Neural Rating Regression with Abstractive Tips Generation for Recommendation

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.

SIGIR 2017

References in corpus (10)

• Distributed Representations of Words and Phrases and their Compositionality
• Neural Machine Translation by Jointly Learning to Align and Translate
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• ADADELTA: An Adaptive Learning Rate Method
• Distributed Representations of Sentences and Documents
• Neural Collaborative Filtering
• Joint Deep Modeling of Users and Items Using Reviews for Recommendation
• Learning Distributed Representations from Reviews for Collaborative Filtering
• Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks
• Abstractive Multi-Document Summarization via Phrase Selection and Merging

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• PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems
• Visually Explainable Recommendation
• Generate Natural Language Explanations for Recommendation
• Towards Explainable Conversational Recommender Systems
• Metric Factorization: Recommendation beyond Matrix Factorization
• Neural Attentive Session-based Recommendation
• Explanation as a Defense of Recommendation
• A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss
• Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation
• Debiased Explainable Pairwise Ranking from Implicit Feedback
• Comparative Explanations of Recommendations
• Persona-Aware Tips Generation
• Automatic Article Commenting: the Task and Dataset
• Explainable Recommendation with Personalized Review Retrieval and Aspect Learning
• AliCoCo: Alibaba E-commerce Cognitive Concept Net
• Personalized Transformer for Explainable Recommendation
• DeepRec: An Open-source Toolkit for Deep Learning based Recommendation
• SoulMate: Short-text author linking through Multi-aspect temporal-textual embedding
• Multi-Objective Recommendations: A Tutorial
• Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning
• Explainable Recommendation with Simulated Human Feedback
• SeER: An Explainable Deep Learning MIDI-based Hybrid Song Recommender System
• Meta Matrix Factorization for Federated Rating Predictions
• Conceptualize and Infer User Needs in E-commerce
• Hierarchical Aspect-guided Explanation Generation for Explainable Recommendation
• Contrastive Learning for Aspect Representation towards Explainable Recommendation
• Hybrid Deep Embedding for Recommendations with Dynamic Aspect-Level Explanations
• Hierarchical Text Interaction for Rating Prediction
• Convolutional Quantum-Like Language Model with Mutual-Attention for Product Rating Prediction