Neural Rating Regression with Abstractive Tips Generation for Recommendation (1708.00154v1)

Abstract: Recently, some E-commerce sites launch a new interaction box called Tips on their mobile apps. Users can express their experience and feelings or provide suggestions using short texts typically several words or one sentence. In essence, writing some tips and giving a numerical rating are two facets of a user's product assessment action, expressing the user experience and feelings. Jointly modeling these two facets is helpful for designing a better recommendation system. While some existing models integrate text information such as item specifications or user reviews into user and item latent factors for improving the rating prediction, no existing works consider tips for improving recommendation quality. We propose a deep learning based framework named NRT which can simultaneously predict precise ratings and generate abstractive tips with good linguistic quality simulating user experience and feelings. For abstractive tips generation, gated recurrent neural networks are employed to "translate" user and item latent representations into a concise sentence. Extensive experiments on benchmark datasets from different domains show that NRT achieves significant improvements over the state-of-the-art methods. Moreover, the generated tips can vividly predict the user experience and feelings.

• The paper introduces NRT, a novel deep learning framework that jointly models user rating prediction and abstractive tips generation for improved recommendation systems.
• The NRT framework employs latent factor modeling for rating prediction and a GRU network for abstractive tips, trained jointly; it consistently outperformed baselines in experiments.
• Integrating abstract tips enhances user engagement and decision-making, while future work could explore incorporating context or hybrid strategies to improve recall.

Neural Rating Regression with Abstractive Tips Generation for Recommendation: An Overview

Introduction

The paper presents a novel deep learning framework named Neural Rating Regression with Abstractive Tips Generation (NRT) designed to improve recommendation systems by jointly modeling rating prediction and tips generation. This approach considers a user's product assessment as comprising two facets: providing a numerical rating and expressing experiences or feelings through brief tips. The integration of these facets aims to enhance recommendation quality, a departure from traditional systems that utilize only item specifications and user reviews for such tasks.

Methodology

NRT leverages a neural network architecture, incorporating both a regression model for rating prediction and a sequence generation model for tips. The crux of the approach is twofold:

1. Latent Factor Modeling for Rating Prediction: The model employs user and item latent factors that are learned through a multilayer perceptron network. This enables the projection of user-item interactions into a shared latent space, thereby facilitating more refined rating predictions. The model seeks to address limitations in existing methods that assume users and items operate within the same vector space.
2. Abstractive Tips Generation: This component utilizes a Gated Recurrent Unit (GRU) network to generate concise sentences that capture user experience and feelings. Importantly, this is achieved without directly copying from existing text, which distinguishes it from extractive approaches.

The framework benefits from a multi-task learning paradigm, whereby both the rating prediction and tips generation are trained jointly. This strategy helps ensure that the user and item latent factors used in both tasks are optimally tuned to capture complex interpersonal dynamics.

Experimental Setup

The authors evaluate their framework using four large-scale datasets across different domains, demonstrating the model's applicability and robustness. Metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are employed for evaluating rating predictions, while ROUGE scores are used to assess the linguistic quality of the generated tips.

Results and Discussion

The NRT framework consistently outperforms baseline models in both rating prediction and tips generation tasks. Specifically, it demonstrates significant improvements in MAE and RMSE across all tested datasets. For tips generation, NRT achieves the highest ROUGE precision and F1 scores, underscoring its ability to generate linguistically coherent and sentiment-consistent tips. However, challenges remain in improving recall scores, possibly due to the inherently concise nature of human-generated tips.

Implications and Future Work

The inclusion of the abstract tips generation feature provides several advantages, including enhanced user engagement and more informed decision-making in recommendation systems. Moreover, the research presents a methodological shift in exploring how qualitative and quantitative aspects of user interaction can be synthesized in a recommendation model.

Future developments could focus on expanding the context-awareness of tips generation. There is potential to explore the integration of supplementary data sources, such as contextual or temporal elements, to further refine personalized recommendations. Additionally, addressing the observed discrepancy in recall metrics could involve exploring alternative neural architectures or hybrid strategies that combine extractive and abstractive methods.

In conclusion, NRT contributes to recommendation system research by introducing a robust framework that effectively merges rating prediction with natural language generation, offering a comprehensive tool for user experience approximation in E-commerce and beyond. This groundbreaking work lays a foundation for more sophisticated models that can both predict user preferences and articulate nuanced user sentiments seamlessly.