Mitigating long tail effect in recommendations using few shot learning technique

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Recommender system has been established as an effective tool for users in providing personalized suggestions in many domains, especially in e-commerce. In these domains, recommendations are provided based on the feedback (ratings) given by the users. However, recommendations provided by the traditional approaches are biased towards the popular items (items that receive more number of ratings). As a result, unpopular items are left out and these items remain un-recommended and unsold. These unpopular items form a “long tail” in the product space, resulting in a huge loss to the e-commerce industry. However, diminishing this long tail effect is a highly challenging and non-trivial task due to limited available rating information. Recommending long tail items helps in improving the item liquidation and recommendation diversity as well.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.