Mixed Materials
User behaviour modelling in a multi-dimensional environment for personalization and recommendation
| Τίτλος: | User behaviour modelling in a multi-dimensional environment for personalization and recommendation |
|---|---|
| Συγγραφείς: | Rawat, Rakesh |
| Στοιχεία εκδότη: | Queensland University of Technology 2010 |
| Τύπος εγγράφου: | Mixed materials |
| Περίληψη: | Handling information overload online, from the user's point of view is a big challenge, especially when the number of websites is growing rapidly due to growth in e-commerce and other related activities. Personalization based on user needs is the key to solving the problem of information overload. Personalization methods help in identifying relevant information, which may be liked by a user. User profile and object profile are the important elements of a personalization system. When creating user and object profiles, most of the existing methods adopt two-dimensional similarity methods based on vector or matrix models in order to find inter-user and inter-object similarity. Moreover, for recommending similar objects to users, personalization systems use the users-users, items-items and users-items similarity measures. In most cases similarity measures such as Euclidian, Manhattan, cosine and many others based on vector or matrix methods are used to find the similarities. Web logs are high-dimensional datasets, consisting of multiple users, multiple searches with many attributes to each. Two-dimensional data analysis methods may often overlook latent relationships that may exist between users and items. In contrast to other studies, this thesis utilises tensors, the high-dimensional data models, to build user and object profiles and to find the inter-relationships between users-users and users-items. To create an improved personalized Web system, this thesis proposes to build three types of profiles: individual user, group users and object profiles utilising decomposition factors of tensor data models. A hybrid recommendation approach utilising group profiles (forming the basis of a collaborative filtering method) and object profiles (forming the basis of a content-based method) in conjunction with individual user profiles (forming the basis of a model based approach) is proposed for making effective recommendations. A tensor-based clustering method is proposed that |
| Όροι ευρετηρίου: | server log data analysis, vector space models, matrix methods of data analysis, tensor space modelling of web users, clustering, association rule mining, user profile, group profile, object profiling, recommendation, Thesis |
| Σύνδεσμος: | Rawat, Rakesh (2010) User behaviour modelling in a multi-dimensional environment for personalization and recommendation. PhD thesis, Queensland University of Technology. |
| Διαθεσιμότητα: | Open access content. Open access content free_to_read |
| Σημείωση: | application/pdf |
| Other Numbers: | ATUTQ oai:eprints.qut.edu.au:48135 Faculty of Science and Technology 829179660 |
| Πηγή συνεισφοράς: | QUEENSLAND UNIV OF TECH From OAIster®, provided by the OCLC Cooperative. |
| Αριθμός Καταχώρησης: | edsoai.ocn829179660 |
| Βάση Δεδομένων: | OAIster |
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