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A case-based recommendation approach for market basket data
Gatzioura, Anna; Sànchez-Marrè, Miquel
Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. KEMLG - Grup d´Enginyeria del Coneixement i Aprenentatge Automàtic
In recent years, recommender systems have become an important part of various applications, supporting both customers and providers in their decision-making processes. However, these systems still must overcome limitations that reduce their performance, like recommendations' overspecialization, cold start, and difficulties when items with unequal probability distribution appear or recommendations for sets of items are asked. A novel approach, addressing the above issues through a case-based recommendation methodology, is presented here. The scope of the presented approach is to generate meaningful recommendations based on items' co-occurring patterns and to provide more insight into customers' buying habits. In contrast to current recommendation techniques that recommend items based on users' ratings or history, and to most case-based item recommenders that evaluate items' similarities, the implemented recommender uses a hierarchical model for the items and searches for similar sets of items, in order to recommend those that are most likely to satisfy a user.
Peer Reviewed
Àrees temàtiques de la UPC::Informàtica
Recommender Systems
Case Based Reasoning
User Preference Learning
Set of Items Recommendations
Items’ Cooccurring Patterns
Market Basket Analysis

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