Explicit factor models for explainable recommendation based on phrase-level sentiment analysis

By Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, Shaoping Ma. 2014

858 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yongfeng Zhang
2. Xiangnan He
3. Xu Chen
4. Tat-seng Chua
5. Xiang Wang
6. Xing Xie
7. Qingyao Ai
8. Liqiang Nie

In Information Systems

1. Yongfeng Zhang
2. Xiangnan He
3. Xu Chen
4. Tat-seng Chua
5. Xiang Wang
6. Xing Xie
7. Liqiang Nie
8. Fuli Feng

Citations in the corpus

• RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (2018, background): “The explanations are usually based on community tags [30], social networks [24], aspect [2], and phrase sentiment [44] Since RippleNet explores users’ interests based on the KG, it provides a new point of view of explainability by tracking the paths from a user’s history to an item with high relevan”
• Knowledge-aware Autoencoders for Explainable Recommender Systems (2018, neutral): “Other interesting works include: a RS based on Restricted Boltzmann Machines which looks at the rating distribution to identify the most explainable items [1], a Latent Factor Model leveraging users reviews to compute more transparent recommendations [23] and, finally, a novel approach based on movi”
• Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “While recent evidence also suggests that these two goals may not necessarily conflict with each other when designing recommendation models (Bilgic et al., 2004; Zhang et al., 2014a).”
• Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects (2019, methodology): “We adopt the method proposed by Zhang et al. (2014) to build a sentiment lexicon which includes a set of fine-grained aspects from the whole dataset.”
• Sequential Recommendation with User Memory Networks (2018, neutral): “user textual reviews [2, 12, 33, 34]) for explanations.”
• Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation (2018, background): “[15] propose an explicit factor model to predict ratings while generating feature-level explanations about why an item is or is not recommended.”
• Generate Natural Language Explanations for Recommendation (2021, methodology): “As a result, researchers have looked into explainable recommendation systems in the recent years [2, 3, 8, 16, 27, 38, 39, 44, 45], which can not only provide users with the recommendation lists, but also intutive explanations about why these items are recommended.”
• Why I like it: multi-task learning for recommendation and explanation (2018, neutral): “In [50], the authors propose the Explicit Factor Model, which utilizes phrase-level sentiment analysis for identifying “topics” that are correlated with user ratings.”
• Learning to Rank Features for Recommendation over Multiple Categories (2016, methodology): “The methods proposed in [17, 18] could capture the feature-sentiment pairs like (screen, perfect) and (earphone, bad), which would provide finer-grained analysis of the textual reviews as compared with HFT, and could help to make more accurate recommendations.”
• Do users rate or review?: boost phrase-level sentiment labeling with review-level sentiment classification (2014, background): “Categories and Subject Descriptors I.2.7 [Artificial Intelligence]: Natural Language Processing; H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval - Classification Keywords Sentiment Analysis; Sentiment Classification; Sentiment Lexicon Construction; Optimization”
• Rating-Boosted Latent Topics: Understanding Users and Items with Ratings and Reviews (2016, methodology): “Some work uses sentiment analysis methods to boost the performance of rating prediction automatically [Jakob et al., 2009; Leung et al., 2006; Zhang et al., 2014a; Zhang, 2015; Zhang et al., 2015].”
• Reinforcement Knowledge Graph Reasoning for Explainable Recommendation (2019, background): “However, researchers have noticed the difficulty of explaining the recommendation results in latent factor or latent representation models, making explainable recommendation [33, 34] an important research problem for the community.”
• Predicting the popularity of web 2.0 items based on user comments (2014, background): “[38] performed phraselevel sentiment analysis of user comments to improve the accuracy and explainability of recommender system.”
• Visually Explainable Recommendation (2018, methodology): “Existing explainable recommendation mod-elsusuallyinterprettherecommendationsbased onuserreviews[20, 25, 37].”
• Explainable Recommendation via Multi-Task Learning in Opinionated Text Data (2018, methodology): “They are restricted to some generic explanations, such as “You might be interested in [feature], on which this product performs well” [38].”
• In2Rec: Influence-based Interpretable Recommendation (2019, methodology): “To make use of textual reviews, topic model is integrated with PMF to determine the explicit review-aware item features which are aligned to the latent factor for explanations generation [20, 31].”
• Explanation Mining: Post Hoc Interpretability of Latent Factor Models for Recommendation Systems (2018, background): “Whenanexternaltextualdatasource is available (e.g. product reviews [36] or news articles [15]), topic modelling can be used to discover the themes present in the text [9, 15, 31] which can then be combined with the rating predictions to provide an interpretation of the latent factors.”
• Towards Knowledge-Based Recommender Dialog System (2019, background): “Besides accuracy, explainability is also an important aspect when evaluating recommender systems (Zhang et al., 2014; Zhang and Chen, 2018; Wang et al., 2018b).”