Mitigating long tail effect in recommendations using few shot learning technique

In this paper, we propose a novel framework to mitigate the long tail effect and overcome the limited ratings problem using few shot learning techniques. Siamese network, a type of few shot learning technique is found to be performing well in many domains with a limited number of instances in the recent past. In the proposed framework, vital statistics of each user are computed and this information is provided to deep siamese network. The trained siamese network is used to identify the long tail items that are similar to the liked items of each user. Finally, the identified long tail items are recommended to the appropriate users. We introduce three novel performance metrics to evaluate the long tail item recommendations. The proposed framework is evaluated on two real world datasets (MovieLens 1M and Netflix) and the results demonstrate that the proposed framework outperforms the traditional approaches and existing long-tail recommendation techniques.