TY - JOUR TI - On Relevance, Probabilistic Indexing and Information Retrieval AU - Maron, M. E. AU - Kuhns, J. L. T2 - Journal of the ACM AB - 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. DA - 1960/07// PY - 1960 DO - 10.1145/321033.321035 DP - ACM Digital Library VL - 7 IS - 3 SP - 216 EP - 244 LA - en SN - 0004-5411 UR - http://doi.acm.org/10.1145/321033.321035 Y2 - 2019/01/27/23:02:51 ER - TY - CONF TI - Optimizing search engines using clickthrough data AU - Joachims, Thorsten T2 - KDD '02 AB - This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous approaches to learning retrieval functions from examples exist, they typically require training data generated from relevance judgments by experts. This makes them difficult and expensive to apply. The goal of this paper is to develop a method that utilizes clickthrough data for training, namely the query-log of the search engine in connection with the log of links the users clicked on in the presented ranking. Such clickthrough data is available in abundance and can be recorded at very low cost. Taking a Support Vector Machine (SVM) approach, this paper presents a method for learning retrieval functions. From a theoretical perspective, this method is shown to be well-founded in a risk minimization framework. Furthermore, it is shown to be feasible even for large sets of queries and features. The theoretical results are verified in a controlled experiment. It shows that the method can effectively adapt the retrieval function of a meta-search engine to a particular group of users, outperforming Google in terms of retrieval quality after only a couple of hundred training examples. C1 - Edmonton, Alberta, Canada C3 - Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining DA - 2002/07/23/ PY - 2002 DO - 10.1145/775047.775067 DP - dl.acm.org SP - 133 EP - 142 LA - en PB - ACM SN - 978-1-58113-567-1 UR - http://dl.acm.org/citation.cfm?id=775047.775067 Y2 - 2019/01/18/20:54:23 ER - TY - JOUR TI - The Probabilistic Relevance Framework: BM25 and Beyond AU - Robertson, Stephen AU - Zaragoza, Hugo T2 - Foundations and Trends® in Information Retrieval AB - The Probabilistic Relevance Framework (PRF) is a formal framework for document retrieval, grounded in work done in the 1970–1980s, which led to the development of one of the most successful text-retrieval algorithms, BM25. In recent years, research in the PRF has yielded new retrieval models capable of taking into account document meta-data (especially structure and link-graph information). Again, this has led to one of the most successful Web-search and corporate-search algorithms, BM25F. This work presents the PRF from a conceptual point of view, describing the probabilistic modelling assumptions behind the framework and the different ranking algorithms that result from its application: the binary independence model, relevance feedback models, BM25 and BM25F. It also discusses the relation between the PRF and other statistical models for IR, and covers some related topics, such as the use of non-textual features, and parameter optimisation for models with free parameters. DA - 2009/12/17/ PY - 2009 DO - 10.1561/1500000019 DP - www.nowpublishers.com VL - 3 IS - 4 SP - 333 EP - 389 J2 - INR LA - en SN - 1554-0669, 1554-0677 ST - The Probabilistic Relevance Framework UR - https://www.nowpublishers.com/article/Details/INR-019 Y2 - 2019/01/18/20:09:44 ER -