Dynamic Clustering Personalization for Recommending Long Tail Items

Recommendation strategies are used in several contexts in order to bring potential users closer to products with a strong probability of interest. When recomendations focus on niche items, they are called recommendations in the long tail. In these cases, they also look for less popular items and try to find your target custumer, niche market. This paper proposes a long tail recommendation approach that prioritizes relevance, diversity and popularity of recommended items. For that, a hybrid approach based on two techniques are used. The first is clustering with dynamic parameters that adapt from according to the dataset used and the second is a type of Markov chains for to calculate the distance of interest of a user to an item of relevance for this user. The results show that the techniques used have a better relevance indexes at the same time more diverse and less popular recommendations.