Abstract
Recommender systems have been an efficient strategy to deal with information overload by producing personalized predictions. Recommendation systems based on deep learning have accomplished magnificent results, but most of these systems are traditional recommender systems that use a single rating. In this work, we introduce a multi-criteria collaborative filtering recommender by combining deep neural network and matrix factorization. Our model consists of two parts: the first part uses a fused model of deep neural network and matrix factorization to predict the criteria ratings and the second one employs a deep neural network to predict the overall rating. The experimental results on two datasets, including a real-world dataset, show that the proposed model outperformed several state-of-the-art methods across different datasets and performance evaluation metrics.
Highlights
In the era of big data, while the World Wide Web keeps growing exponentially, the size and complexity of many websites (Google, YouTube, Netflix, Amazon, and others) grow along with it, making it increasingly difficult and time-consuming for the users of these websites to find the item they are looking for
Dataset We evaluated our model on two multi-criteria rating datasets, a real-world TripAdvisor1 dataset and a Movies2 dataset
We can see that our model achieves the best performance on both datasets, significantly outperforming each of deep multi-criteria collaborative filtering (DMCCF) model, single Deep neural network (DNN), and the other state-of-the-art methods on all the evaluation metrics
Summary
In the era of big data, while the World Wide Web keeps growing exponentially, the size and complexity of many websites (Google, YouTube, Netflix, Amazon, and others) grow along with it, making it increasingly difficult and time-consuming for the users of these websites to find the item (movie, music, restaurant, book, and product in general) they are looking for. Recommender systems (RSs) provide personalized suggestion for items that the user might like [1]. Exploiting the information from users’ ratings can be useful to solve one of the problems recommender systems suffer from, predicting users’ preferences about an item using a single rating. This is a clear limitation, since the user who makes a choice might take into consideration more than one aspect of the item. In a movie recommender system, some users may like a movie based on its plot, direction, or conflict, while others may like the same movie but for its acting, characters, or any other attribute of that movie. RSs use many techniques, Collaborative Filtering (CF) is the most commonly used, and it makes recommendations based
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