PPIAS: STATISTICAL LANGUAGE MODELS FOR PERSONALISING RANKINGS IN INFORMATION ACCESS SYSTEMS

Recommender Systems (RecSys) aim, given a set of users, a set of items and a set of users'ratings to items, generate personalised item recommendations for users. Traditionally, RecSys can exploit information both from the past interaction of users and products and from the content of the items to generate new suggestions for users. These systems have proven key to facilitating access to information, products and services. Specifically, it is estimated that a significant percentage of e-commerce transactions are motivated by recommendations: for example, Amazon sales increased by 29% after integrating a recommendation engine.

In this project, we want to advance the state of the art, proposing new models of recommendation that, with a solid formal probabilistic basis, may help to increase sales and improve products and the satisfaction of buyers. These models and their translation into domains and instances of actual use in the business community contribute, through the quality of its recommendations, to the development of the digital economy.

Objectives

An increasingly important research area is the translation of classic Information Retrieval approaches to the Recommendation task. In particular, in this research project, we propose the use of probabilistic Language Models to the item recommendation task. Recently, we developed the first formalisations obtaining high effectiveness figures. Given the previous positive experience, we want to extend the predictability of these models beyond the collaborative filtering approach considering new estimates and models that include and integrate different content information, capturing contextual and temporal aspects. Furthermore, we propose the integration of Bayesian optimization techniques to develop models that not only generate tailored product suggestions but also generate them in a personalised manner, adapting the recommendation models to the particularities of the users. All these objectives are constrained by a common core objective which is transversal: efficiency, scalability and robustness of such methods in relation to their translation into real applications in the
productive sector.

Recommender Systems (RecSys) aim, given a set of users, a set of items and a set of users'ratings to items, generate personalised item recommendations for users. Traditionally, RecSys can exploit information both from the past interaction of users and products and from the content of the items to generate new suggestions for users. These systems have proven key to facilitating access to information, products and services. Specifically, it is estimated that a significant percentage of e-commerce transactions are motivated by recommendations: for example, Amazon sales increased by 29% after integrating a recommendation engine. In this project, we want to advance the state of the art, proposing new models of recommendation that, with a solid formal probabilistic basis, may help to increase sales and improve products and the satisfaction of buyers. These models and their translation into domains and instances of actual use in the business community contribute, through the quality of its recommendations, to the development of the digital economy.An increasingly important research area is the translation of classic Information Retrieval approaches to the Recommendation task. In particular, in this research project, we propose the use of probabilistic Language Models to the item recommendation task. Recently, we developed the first formalisations obtaining high effectiveness figures. Given the previous positive experience, we want to extend the predictability of these models beyond the collaborative filtering approach considering new estimates and models that include and integrate different content information, capturing contextual and temporal aspects. Furthermore, we propose the integration of Bayesian optimization techniques to develop models that not only generate tailored product suggestions but also generate them in a personalised manner, adapting the recommendation models to the particularities of the users. All these objectives are constrained by a common core objective which is transversal: efficiency, scalability and robustness of such methods in relation to their translation into real applications in the productive sector. - David Enrique Losada Carril