A personalised semantic and spatial information retrieval system based on user's modelling and accessibility measure

Baazaoui, Hajer; Haddad, Mohamed Ramzi; Ghezala, Henda Ben

doi:10.1504/ijmcdm.2014.060427

Cited by 4 publications

(1 citation statement)

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“…Such data may include, for example, keywords related to users' opinions and responses to surveys or explicit weights assigned to items' features and categories. Multi-criteria and utility-based recommendation approaches use such data in order to infer users' interests and personalise recommendations (Baazaoui-Zgha et al, 2014).…”

Section: Data Acquisition and Modellingmentioning

confidence: 99%

An adaptive and interactive recommendation model for consumers' behaviours prediction

Haddad

Baazaoui

2018

IJDMMM

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Recommendation algorithms aim at predicting customers' interests and purchases using different ideas and hypotheses. Consequently, system designers need to choose the recommendation approach that is the most suitable with regard to their products' nature and consumers' behaviours within the application field. In this paper, we propose an adaptive recommendation model based on statistical modelling to assist consumers facing choice overload by predicting their interests and consumption behaviours. We also propose a dynamic variant of the model taking into account the recommendations' time-value during interactive online recommendation scenarios. Our proposal has endured a two-fold evaluation. First, we conducted an offline comparative study on the MovieLens recommendation dataset in order to assess our model's performance with regard to several widely adopted recommendation techniques. Then, the model was evaluated within a real time online news recommendation platform to highlight its adaptability, scalability and efficiency in a highly interactive application domain.

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Section: Data Acquisition and Modellingmentioning

confidence: 99%