id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-020806-lof49r72	Landin, Alfonso	Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings	2020-03-24		.txt	text/plain	2373	150	52	title: Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings 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. In this paper, we propose a method to augment an existing recommendation linear model to make more diverse and novel recommendations, while maintaining similar accuracy results. Experiments conducted on three datasets show that our proposal outperforms the original model in both novelty and diversity while maintaining similar levels of accuracy. On the other side, as results in Table 3 show, ELP is able to provide good figures in novelty and diversity, thanks to the embedding model capturing non-linear relations between users and items. It is common in the field of recommender systems for methods with lower accuracy to have higher values in diversity and novelty. FISM: factored item similarity models for top-n recommender systems	./cache/cord-020806-lof49r72.txt	./txt/cord-020806-lof49r72.txt