Neural Collaborative Embedding From Reviews for Recommendation

Feng, Xingjie; Zeng, Yunze

doi:10.1109/access.2019.2931357

Cited by 11 publications

(8 citation statements)

References 30 publications

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“… D ual A ttention M utual L earning ( DAML ) 17 : This model utilizes local and mutual attention of CNN to jointly learn user and item features from reviews, and neural factorization machine is introduced to predict ratings. N eural C ollaborative E mbedding M odel ( NCEM ) 41 : This model utilizes an aspect-level attention layer to measure the correlation degree of reviews towards different aspects, and a multi-layer neural factorization machine is introduced to predict ratings. C ross-domain Recommendation Framework Via A spect T ransfer N etwork ( CATN ) 42 : The model learns the aspect level features of each user and item from the corresponding reviews through attention mechanism, then semantic matching is performed between such aspect level features to predict ratings.…”

Section: Methodsmentioning

confidence: 99%

Attentional factorization machine with review-based user–item interaction for recommendation

Li,

Jin,

Yuan

2023

Sci Rep

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In recommender systems, user reviews on items contain rich semantic information, which can express users’ preferences and item features. However, existing review-based recommendation methods either use the static word vector model or cannot effectively extract long sequence features in reviews, resulting in the limited ability of user feature expression. Furthermore, the impact of different or useless feature interactions between users and items on recommendation performance is ignored. Therefore, we propose an attentional factorization machine with review-based user–item interaction for recommendation (AFMRUI), which first leverages RoBERTa to obtain the embedding feature of each user/item review, and combines bidirectional gated recurrent units with attention network to highlight more useful information in both user and item reviews. Then we adopt AFM to learn user–item feature interactions to distinguish the importance of different user–item feature interactions and further to obtain more accurate rating prediction, so as to promote recommendation. Finally, we conducted performance evaluation on five real-world datasets. Experimental results on five datasets demonstrated that the proposed AFMRUI outperformed the state-of-the-art review-based methods regarding two commonly used evaluation metrics.

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Section: Methodsmentioning

confidence: 99%

Attentional factorization machine with review-based user–item interaction for recommendation

Li,

Jin,

Yuan

2023

Sci Rep

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“…The structure of DeepCoNN is shown in Figure 6, which mainly consists of two parallel CNN networks [18]. Finally, using a sharing layer to couple the user network and item network.…”

Section: Feature Extractingmentioning

confidence: 99%

Fusing User Reviews Into Heterogeneous Information Network Recommendation Model

Chen

Tian

et al. 2022

IEEE Access

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With the advent of the information epoch and the development of Big Data, users are constantly overwhelmed in massive information online. As an effective method to deal with this dilemma of information overload, recommendation system has become a popular area of research for the past few years. However, most of the current recommendation models fail to make the most of the ample resources hidden behind auxiliary data and user reviews. For this reason, we put forward the FHRec model. We try to combine heterogeneous information network with deep learning technology to improve recommendation performance. The working mechanism is to represent the rich auxiliary data by using heterogeneous information network, learning the features of entities through network embedding; in the meantime, by adopting deep learning technology to mine the features of entities from review texts, using the attention mechanism to fuse feature vectors of entities, inputting them into the neural network for recommendation; the last step is that we use Yelp dataset and Douban movie dataset to verify the FHRec model. The experimental results show that the FHRec behaves better than the traditional comparison algorithm on the Yelp dataset and Douban movie dataset.

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“…Common types of explanations include review-level and word-level. In a review-level explanation, the attention mechanism is applied to measure every review's contribution to the item (or user) embedding (Chen et al, 2018;Feng and Zeng, 2019). High-scoring reviews are then selected to serve as explanations.…”

Section: Generated Explanationsmentioning

confidence: 99%

“…The two said factors are crucial as they can affect recommendation performance (Pilehvar and Camacho-Collados, 2019;Wang et al, 2018a). To deal with such dilemmas, NCEM (Feng and Zeng, 2019) and BENEFICT (Pugoy and Kao, 2020) use a pre-trained BERT model to obtain review features. BERT's advantage lies in its full retention of global context and word frequency information (Feng and Zeng, 2019).…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Unsupervised Extractive Summarization-Based Representations for Accurate and Explainable Collaborative Filtering

Pugoy¹,

Kao²

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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We pioneer the first extractive summarizationbased collaborative filtering model called ES-COFILT. Our proposed model specifically produces extractive summaries for each item and user. Unlike other types of explanations, summary-level explanations closely resemble real-life explanations. The strength of ES-COFILT lies in the fact that it unifies representation and explanation. In other words, extractive summaries both represent and explain the items and users. Our model uniquely integrates BERT, K-Means embedding clustering, and multilayer perceptron to learn sentence embeddings, representation-explanations, and user-item interactions, respectively. We argue that our approach enhances both rating prediction accuracy and user/item explainability. Our experiments illustrate that ESCOFILT's prediction accuracy is better than the other state-of-the-art recommender models. Furthermore, we propose a comprehensive set of criteria that assesses the real-life explainability of explanations. Our explainability study demonstrates the superiority of and preference for summary-level explanations over other explanation types.

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Neural Collaborative Embedding From Reviews for Recommendation

Cited by 11 publications

References 30 publications

Attentional factorization machine with review-based user–item interaction for recommendation

Attentional factorization machine with review-based user–item interaction for recommendation

Fusing User Reviews Into Heterogeneous Information Network Recommendation Model

Unsupervised Extractive Summarization-Based Representations for Accurate and Explainable Collaborative Filtering

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