Ask the GRU: Multi-Task Learning for Deep Text Recommendations (1609.02116v2)

Abstract: In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor models can be extended naturally to leverage text by employing an explicit mapping from text to factors. This enables recommendations for new, unseen content, and may generalize better, since the factors for all items are produced by a compactly-parametrized model. Previous work has used topic models or averages of word embeddings for this mapping. In this paper we present a method leveraging deep recurrent neural networks to encode the text sequence into a latent vector, specifically gated recurrent units (GRUs) trained end-to-end on the collaborative filtering task. For the task of scientific paper recommendation, this yields models with significantly higher accuracy. In cold-start scenarios, we beat the previous state-of-the-art, all of which ignore word order. Performance is further improved by multi-task learning, where the text encoder network is trained for a combination of content recommendation and item metadata prediction. This regularizes the collaborative filtering model, ameliorating the problem of sparsity of the observed rating matrix.

• The paper proposes using GRUs to encode text for recommendation systems, achieving up to 34% relative improvement in Recall@50 by capturing word order and leveraging multi-task learning.
• Using GRUs preserves word order in text encoding, leading to more accurate and nuanced representations compared to models that ignore sequence.
• Multi-task learning, incorporating metadata prediction, effectively regularizes the model and improves performance in sparse datasets and cold-start situations.

Multi-Task Learning for Deep Text Recommendations Using Recurrent Neural Networks

This paper presents a method for text-based item recommendation through the use of deep recurrent neural networks (RNNs), specifically gated recurrent units (GRUs). The work is focused on improving the accuracy of recommendations by leveraging the textual content associated with items, such as scientific paper abstracts, for collaborative filtering tasks. The approach is particularly aimed at addressing the cold-start problem, which is pervasive in recommendation systems when there is insufficient historical data for new items.

The proposed method extends latent factor models by incorporating an efficient mapping from text sequences to latent vectors using GRUs. The GRUs are trained end-to-end, optimizing the collaborative filtering task directly. This distinguishes the proposed approach from traditional models that rely heavily on topic models or word embeddings while ignoring word order. By preserving word order, GRUs capture more nuanced meanings, leading to representations that are more informative and precise for recommendation tasks.

The paper addresses sparsity issues in collaborative filtering datasets by applying multi-task learning. This involves not just training the text encoder network for content recommendation but also for metadata prediction. The multi-task setup utilizes auxiliary item metadata, like tags, as part of a regularization strategy. This dual-task approach mitigates overfitting, predominantly in cases with sparse user-item matrices, ensuring that the model generalizes better in both warm and cold-start scenarios.

The evaluation is conducted on two datasets from CiteULike, capturing user interaction with scientific paper abstracts. The results showcase considerable improvement in recommendation accuracy, reporting up to 34% relative improvement in the Recall@50 metric over existing approaches such as collaborative topic regression (CTR) and an embedding model based on word averages. The inclusion of multi-task learning further enhances performance across all tested models.

Key Findings and Implications

• Order-sensitive Encoding: The use of GRUs allows the model to effectively capture and utilize the sequence of words within the textual content, providing an advantage over models that use order-insensitive approaches.
• Cold-start Capability: By mapping text into latent factors through GRUs, new items without prior interaction data can still be accurately represented, mitigating the cold-start issue.
• Multi-task Learning Benefits: Incorporating metadata prediction tasks enriches the training process, providing significant performance gains over focused single-task models and unregularized baselines.
• Robustness in Sparse Environments: The proposed method's robustness to data sparsity and its ability to operate effectively on sparse datasets like CiteULike are notable, showing the method’s applicability to a wide range of real-world scenarios.

This research suggests a promising direction for developing recommendation systems capable of exploiting rich textual information. The integration of deep learning techniques offers new opportunities to refine and optimize the personalization of content across diverse application domains. Future explorations could further involve additional modalities, like images and extended user data, to enhance the model's comprehensiveness and applicability. Additionally, expanding the scope of auxiliary tasks or refining the multi-task learning framework could yield further insights into maximizing the shared representations' utility in recommendation systems.