Neural Attentional Rating Regression with Review-level Explanations

Chen, Chong; Zhang, Min; Liu, Yiqun; Ma, Shaoping

doi:10.1145/3178876.3186070

Cited by 457 publications

(364 citation statements)

References 20 publications

Supporting

Mentioning

363

Contrasting

Order By: Relevance

“…Recently, deep neural networks have been adopted to enhance recommender systems [2,47]. Most of them utilize deep neural networks as feature learning tools to extract features from auxiliary information such as text description of an item [5,18,37] and visual information of images [45]. NeuMF [14], is a matrix factorization based deep recommendation method, which uses deep neural networks to explore the non-linearity in user-item interactions.…”

Section: Related Workmentioning

confidence: 99%

Deep social collaborative filtering

Fan

Yin

et al. 2019

Proceedings of the 13th ACM Conference on Recommender Systems

View full text Add to dashboard Cite

Recommender systems are crucial to alleviate the information overload problem in online worlds. Most of the modern recommender systems capture users' preference towards items via their interactions based on collaborative filtering techniques. In addition to the user-item interactions, social networks can also provide useful information to understand users' preference as suggested by the social theories such as homophily and influence. Recently, deep neural networks have been utilized for social recommendations, which facilitate both the user-item interactions and the social network information. However, most of these models cannot take full advantage of the social network information. They only use information from direct neighbors, but distant neighbors can also provide helpful information. Meanwhile, most of these models treat neighbors' information equally without considering the specific recommendations. However, for a specific recommendation case, the information relevant to the specific item would be helpful. Besides, most of these models do not explicitly capture the neighbor's opinions to items for social recommendations, while different opinions could affect the user differently. In this paper, to address the aforementioned challenges, we propose DSCF, a Deep Social Collaborative Filtering framework, which can exploit the social relations with various aspects for recommender systems. Comprehensive experiments on two-real world datasets show the effectiveness of the proposed framework.

show abstract

Section: Related Workmentioning

confidence: 99%

Deep social collaborative filtering

Fan

Yin

et al. 2019

Proceedings of the 13th ACM Conference on Recommender Systems

View full text Add to dashboard Cite

show abstract

“…Chen et al [5] propose an attentive collaborative filtering framework, where each item is segmented into component-level elements, and attention scores are learned for these components for obtaining a better representation of items. Attention networks are also applied in group recommendation [2], sequential recommendation [37], review-based recommendation [4,29,32] and context-aware recommendation [26].…”

Section: Related Workmentioning

confidence: 99%

A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists

Wang

Niu

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

View full text Add to dashboard Cite

User-generated item lists are a popular feature of many different platforms. Examples include lists of books on Goodreads, playlists on Spotify and YouTube, collections of images on Pinterest, and lists of answers on question-answer sites like Zhihu. Recommending item lists is critical for increasing user engagement and connecting users to new items, but many approaches are designed for the item-based recommendation, without careful consideration of the complex relationships between items and lists. Hence, in this paper, we propose a novel user-generated list recommendation model called AttList. Two unique features of AttList are careful modeling of (i) hierarchical user preference, which aggregates items to characterize the list that they belong to, and then aggregates these lists to estimate the user preference, naturally fitting into the hierarchical structure of item lists; and (ii) item and list consistency, through a novel self-attentive aggregation layer designed for capturing the consistency of neighboring items and lists to better model user preference. Through experiments over three real-world datasets reflecting different kinds of user-generated item lists, we find that AttList results in significant improvements in NDCG, Precision@k, and Recall@k versus a suite of state-ofthe-art baselines. Furthermore, all code and data are available at https://github.com/heyunh2015/AttList.

show abstract

“…The other class is based on based on reviews. Recently, a large number of literatures have been proposed for exploiting textual review information to provide explanations while improving the rating prediction performance, for examples, EFM [26], HFT [15], AMF [10], and NARRE [2].…”

Section: A Explainable Recommendationmentioning

confidence: 99%

“…Recently, some research works have incorporated deep learning techniques, including RBM [8], Autoencoders [21], RNN [23], and CNN [22], into recommender systems to improve the performance of user and item embedding learning. In addition to combining deep neural networks with collaborative filtering [2], the existing deep learning based recommendation models often integrate textual reviews to enhance the performance of latent factor modeling [1], [10], [18], [26], [27]. For example, DeepCoNN [27] uses convolutional neural networks to process reviews, and utilizes deep learning technology to jointly model user and item from textual reviews.…”

Section: B Deep Learning For Recommendationmentioning

confidence: 99%

Hybrid Deep Embedding for Recommendations with Dynamic Aspect-Level Explanations

Luo

Yang

2019

2019 IEEE International Conference on Big Data (Big Data)

View full text Add to dashboard Cite

Explainable recommendation is far from being well solved partly due to three challenges. The first is the personalization of preference learning, which requires that different items/users have different contributions to the learning of user preference or item quality. The second one is dynamic explanation, which is crucial for the timeliness of recommendation explanations. The last one is the granularity of explanations. In practice, aspect-level explanations are more persuasive than item-level or user-level ones. In this paper, to address these challenges simultaneously, we propose a novel model called Hybrid Deep Embedding (HDE) for aspect-based explainable recommendations, which can make recommendations with dynamic aspect-level explanations. The main idea of HDE is to learn the dynamic embeddings of users and items for rating prediction and the dynamic latent aspect preference/quality vectors for the generation of aspect-level explanations, through fusion of the dynamic implicit feedbacks extracted from reviews and the attentive user-item interactions. Particularly, as the aspect preference/quality of users/items is learned automatically, HDE is able to capture the impact of aspects that are not mentioned in reviews of a user or an item. The extensive experiments conducted on real datasets verify the recommending performance and explainability of HDE. The source code of our work is available at https://github.com/lola63/HDE-Python.

show abstract

Neural Attentional Rating Regression with Review-level Explanations

Cited by 457 publications

References 20 publications

Deep social collaborative filtering

Deep social collaborative filtering

A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists

Hybrid Deep Embedding for Recommendations with Dynamic Aspect-Level Explanations

Contact Info

Product

Resources

About