2024-04-042024-04-042008-02-01Ă–zmutlu, H.C. vd. (2008). "Cross-validation of neural network applications for automatic new topic identification". Journal of the American Society for Information Science and Technology, 59(3), 339-362.1532-28821532-2890https://doi.org/10.1002/asi.20696https://asistdl.onlinelibrary.wiley.com/doi/full/10.1002/asi.20696https://hdl.handle.net/11452/41009The purpose of this study is to provide results from experiments designed to investigate-the cross-validation of an artificial neural network application to automatically identify topic changes in Web search engine user sessions by using data logs of different Web search engines for training and testing the neural network. Sample data logs from the FAST and Excite search engines are used in this study. The results of the study show that identification of topic shifts and continuations on a particular Web search engine user session can be achieved with neural networks that are trained on a different Web search engine data log. Although FAST and Excite search engine users differ with respect to some user characteristics (e.g., number of queries per session, number of topics per session), the results of this study demonstrate that both search engine users display similar characteristics as they shift from one topic to another during a single search session. The key finding of this study is that a neural network that is trained on a selected data log could be universal; that is, it can be applicable on all Web search engine transaction logs regardless of the source of the training data log.eninfo:eu-repo/semantics/closedAccessComputer scienceInformation science & library scienceData structuresIdentification (control systems)Search enginesUser interfacesEngine transactionSample data logsSearch sessionNeural networksInformation-seekingWeb queriesContextUsersRelevanceTrendsLifeCross-validation of neural network applications for automatic new topic identificationArticle0002528216000012-s2.0-39649103881339362593Computer science, information systemsInformation science & library scienceQuery Reformulation; Image Indexing; Digital Libraries