A Case-Based Recommendation Approach for Market Basket Data

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.