TriRank: Review-aware Explainable Recommendation by Modeling Aspects

@article{He2015TriRankRE,
  title={TriRank: Review-aware Explainable Recommendation by Modeling Aspects},
  author={Xiangnan He and Tao Chen and Min-Yen Kan and Xiao Chen},
  journal={Proceedings of the 24th ACM International on Conference on Information and Knowledge Management},
  year={2015}
}
  • Xiangnan He, Tao Chen, Xiao Chen
  • Published 17 October 2015
  • Computer Science
  • Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
Most existing collaborative filtering techniques have focused on modeling the binary relation of users to items by extracting from user ratings. Aside from users' ratings, their affiliated reviews often provide the rationale for their ratings and identify what aspects of the item they cared most about. We explore the rich evidence source of aspects in user reviews to improve top-N recommendation. By extracting aspects (i.e., the specific properties of items) from textual reviews, we enrich the… 

Figures and Tables from this paper

Dual-Prior Review-Based Matrix Factorization for Recommendation System
TLDR
A collaborative filtering framework, Dual-Prior Review-based Matrix Factorization (DPRMF), a model integrates review information into probabilistic matrix factorization (PMF) model using convolutional neural networks (CNNs) to generate the review text information vector and considering the review information a prior constraint of interaction between user and item.
Exploiting Ratings, Reviews and Relationships for Item Recommendations in Topic Based Social Networks
TLDR
Employing Poisson factorization, TSNPF fully exploits the wealth of information in rating scores, review text and social relationships altogether and makes use of similarities between user pairs with social relationships, which results in a comprehensive understanding of user preferences.
Learning Aspect-aware High-order Representations from Ratings and Reviews for Recommendation
TLDR
A new recommendation model is proposed, namely AHOR, to jointly distill rating- based features and review-based features, which are derived from ratings and reviews, respectively, and a novel graph neural network is introduced to learn aspect-aware high-order representations.
Leveraging Review Properties for Effective Recommendation
TLDR
A novel review properties-based recommendation model (RPRM) that learns which review properties are more important than others in capturing the usefulness of reviews, thereby enhancing the recommendation results.
Social Collaborative Viewpoint Regression with Explainable Recommendations
TLDR
This paper proposes a latent variable model, called social collaborative viewpoint regression (sCVR), for predicting item ratings based on user opinions and social relations, and uses so-called viewpoints, represented as tuples of a concept, topic, and a sentiment label from both user reviews and trusted social relations.
UniWalk: Explainable and Accurate Recommendation for Rating and Network Data
TLDR
UniWalk is proposed, an explainable and accurate recommender system that exploits both social network and rating data and combines both data into a unified graph, learns latent features of users and items, and recommends items to each user through the features.
A Zero Attentive Relevance Matching Networkfor Review Modeling in Recommendation System
TLDR
This model implements a relevance matching model with regularized training losses to discover user relevant information from long item reviews, and it also adapts a zero attention strategy to dynamically balance the item-dependent and item-independent information extracted from user reviews.
Graphing else matters: exploiting aspect opinions and ratings in explainable graph-based recommendations
TLDR
This paper proposes to exploit embeddings extracted from graphs that combine information from ratings and aspect-based opinions expressed in textual reviews, and adapt and evaluate state-of-the-art graph embedding techniques over graphs generated from Amazon and Yelp reviews on six domains, outperforming baseline recommenders.
Dual Learning for Explainable Recommendation: Towards Unifying User Preference Prediction and Review Generation
TLDR
A unified dual framework of how to inject the probabilistic duality of the two tasks in the training stage is designed, and a transfer learning based model for preference prediction and review generation is proposed.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 38 REFERENCES
Opinion-Driven Matrix Factorization for Rating Prediction
TLDR
The approach proposed in this paper is providing a simple, personalized and scalable rating prediction framework utilizing both ratings provided by users and opinions inferred from their reviews, demonstrating the effectiveness of the proposed framework.
Ratings meet reviews, a combined approach to recommend
TLDR
A unified model that combines content-based filtering with collaborative filtering, harnessing the information of both ratings and reviews is proposed, which can alleviate the cold-start problem and learn latent topics that are interpretable.
Explicit factor models for explainable recommendation based on phrase-level sentiment analysis
TLDR
The Explicit Factor Model (EFM) is proposed to generate explainable recommendations, meanwhile keep a high prediction accuracy, and online experiments show that the detailed explanations make the recommendations and disrecommendations more influential on user's purchasing behavior.
ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines
TLDR
This paper presents ItemRank, a random-walk based scoring algorithm, which can be used to rank products according to expected user preferences, in order to recommend top-rank items to potentially interested users.
EigenRank: a ranking-oriented approach to collaborative filtering
TLDR
This paper proposes a collaborative filtering approach that addresses the item ranking problem directly by modeling user preferences derived from the ratings and shows that the proposed approach outperforms traditional collaborative filtering algorithms significantly on the NDCG measure for evaluating ranked results.
A Random Walk Model for Item Recommendation in Social Tagging Systems
TLDR
This article proposes to deal with the sparsity problem in social tagging by applying random walks on ternary interaction graphs to explore transitive associations between users and items and demonstrates that this approach can effectively alleviate theSparsity problem and improve the quality of item recommendation.
Hidden factors and hidden topics: understanding rating dimensions with review text
TLDR
This paper aims to combine latent rating dimensions (such as those of latent-factor recommender systems) with latent review topics ( such as those learned by topic models like LDA), which more accurately predicts product ratings by harnessing the information present in review text.
Item-based collaborative filtering recommendation algorithms
TLDR
This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)
TLDR
A probabilistic model based on collaborative filtering and topic modeling is proposed that allows it to capture the interest distribution of users and the content distribution for movies; it provides a link between interest and relevance on a per-aspect basis and it allows us to differentiate between positive and negative sentiments on aPer-Aspect basis.
Comment-based multi-view clustering of web 2.0 items
TLDR
This paper systematically investigates how user-generated comments can be used to improve the clustering of Web 2.0 items and proposes CoNMF (Co-regularized Non-negative Matrix Factorization), which extends NMF for multi-view clustering by jointly factorizing the multiple matrices through co-regularization.
...
1
2
3
4
...