Our results indicate that parallel convolutions of filter lengths up to three are usually enough for capturing relevant text features.
Driving and music listening are two inseparable everyday activities for millions of people today in the world.
Quality of word embeddings and performance of their applications depends on several factors like training method, corpus size and relevance etc.
This paper describes the solution of the POLINKS team to the RecSys Challenge 2019 that focuses on the task of predicting the last click-out in a session-based interaction.
The public availability of collections containing user preferences is of vital importance for performing offline evaluations in the field of recommender systems.
This work investigates the role of factors like training method, training corpus size and thematic relevance of texts in the performance of word embedding features on sentiment analysis of tweets, song lyrics, movie reviews and item reviews.
Also cold-start and data sparsity are the two traditional and top problems being addressed in 23 and 22 studies each, while movies and movie datasets are still widely used by most of the authors.
In this paper, we present sequeval, a software tool capable of performing the offline evaluation of a recommender system designed to suggest a sequence of items.
For these reasons, we introduce RecLab, an open source software for evaluating recommender systems in a distributed fashion.