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This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

/pdf/toward-the-next-generation-of-recommender-systems-a-survey-4wwsv77rns.pdf

Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.

/pdf/machine-learning-in-automated-text-categorization-e4pkjhyj3r.pdf

Machine learning in automated text categorization

Collaborative filters help people make choices based on the opinions of other people. GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles. News reader clients display predicted scores and make it easy for users to rate articles after they read them. Rating servers, called Better Bit Bureaus, gather and disseminate the ratings. The rating servers predict scores based on the heuristic that people who agreed in the past will probably agree again. Users can protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction. The entire architecture is open: alternative software for news clients and Better Bit Bureaus can be developed independently and can interoperate with the components we have developed.

http://pages.cpsc.ucalgary.ca/~saul/601.13/readings/p175-resnick.pdf

GroupLens: an open architecture for collaborative filtering of netnews

Modern large retrieval environments tend to overwhelm their users by their large output. Since all documents are not of equal relevance to their users, highly relevant documents should be identified and ranked first for presentation. In order to develop IR techniques in this direction, it is necessary to develop evaluation approaches and methods that credit IR methods for their ability to retrieve highly relevant documents. This can be done by extending traditional evaluation methods, that is, recall and precision based on binary relevance judgments, to graded relevance judgments. Alternatively, novel measures based on graded relevance judgments may be developed. This article proposes several novel measures that compute the cumulative gain the user obtains by examining the retrieval result up to a given ranked position. The first one accumulates the relevance scores of retrieved documents along the ranked result list. The second one is similar but applies a discount factor to the relevance scores in order to devaluate late-retrieved documents. The third one computes the relative-to-the-ideal performance of IR techniques, based on the cumulative gain they are able to yield. These novel measures are defined and discussed and their use is demonstrated in a case study using TREC data: sample system run results for 20 queries in TREC-7. As a relevance base we used novel graded relevance judgments on a four-point scale. The test results indicate that the proposed measures credit IR methods for their ability to retrieve highly relevant documents and allow testing of statistical significance of effectiveness differences. The graphs based on the measures also provide insight into the performance IR techniques and allow interpretation, for example, from the user point of view.

https://www.cc.gatech.edu/~zha/CS8803WST/dcg.pdf

Cumulated gain-based evaluation of IR techniques

Information filtering systems are designed for unstructured or semistructured data, as opposed to database applications, which use very structured data. The systems also deal primarily with textual information, but they may also entail images, voice, video or other data types that are part of multimedia information systems. Information filtering systems also involve a large amount of data and streams of incoming data, whether broadcast from a remote source or sent directly by other sources. Filtering is based on descriptions of individual or group information preferences, or profiles, that typically represent long-term interests. Filtering also implies removal of data from an incoming stream rather than finding data in the stream; users see only the data that is extracted. Models of information retrieval and filtering, and lessons for filtering from retrieval research are presented.

/pdf/information-filtering-and-information-retrieval-two-sides-of-zycsi977zk.pdf

Information filtering and information retrieval: two sides of the same coin?

We report the results of a British Library Research and Development Department‐funded design study for an interactive information retrieval system which will determine structural representations of the anomalous states of knowledge (ASKs) underlying information needs, and attempt to resolve the anomalies through a variety of retrieval strategies performed on a database of documents represented in compatible structural formats. Part I discusses the background to the project and the theory underlying it, Part II (next issue) presents our methods, results and conclusions. Basic premises of the project were: that information needs are not in principle precisely specifiable; that it is possible to elicit problem statements from information system users from which representations of the ASK underlying the need can be derived; that there are classes of ASKs; and, that all elements of information retrieval systems ought to be based on the user's ASK. We have developed a relatively freeform interview technique for eliciting problem statements, and a statistical word co‐occurrence analysis for deriving network representations of the problem statements and abstracts. Structural characteristics of the representations have been used to determine classes of ASKs, and both ASK and information structures have been evaluated by, respectively, users and authors. Some results are: that interviewing appears to be a satisfactory technique for eliciting problem statements from which ASKs can be determined; that the statistical analysis produces structures which are generally appropriate both for documents and problem statements; that ASKs thus represented can be usefully classified according to their structural characteristics; and, that of thirty‐five subjects, only two had ASKs for which traditional ‘best match’ retrieval would be intuitively appropriate. The results of the design study indicate that at least some of our premises are reasonable, and that an ASK‐based information retrieval system is at least feasible.

/pdf/ask-for-information-retrieval-part-i-background-and-theory-3ep6fbllp2.pdf

Ask for information retrieval: part I.: background and theory

This study investigates the use and effectiveness of an ,advanced information retrieval (IR) system (INQUERY). 64 novice IR system users were studied in their use of a baseline version of INQUERY compared with one of three experimental versions, each offering a different level of interaction with a relevance feedback facility for automatic query reformulation. Results, in an inforumtion filtering task, indicate that: these subjects, after minimal training, were able to use the baseline system reasonably effectively; availability and use of relevance feedback increased retrieval effectiveness; and increased opportunity for user interaction with and control of relevance feedback made the interactions more efficient and usable while maintaining or increasing effective-

A case for interaction: a study of interactive information retrieval behavior and effectiveness

The support of effective interaction of the user with the other components of the system is a central problem for information retrieval. In this paper, we present a theory of such interactions taking place within a space of information-seeking strategies, and discuss how such a concept can be used to design for effective interaction. In particular, we propose a model of information retrieval system design based on the ideas of: a multidimensional space of information-seeking strategies; dialogue structures for information seeking; cases of specific information-seeking dialogues; anti, scripts as distinguished prototypical cases. We demonstrate the use of this model by discussing in some detail the MERIT system, a prototype information retrieval system, that incorporates these design principles.

Cases, scripts, and information-seeking strategies: On the design of interactive information retrieval systems

In ‘ASK for Information Retrieval: Part I’, we discussed the theory and background to a design study for an information retrieval (IR) system based on the attempt to represent the anomalous states of knowledge (ASKs) underlying information needs. In Part II, we report the methods and results of the design study, and our conclusions.

/pdf/ask-for-information-retrieval-part-ii-results-of-a-design-4axkwxgxws.pdf

Nicholas J. Belkin

Papers

Information filtering and information retrieval: two sides of the same coin?

Ask for information retrieval: part I.: background and theory

A case for interaction: a study of interactive information retrieval behavior and effectiveness

Cases, scripts, and information-seeking strategies: On the design of interactive information retrieval systems

Ask for information retrieval: part ii. results of a design study