the TREC 4 routing task to train a model that predicts the effectiveness of data fusion, and use the IR systems participating in the TREC 5 routing task to test that model. The model asks, “when will fusion perform better than an oracle who uses the best scheme from each pair?” We explore statistical techniques for fitting the model to the training data and use the receiver operating characteristic curve of signal detection theory to represent the power of the resulting models. The trained prediction methods predict whether fusion will beat an oracle, at levels much higher than could be achieved by chance.

The TREC-5 Confusion Track: Comparing Retrieval Methods for Scanned Text

A known-item search is a particular information retrieval task in which the system is asked to find a single target document in a large document set. The TREC-5 confusion track used a set of 49 known-item tasks to study the impact of data corruption on retrieval system performance. Two corrupted versions of a 55,600 document corpus whose true content was known were created by applying OCR techniques to page images. The first version of the corpus used the page images as scanned, resulting in an estimated character error rate of approximately 5%. The second version used page images that had been down-sampled, resulting in an estimated character error rate of approximately 20%. The true text and each of the corrupted versions were then searched using the same set of 49 questions. In general, retrieval methods that attempted a probabilistic reconstruction of the original clean text fared better than methods that simply accepted corrupted versions of the query text.

Capturing human intelligence in the net

112 August 2000/Vol. 43, No. 8 COMMUNICATIONS OF THE ACM As a resource the Web is amazing and bewildering, and, at times, infuriating. All of us have, at one time or another, followed a seemingly endless loop, hopefully clicking one more time in a quest for some specific information. Many of us were also not the first person ever to be frustrated searching for that particular information. But the Web does not (yet) learn from other people’s mistakes. In that sense, we who use it are not even as clever as ants in the kitchen, who always leave chemical trails for their nestmates when they find something good to eat. Pursuing the ant metaphor, we imagine a user community operating in asynchronous collaboration mode, where information trails from user quests for information on the Internet are left behind for any community member to follow. The goal is to post and share communal knowledge: as community members engage in individual information quests, they make a small extra CapturingHUMAN INTELLIGENCE in the Net Paul B. Kantor, Endre Boros, Benjamin Melamed, Vladimir MeÑkov, Bracha Shapira, and David J. Neu