Towards a graph-based user profile modeling for a session-based personalized search

Daoud, Mariam; Lechani, Lynda-Tamine; Boughanem, Mohand

doi:10.1007/s10115-009-0232-0

Cited by 40 publications

(31 citation statements)

References 37 publications

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“…Below, we give an overview of the graph-based ontological representation of the user profile detailed in our previous work [2]. User profiles are built over each identified situation by combining graph-based query profiles.…”

Section: Learning the User Profilementioning

confidence: 99%

Inferring situation-based interests for mobile users

Bouidghaghen

Tamine

2011

Proceedings of the 2011 ACM Symposium on Applied Computing

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Section: Learning the User Profilementioning

confidence: 99%

Inferring situation-based interests for mobile users

Bouidghaghen

Tamine

2011

Proceedings of the 2011 ACM Symposium on Applied Computing

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“…The profile evolves as the data evolve. According to [10] the management of the evolution of the user profile is a complementary process to build a user profile and its adaptation refers to changes in the user's interests over time. The evolution of the profile is dependent to the time constraint.…”

Section: A Techniques For Updating the User Profilementioning

confidence: 99%

“…The evolution of the profile is dependent to the time constraint. To manage this change, Daoud et al, [10] distinguish the short-term profile (representing the interests of current research sessions) and long-term profile (representing the persistent interests deduced from entire research history). Zayani et al, [35] distinguish permanent features that represent the personal data that are generally stable over time, and changing characteristics such as interests and preferences.…”

Section: A Techniques For Updating the User Profilementioning

confidence: 99%

Dynamic enrichment of social users' interests

Mezghani¹,

Zayani²,

Amous³

et al. 2014

2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS)

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 13109 Abstract-In a social context, the user is more and more an active contributor for producing social information. Then, he needs a tailored information reflecting his current needs and interests in every period of time. This aims to provide a better adaptation while accessing the information space by integrating users' interests dynamic. Indeed, users' interests may change and become "outdated" through time. So, an interest judged as relevant in a period of time may fluctuate in the next period of time. Moreover, analysing the classic user behaviour to deduce his current interests is a difficult task. In fact, his behaviour isn't always reflecting his real interests. In this paper, we propose a new approach for enriching the user profile in an evolutionary environment such as a social network. The enrichment takes into account: i) the social behaviour and more precisely the tagging behaviour (that reflects user's interests) and ii) the temporal information (that reflects the dynamic evolution of users' interests). Our approach focus on the concept of temperature that reflects the importance of a resource in each period of time. This concept is used to infer common interests of users tagging the same "important" resource. The originality of our approach relies on combining information tags, users and resources in a way that guarantees a better enrichment for the social user profile. Our approach has been tested and evaluated with the Delicious social database and shows interesting precision values.

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“…Expanding queries from user profiles has been studied in the field of information retrieval and Semantic Web search [8,13,29,31]. Many of them are based on user inputs of interests and cannot reflect the dynamic changing process through time.…”

Section: Related Workmentioning

confidence: 99%

User-centric query refinement and processing using granularity-based strategies

et al. 2010

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Under the context of large-scale scientific literatures, this paper provides a usercentric approach for refining and processing incomplete or vague query based on cognitiveand granularity-based strategies. From the viewpoints of user interests retention and granular information processing, we examine various strategies for user-centric unification of search and reasoning. Inspired by the basic level for human problem-solving in cognitive science, we refine a query based on retained user interests. We bring the multi-level, multi-perspective strategies from human problem-solving to large-scale search and reasoning. The power/ exponential law-based interests retention modeling, network statistics-based data selection, and ontology-supervised hierarchical reasoning are developed to implement these strategies. As an illustration, we investigate some case studies based on a large-scale scientific literature dataset, DBLP. The experimental results show that the proposed strategies are potentially effective.

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Towards a graph-based user profile modeling for a session-based personalized search

Cited by 40 publications

References 37 publications

Inferring situation-based interests for mobile users

Inferring situation-based interests for mobile users

Dynamic enrichment of social users' interests

User-centric query refinement and processing using granularity-based strategies

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