A hybrid approach for user profiling

Poo, Danny Chiang Choon; Chng, Bee Lee; Goh, Jie-Mein

doi:10.1109/hicss.2003.1174242

Cited by 42 publications

(18 citation statements)

References 6 publications

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“…Generally, the hybrid method assigns the new user a default profile with the use of the collaborative method and further enhances the profile using the content-based method [1]. In the literature four hybrid user profiling techniques have been introduced [8]. These are called 'static content profiling', 'dynamic content profiling', 'static collaborative profiling', and 'dynamic collaborative profiling'.…”

Section: Hybrid Methodsmentioning

confidence: 99%

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User Profiling - A Short Review

Cufoglu¹

2014

IJCA

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In Today's technology driven world user profiles are the virtual representation of each user and they include a variety of user information such as personal, interest and preference data. These profiles are the outcome of the user profiling process and they are essential to service personalization. Different methods, techniques and algorithms have been proposed in the literature for the user profiling process. This paper aims to give an overview on the user profiling and its related concepts, and discuss the pros and cons of current methods for the future service personalization. Furthermore, it also give details about the simulations which have been carried out with well known classification and clustering algorithms with real world user profile dataset. This work is based on the doctoral thesis of the author.

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Section: Hybrid Methodsmentioning

confidence: 99%

“…Unlike static profiling, dynamic profiling uses the implicit method and analyzes user's behaviour pattern (e.g. usage history) to deter-mine user's interests [7] [8]. Here, the accuracy of the user profile depends on the amount of generated data through user-system interaction.…”

Section: Introductionmentioning

confidence: 99%

User Profiling - A Short Review

Cufoglu¹

2014

IJCA

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“…In text-based systems, finding similar patterns is often done by Collaborative Filtering (CF) approaches, which are widely used in recommendation systems [15] and web personalization [13]. Association Rules [13], Latent Semantic Analysis [5] and User Profiling [14] methods are specific techniques that have been applied successfully in text-based CF applications.…”

Section: Introductionmentioning

confidence: 99%

Learning image semantics from users relevance feedback

Shah-hosseini

Knapp

2004

Proceedings of the 12th Annual ACM International Conference on Multimedia

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In this paper, a learning method is proposed to improve the retrieval process in image databases. This method uses the search transaction logs in the system and user relevance feedback scores to create a semantic space of the image database. The semantic space includes many semantic classes and all the images in the database are clustered to these classes with different membership values. The sparsity problem in the transaction logs data is solved by filling the missing values by an estimation based on the image contents and image similarities. A Fuzzy clustering algorithm is developed to create the semantic classes and find image memberships in the classes.

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“…Balabanovic [3] has defined as content-based a recommendation based solely on a profile built up by analyzing the content of items which a specific user has rated in the past, whilst a collaborative recommendation relies on a similarity measure among user profiles, without taking the item content into account. Most recommendation systems combine both to overcome the limitations of each approach [14,3]. Albadvi et al have conducted an extensive survey on these hybrid techniques [1].…”

Section: Related Workmentioning

confidence: 99%