Ratings meet reviews, a combined approach to recommend

By Guang Ling, Michael R. Lyu, Irwin King. 2014

381 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Learning Distributed Representations from Reviews for Collaborative Filtering (2015, methodology): “For RMR, we only report the results from [12].”
Ask the GRU: Multi-task Learning for Deep Text Recommendations (2016, methodology): “Existing hybrid methods have used unsupervised learning objectives on text content to regularize the parameters of the recommendation model [4, 23, 24].”
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text (2019, methodology): “Combining matrix factorization and topic modelling technique (Topic MF) is an effective way to integrate ratings with item contents [2, 29, 33].”
Artificial intelligence in recommender systems (2020, methodology): “Ling et al. extended this method to make the learnt latent topic interpretable, thus enabling the recommendation of completely “cold” items [188].”
Generate Natural Language Explanations for Recommendation (2021, background): “Since user textual reviews are informative and be er re ect user preferences, a lot of research explored the possibility of incorporating user reviews to improve the recommendation quality [3, 20, 22, 43, 49] and recommendation explainability [4, 6, 18, 29, 39, 47], which helps to enhance the e ecti”
A Context-Aware User-Item Representation Learning for Item Recommendation (2017, methodology): “Some researchers [2, 1, 4, 20, 3] propose to employ topic modeling techniques to learn latent topic factors from text.”
Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews (2018, methodology): “• RMR [23].”
Explainable recommendation with fusion of aspect information (2018, background): “(3) LDAMF [19] LDAMF leverages the information in reviews with LDA model and regards topic distribution of each item as latent factors of corresponding item in a matrix factorization.”
Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation (2019, background): “xplainable recommendation has also attracted a lot of attention in recent years [43]. Early approaches attempt to use topic models to generate intuitive explanations for recommendation results, e.g., [20, 23, 34]. Zhang et al. [44] propose EFM which aligns the latent dimensions with explicit product”
Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction (2017, methodology): “Recently using review text information has shown to improve rating prediction accuracy, especially for users and items with few ratings [8, 14, 17].”
Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews (2017, background): “More recently, [8], [16] and [27] went beyond [18] by using more complicated graphical models to predict unknown ratings based on collaborative ltering and topic modeling of user reviews.”
TriRank: Review-aware Explainable Recommendation by Modeling Aspects (2015, methodology): “Subsequently, [17, 30] adopted a full Bayesian treatment to combine topics and latent factors for rating prediction.”
A Capsule Network for Recommendation and Explaining What You Like and Dislike (2019, methodology): “These works integrate the latent semantic topics into the factor learning models [1, 18, 20, 26, 29].”
Convolutional Matrix Factorization for Document Context-Aware Recommendation (2016, neutral): “Collaborative Filtering; Document Modeling; Contexual Information”
Neural Attentional Rating Regression with Review-level Explanations (2018, background): “In the last few years, there is a large literature exploiting text review information for improving the rating prediction performance, such as HFT[27], RMR[26], EFM[44],TriRank[11], RBLT[39], and sCVR[32].”