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Found 10 papers, 5 papers with code
MTSI-BERT: A Session-aware Knowledge-based Conversational Agent
In the last years, the state of the art of NLP research has made a huge step forward.
Sequeval: A Framework to Assess and Benchmark Sequence-based Recommender Systems
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.
Information Retrieval
A Distributed and Accountable Approach to Offline Recommender Systems Evaluation
For these reasons, we introduce RecLab, an open source software for evaluating recommender systems in a distributed fashion.
Information Retrieval
Predict your Click-out: Modeling User-Item Interactions and Session Actions in an Ensemble Learning Fashion
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.
Hybrid Recommender Systems: A Systematic Literature Review
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.
Word Embeddings for Sentiment Analysis: A Comprehensive Empirical Survey
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.
All You Need is Ratings: A Clustering Approach to Synthetic Rating Datasets Generation
The public availability of collections containing user preferences is of vital importance for performing offline evaluations in the field of recommender systems.
Quality of Word Embeddings on Sentiment Analysis Tasks
Quality of word embeddings and performance of their applications depends on several factors like training method, corpus size and relevance etc.
Mood-based On-Car Music Recommendations
Driving and music listening are two inseparable everyday activities for millions of people today in the world.
A Data-driven Neural Network Architecture for Sentiment Analysis
Our results indicate that parallel convolutions of filter lengths up to three are usually enough for capturing relevant text features.
