Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings

Landin, Alfonso; Parapar, Javier; Barreiro, Álvaro

doi:10.1007/978-3-030-45442-5_27

Title

Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings

Author(s)

Landin, Alfonso

Parapar, Javier

Barreiro, Álvaro

Date

2020-04-08

Citation

Landin A., Parapar J., Barreiro Á. (2020) Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings. In: Jose J. et al. (eds) Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science, vol 12036. Springer, Cham. https://doi.org/10.1007/978-3-030-45442-5_27

Abstract

[Abstract] Nowadays, item recommendation is an increasing concern for many companies. Users tend to be more reactive than proactive for solving information needs. Recommendation accuracy became the most studied aspect of the quality of the suggestions. However, novel and diverse suggestions also contribute to user satisfaction. Unfortunately, it is common to harm those two aspects when optimizing recommendation accuracy. In this paper, we present EER, a linear model for the top-N recommendation task, which takes advantage of user and item embeddings for improving novelty and diversity without harming accuracy.