Filters








689 Hits in 3.8 sec

Personalised Search Time Prediction using Markov Chains

Vu Tran, David Maxwell, Norbert Fuhr, Leif Azzopardi
2017 Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval - ICTIR '17  
In this paper, we show how Markov models derived from search logs can be used for predicting search times, and describe a method for evaluating these predictions.  ...  Our experimental results show that by observing users for only 100 seconds, the personalised predictions are already signicantly better than global predictions.  ...  Figure 3 : 3 The mean absolute error of the predictions for each Markov chain model over the cuto times (refer to Section 4).  ... 
doi:10.1145/3121050.3121085 dblp:conf/ictir/TranMFA17 fatcat:ciwstegqwfgynke3lfpded4o54

21st Century Search and Recommendation: Exploiting Personalisation and Social Media [chapter]

Morgan Harvey, Fabio Crestani
2014 Lecture Notes in Computer Science  
We first tackle the problem of search result personalisation in the face of extremely sparse and noisy data from a query log.  ...  We describe a novel approach which uses query logs to build personalised ranking models in which user profiles are constructed based on the representation of clicked documents over a topic space.  ...  After the Markov chain has converged, samples from the chain are used (as per normal) to calculate the 3 posterior means (using Equations 1-3 above).  ... 
doi:10.1007/978-3-319-12511-4_5 fatcat:glhlh6czive2hfz67acdhof3ua

A Hybrid Approach for Spatial Web Personalization [chapter]

Yanwu Yang, Christophe Claramunt
2005 Lecture Notes in Computer Science  
In the context of Web personalization, Markov chains have been recently proposed to model user's navigational trails, in order to infer user preference and predict future visits through computation of  ...  Based on these principles, the research introduced in this paper develops a hybrid Web personalization approach that applies k-order Markov chains towards an integration of spatial proximity and semantic  ...  Markov Chains Personalization Markov chains are used extensively to predict the next state of a system given a sequence of previous states.  ... 
doi:10.1007/11599289_18 fatcat:6yvn577edzdardoeg5uhcu37be

Building user profiles from topic models for personalised search

Morgan Harvey, Fabio Crestani, Mark J. Carman
2013 Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13  
Further examination shows that the performance of the personalised system is particularly good in cases where prior knowledge of the search query is limited.  ...  In this work we use query logs to build personalised ranking models in which user profiles are constructed based on the representation of clicked documents over a topic space.  ...  After the Markov chain has converged, samples from the chain are used (as per normal) to calculate the 3 posterior means (using Equations 1-3 above).  ... 
doi:10.1145/2505515.2505642 dblp:conf/cikm/HarveyCC13 fatcat:t43z7b6yqva3rnhyznrusm47fm

Tripartite Hidden Topic Models for Personalised Tag Suggestion [chapter]

Morgan Harvey, Mark Baillie, Ian Ruthven, Mark J. Carman
2010 Lecture Notes in Computer Science  
In this paper we extend the latent Dirichlet allocation topic model to include user data and use the estimated probability distributions in order to provide personalised tag suggestions to users.  ...  We describe the resulting tripartite topic model in detail and show how it can be utilised to make personalised tag suggestions.  ...  which users are similar and providing personalised search.  ... 
doi:10.1007/978-3-642-12275-0_38 fatcat:l2txmarpgnalxgnttoe5agb5hq

Predictive Machine Learning for Personalised Medicine in Major Depressive Disorder [article]

Viktoria-Eleni Gountouna, Mairead Bermingham, Ksenia Kuznetsova, Daniel Urda Munoz, Felix Agakov, Sian Robson, Joeri Meijsen, Archie Campbell, Caroline Hayward, Eleanor Wigmore, Toni Clarke, Ana Maria Fernandez (+4 others)
2022 medRxiv   pre-print
Rank aggregation was used to combine results across ten different algorithms and identify highly predictive variables.  ...  We used machine learning in the Generation Scotland cohort to predict lifetime risk of depression and, among cases, recurrent depression.  ...  a novel approach: the Markov Chain 4 (MC4) algorithm, which was developed for rank aggregation of Internet search engine rankings (16) .  ... 
doi:10.1101/2022.02.11.22270724 fatcat:aryjurnfsne5vagdxwu3n2wf44

Toward a Better Understanding of News User Journeys: A Markov Chain Approach

Susan Vermeer, Damian Trilling
2020 Journalism Studies  
We propose the use of Markov chains. These models provide an effective and compact way to discover meaningful patterns in clickstream data.  ...  In particular, they capture the sequentiality in news use patterns.  ...  The research was supported by the Research Priority Area "Personalised Communication" of the University of Amsterdam.  ... 
doi:10.1080/1461670x.2020.1722958 fatcat:5gutfeui5jhzdgadhhv4fgycyy

Revisiting Neighbourhood-Based Recommenders For Temporal Scenarios

Alejandro Bellogín, Pablo Sánchez
2017 ACM Conference on Recommender Systems  
Methods such as matrix factorisation and Markov chains have been combined recently to model the temporal preferences of users in a sequential basis.  ...  Modelling the temporal context efficiently and effectively is essential to provide useful recommendations to users.  ...  Markov chain.  ... 
dblp:conf/recsys/BelloginS17 fatcat:raqtxzjuunhz3eb5ijkeylrlba

Representing interests as a hyperlinked document collection

Michelle Fisher, Richard Everson
2003 Proceedings of the twelfth international conference on Information and knowledge management - CIKM '03  
This model can be used to personalise information access tasks such as a personalised search engine or a personalised news service.  ...  By collecting hyper-text documents that a user views, creates or updates whilst at their computer, we are able to use not only the content of these documents but also the inter-connectivity of the collection  ...  The tens of thousands of results from a search engine must be tackled or the research paper repositories must be searched; we spend far too much time accessing the information rather than using it.  ... 
doi:10.1145/956863.956936 dblp:conf/cikm/FisherE03 fatcat:h3iv64xvb5bjtb6qjl7tolapry

Representing interests as a hyperlinked document collection

Michelle Fisher, Richard Everson
2003 Proceedings of the twelfth international conference on Information and knowledge management - CIKM '03  
This model can be used to personalise information access tasks such as a personalised search engine or a personalised news service.  ...  By collecting hyper-text documents that a user views, creates or updates whilst at their computer, we are able to use not only the content of these documents but also the inter-connectivity of the collection  ...  The tens of thousands of results from a search engine must be tackled or the research paper repositories must be searched; we spend far too much time accessing the information rather than using it.  ... 
doi:10.1145/956935.956936 fatcat:cqa6y7vsh5gtvhgvcyctyc5vty

Computing the Entropy of User Navigation in the Web

Mark Levene, George Loizou
2003 International Journal of Information Technology and Decision Making  
Finally, we present an extension of our technique to higher-order Markov chains by a suitable reduction of a higher-order Markov chain model to a first-order one.  ...  Herein we give a theoretical underpinning of user navigation in terms of the entropy of an underlying Markov chain modelling the web topology.  ...  Moreover, knowledge of the entropy of a typical trail and the stationary distribution of the underlying Markov chain can be used to personalise ranking algorithms, such as Google's PageRank [PBMW98] ,  ... 
doi:10.1142/s0219622003000768 fatcat:arvmcuwcxrgqnm72lnttvcxii4

Evaluating Variable Length Markov Chain Models for Analysis of User Web Navigation Sessions [article]

Jose Borges, Mark Levene
2006 arXiv   pre-print
In previous work we have proposed a method to dynamically extend the order of a Markov chain model and a complimentary method for assessing the predictive power of such a variable length Markov chain.  ...  Markov models have been widely used to represent and analyse user web navigation data.  ...  In Section 2 we introduce the variable length Markov chain methods we make use of.  ... 
arXiv:cs/0606115v1 fatcat:wjytkhxmnfayrf3xpb55mebmy4

On-Device User Intent Prediction for Context and Sequence Aware Recommendation [article]

Benu Madhab Changmai, Divija Nagaraju, Debi Prasanna Mohanty, Kriti Singh, Kunal Bansal, Sukumar Moharana
2019 arXiv   pre-print
Through a neighborhood searching method followed by a sequence matching algorithm, we search for the most relevant node to make the prediction.  ...  We propose a secure and efficient on-device mechanism to predict a user's next intention.  ...  above with Markov chains.  ... 
arXiv:1909.12756v1 fatcat:yt3hurmdvnddfhazel3bcdiiae

A Map-Based Recommendation System and House Price Prediction Model for Real Estate

Maryam Mubarak, Ali Tahir, Fizza Waqar, Ibraheem Haneef, Gavin McArdle, Michela Bertolotto, Muhammad Tariq Saeed
2022 ISPRS International Journal of Geo-Information  
A personalised real estate portal can use this information to suggest properties, assist homeowners and provide valuable real estate analytics.  ...  In 2015, global real estate was worth $217 trillion, which is approximately 2.7 times the global GDP; it also accounts for roughly 60% of all conventional global resources, making it one of the key factors  ...  The recommended procedure is taken as a sequential decision-making process, and the use of Markov decision chains have been suggested to create a model.  ... 
doi:10.3390/ijgi11030178 fatcat:xnxigz7abjeubnqjakvkonwnga

Artificial intelligence in healthcare—the road to precision medicine

Tran Quoc Bao Tran, Clea du Toit, Sandosh Padmanabhan
2021 Journal of Hospital Management and Health Policy  
Distillation of high-dimensional data across clinical, biological, patient-generated and environmental domains using ML and translating garnered insights into clinical practice requires not only extant  ...  Hidden markov (37) Real-time calibration and automatic drug dosing recommendation for chemotherapy treatment plans (Curate.AI) Hidden markov (38) Classifying prognostic phenotypes in heart failure  ...  (34) Detecting QRS complexes in single-lead ECG recordings Hidden markov (35) Real-time circadian phase estimation Hidden markov (36) Multi-channel EEG based automatic epileptic seizure detection  ... 
doi:10.21037/jhmhp-20-132 fatcat:4rszxxfto5hkzc5ixo5dixxck4
« Previous Showing results 1 — 15 out of 689 results