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Projecte llegit

Títol: Benchmarking on web technologies and a recommender system development for E-commerce

Estudiants que han llegit aquest projecte:

Director/a: MESEGUER PALLARÈS, ROC

Departament: DAC

Títol: Benchmarking on web technologies and a recommender system development for E-commerce

Data inici oferta: 06-04-2012     Data finalització oferta: 06-12-2012


Estudis d'assignació del projecte:
    Tipus: Individual
     
    Lloc de realització: EETAC
     
    Paraules clau:
    E-commerce, recommender system
     
    Descripció del contingut i pla d'activitats:
    Internet has opened a window enabling retailers to sell to anyone, anywhere and at any time. E-commerce has completely changed the way of doing business and in this context appeared the “daily deals” or “group buying” web pages: a business model which attracts millions of customers through the online sale of experiences – like a dinner or a trip – or products with a high percentage of discount, possible due to the great number of buyers.
    Motivated by this context of growth, in this project a benchmarking on web functionalities is done for the main group buying web pages and some general electronic marketplaces.
    As a result of this benchmarking a web functionality is chosen to be analyzed and developed for an e-commerce deals web: recommender systems. This leads to a second part of this project, where the main techniques to implement a recommendation system are studied and then used to generate a proof of concept for a recommender engine to work in a group buying web environment.
    The goal of using a recommendation system in this environment is to make the right information arrive to the right costumers. Recommender systems are valuable both for users and businesses. From a consumer perspective, they may help the users to manage the information overload of the e-commerce world. From a corporate point of view, they may contribute to the cross-sell and upsell of products.
    In this project, an item-based collaborative filtering approach is used to generate the recommender engine for the group buying web environment. After the model is designed, a proof of concept focused in the recommender core is implemented. At the end, some evaluation techniques for the recommender system are described.
     
    Overview (resum en anglès):

    Internet has opened a window enabling retailers to sell to anyone, anywhere and at any time. E-commerce has completely changed the way of doing business and in this context appeared the “daily deals” or “group buying” web pages: a business model which attracts millions of customers through the online sale of experiences – like a dinner or a trip – or products with a high percentage of discount, possible due to the great number of buyers.
    Motivated by this context of growth, in this project a benchmarking on web functionalities is done for the main group buying web pages and some general electronic marketplaces.
    As a result of this benchmarking a web functionality is chosen to be analyzed and developed for an e-commerce deals web: recommender systems. This leads to a second part of this project, where the main techniques to implement a recommendation system are studied and then used to generate a proof of concept for a recommender engine to work in a group buying web environment.
    The goal of using a recommendation system in this environment is to make the right information arrive to the right costumers. Recommender systems are valuable both for users and businesses. From a consumer perspective, they may help the users to manage the information overload of the e-commerce world. From a corporate point of view, they may contribute to the cross-sell and upsell of products.
    In this project, an item-based collaborative filtering approach is used to generate the recommender engine for the group buying web environment. After the model is designed, a proof of concept focused in the recommender core is implemented. At the end, some evaluation techniques for the recommender system are described.

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