Time-Sensitive User Profile for Optimizing Search Personlization

Abstract

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 : 15229The contribution was presented at UMAP 2014 : http://www.um.org/umap2014/umap2014/index.html Any correspondence concerning this service should be sent to the repository administrator: staff-oatao@listes-diff.inp-toulouse.fr Abstract. Thanks to social Web services, Web search engines have the opportunity to afford personalized search results that better fit the user's information needs and interests. To achieve this goal, many personalized search approaches explore user's social Web interactions to extract his preferences and interests, and use them to model his profile. In our approach, the user profile is implicitly represented as a vector of weighted terms which correspond to the user's interests extracted from his online social activities. As the user interests may change over time, we propose to weight profiles terms not only according to the content of these activities but also by considering the freshness. More precisely, the weights are adjusted with a temporal feature. In order to evaluate our approach, we model the user profile according to data collected from Twitter. Then, we rerank initial search results accurately to the user profile. Moreover, we proved the significance of adding a temporal feature by comparing our method with baselines models that does not consider the user profile dynamics.

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Towards Improving e-Government Services Using Social Media-Based Citizen's Profile Investigation

“…Thus, considering the relative frequency of a term in a publication moment compared to the frequencies of all terms , we obtain the following content feature: (2) Owing to the constantly changes in the users' preferences, we use the day as a temporal unit. We use the same weighting scheme as in [11] where we combine short-term and long-term profiles by integrating the Kernel Gaussian function in order to give fresh terms higher value:…”

A Semantic Social Recommender System Using Ontologies Based Approach For Tunisian Tourism

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“…Where nTF is the normalized term frequency (Kacem et al, 2014) of the concept Ci at a current date Dc. K(Dc,Dj) is the Kernel Gaussian function as a temporal-biased function (Lv et al, 2009;Gerani et al, 2010) between the current date Dc and another date Dj.…”