Enhancing long tail item recommendation in collaborative filtering: An econophysics-inspired approach

Recommender systems have been immensely successful in overcoming information overload problem through personalized suggestions to consumers. Traditional recommendation algorithms tend to recommend more popular items. A significant number of items in an enterprise are non-popular (long tail items) due to lack of visibility in recommendations. These long tail items are left unsold and result in a significant loss to the business. The consumers on the other end are deprived of receiving relevant item recommendations. In this paper, we propose two approaches inspired from econophysics to recommend long tail items. The proposed approaches selectively inject ratings to the long tail items to diminish the bias towards the popular items by utilizing the existing rating information. Subsequently, the injected rating datasets are used to provide recommendations. The results on real-world datasets show that the proposed approaches outperform the existing techniques in mitigating long tail effect with little or no drop in accuracy.

Automated summary

Recommender systems have successfully tackled information overload by providing personalized suggestions, but traditional algorithms favor popular items, neglecting long tail items. The proposed approaches inject ratings to long tail items based on existing data to offer better recommendations.