Learning-to-Rank for Hybrid User Profiles
Houssem Safi, Maher Jaoua, Lamia Hadrich Belguith · Research in Computing Science · 2017
In the context of the Personalized Information Retrieval method applied to the Arabic language, this work consists in presenting a personalized ranking method based on a model of supervised learning and its implementation.This method consists of four steps, namely, the user's modeling, the document / query / profile matching, the learning to rank and the result classification.Thus, we proposed a hybrid approach of the user's modeling that relies on both multidimensional and conceptual representations by exploiting Arabic semantic resources.Therefore, to determine the similarity between the document and the profile, we used a learning model that exploits the users' explicit pertinence judgments.In this context, we have proposed learning semantic features related to the user's profile (represented by hierarchies of concepts).The predicted model will then be used in the ordering phase to classify other documents that result from a new query submitted by the user.In this context, we have proposed a novel multi-objective function to order the documents (based on the classic Retrieval Status Value function and the predictive personalized Retrieval Status Value function).Finally, we have explained the evaluation results of the predictive model and the ranking method.These evaluations, which were made based on a training corpus and a test corpus, led to some interesting results.Indeed, the proposed semantic learning criteria connected to the user profile have a significant impact on the performance of our personalized document ranking system.