Time-Sensitive User Profile for Optimizing Search Personlization

Ameni Kacem (1, 2) , Mohand Boughanem (3, 4) , Rim Faiz (1, 2)

1 LARODEC - Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus
2 UCAR - Université de Carthage (Tunisie)
3 IRIT-SIG - Systèmes d’Informations Généralisées
4 UT3 - Université Toulouse III - Paul Sabatier

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.

Mots clés: Personalized search; User profile; Freshness; Interests dynamics; Kernel function

Domaines: Théorie de l'information [cs.IT]; Recherche d'information [cs.IR]

Ameni Kacem, Mohand Boughanem, Rim Faiz. Time-Sensitive User Profile for Optimizing Search Personlization. 22nd International Conference on User Modeling, Adaptation and Personalization (UMAP 2014), Jul 2014, Aalborg, Denmark. pp.111-121, ⟨10.1007/978-3-319-08786-3_10⟩. ⟨hal-01387803⟩