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Graph-based Extractive Explainer for Recommendations

Peng Wang, Renqin Cai, Hongning Wang
2022 Proceedings of the ACM Web Conference 2022  
In this work, we develop a graph attentive neural network model that seamlessly integrates user, item, attributes and sentences for extraction-based explanation.  ...  Explanations in a recommender system assist users make informed decisions among a set of recommended items.  ...  CONCLUSION AND FUTURE WORK In this paper, we present a graph neural network based extractive solution for explaining a system's recommended items to its users.  ... 
doi:10.1145/3485447.3512168 fatcat:tm5y3n3warcavd7dnrfq3abwxu

A Meta-graph Solution for Recommender Systems

Neda Abolhassani
2020 Zenodo  
The proposed meta-graph solution for recommender systems is a living process for semi-automatically resolving recommendations using guided queries upon a knowledge graph.  ...  In addition, this solution is explainable; it can provide comprehensible recommendations that show the reason for each result along with a statistical measure.  ...  RECOMMENDER SYSTEM Action Ratings for Explainable Recommendations 0 10 20 30 40 50 60 70 80 90 Type Category Assignee Asset, Location, Facility Priority Data Source Significance  ... 
doi:10.5281/zenodo.3813855 fatcat:ifadgc6va5gtnel7rhibg5jdey

Transferrable Framework Based on Knowledge Graphs for Generating Explainable Results in Domain-Specific, Intelligent, Information Retrieval

Hasan Abu-Rasheed, Christian Weber, Johannes Zenkert, Mareike Dornhöfer, Madjid Fathi
2022 Informatics  
Utilizing the same KG, we develop graph-based components for generating textual and visual explanations of the retrieved information, taking into account the domain requirements and supporting the transferability  ...  The aim of our work is to provide a comprehensive approach for constructing explainable IR and recommendation algorithms, which are capable of adopting to domain requirements and are usable in multiple  ...  Acknowledgments: Authors acknowledge and thank Chirayu Upadhyay for his support with the recommender system in Use Case 2. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/informatics9010006 fatcat:kazl7m3egzbrpkx763wuqyxdei

Ontology Reasoning Towards Sentimental Product Recommendations Explanations

2019 International journal of recent technology and engineering  
Secondly, we reviewed sentiment-based and ontology based recommendation systems. Finally, prospects for the research in opinion mining is discussed.  ...  Explainable Recommendation algorithms help the user by providing explainable recommendations, which improves user satisfaction. Recently, many researchers proposed explainable recommendations.  ...  product recommendations using the content-based knowledge graph.  ... 
doi:10.35940/ijrte.c6852.098319 fatcat:takmhb4ernhnzh6trs2yhgtiwq

PERS: A Personalized and Explainable POI Recommender System [article]

Ramesh Baral, Tao Li
2017 arXiv   pre-print
as a bipartite relation, represents it as a location-aspect category bipartite graph, and models the explainable recommendation with the notion of ordered dense subgraph extraction using bipartite core-based  ...  user-aspect category bipartite relation as a bipartite graph, and models the explainable recommendation using bipartite core-based and ranking-based methods.  ...  To the best of our knowledge, the exploitation of aspects for explainable POI recommendation has barely been explored. Aspect-based approaches Yang et al.  ... 
arXiv:1712.07727v1 fatcat:ljx5axvgzfcr5cclofyyfs3pmu

TriRank

Xiangnan He, Tao Chen, Min-Yen Kan, Xiao Chen
2015 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management - CIKM '15  
ü Graph-based method. -Top-K recommendation è Vertex ranking. ü Good accuracy. ü Explainable. ü Transparent. ü Offline training + online learning.  ...  COLING'10]: phrase/sentence patterns. 22 Oct 2015 16 CIKM2015 -Review-aware Explainable Recommendation Aspect Extraction 3.  ... 
doi:10.1145/2806416.2806504 dblp:conf/cikm/HeCKC15 fatcat:mj2saduwibbpnp4qeuosgagabi

Graphing else matters: exploiting aspect opinions and ratings in explainable graph-based recommendations [article]

Iván Cantador, Andrés Carvallo, Fernando Diez, Denis Parra
2022 arXiv   pre-print
Different from previous work, in this paper, we propose to exploit embeddings extracted from graphs that combine information from ratings and aspect-based opinions expressed in textual reviews.  ...  In particular, current recommendation methods based on graph embeddings have shown state-of-the-art performance. These methods commonly encode latent rating patterns and content features.  ...  Graph-based recommendations We can distinguish between three main types of approaches that use graphs for recommendation purposes: path-based, embedding-based, and unified.  ... 
arXiv:2107.03226v2 fatcat:777hnvwc6rfy5evqvaxbi6mzpy

Knowledge Graph-based Recommendation Systems: The State-of-the-art and Some Future Directions

Sajisha P. S, Anoop V.S, Ansal K. A
2019 International Journal of Machine Learning and Networked Collaborative Engineering  
A system that learns rules which are explainable for recommendation tasks, with knowledge graphs is reported very recently which was proposed by Weizhi Ma et. al. [3] .  ...  The advantage of graph-based recommendation systems is that heterogeneous data sources can be used for providing better recommendation results.  ... 
doi:10.30991/ijmlnce.2019v03i03.004 fatcat:45ornhc7qzceffqffv7z4xfdd4

Graph Learning Approaches to Recommender Systems: A Review [article]

Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z. Sheng, Mehmet Orgun, Longbing Cao, Nan Wang, Francesco Ricci, Philip S. Yu
2020 arXiv   pre-print
In this paper, we provide a systematic review of GLRS, on how they obtain the knowledge from graphs to improve the accuracy, reliability and explainability for recommendations.  ...  Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).  ...  In recent years, OKGRS have been widely studied to enhance the explainability of recommendations, e.g., using it to extract multi-level user interest from the item ontology graph [Gao et al., 2019 , Wang  ... 
arXiv:2004.11718v1 fatcat:w6ug72c4pvgoxjcf643tmddfii

Hierarchical Aspect-guided Explanation Generation for Explainable Recommendation [article]

Yidan Hu, Yong Liu, Chunyan Miao, Gongqi Lin, Yuan Miao
2021 arXiv   pre-print
Explainable recommendation systems provide explanations for recommendation results to improve their transparency and persuasiveness.  ...  An aspect-guided graph pooling operator is proposed to extract the aspect-relevant information from the review-based syntax graphs to model the user's preferences on an item at the aspect level.  ...  Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of National Research Foundation, Singapore.  ... 
arXiv:2110.10358v2 fatcat:kgdikqr4mzfabep4zjvzv6wrom

Rating and aspect-based opinion graph embeddings for explainable recommendations [article]

Iván Cantador, Andrés Carvallo, Fernando Diez
2022 arXiv   pre-print
Differently from previous work, in this paper, we propose to exploit embeddings extracted from graphs that combine information from ratings and aspect-based opinions expressed in textual reviews.  ...  In particular, recent recommendation methods based on graph embeddings have shown state-of-the-art performance. In general, these methods encode latent rating patterns and content features.  ...  RELATED WORK Graph-based recommendations. We can distinguish between three main types of approaches that use graphs for recommendation purposes: path-based, embedding-based and unified.  ... 
arXiv:2107.03385v2 fatcat:lxictoffuffhpncxsvy2tucacu

Explainable Entity-based Recommendations with Knowledge Graphs [article]

Rose Catherine, Kathryn Mazaitis, Maxine Eskenazi, William Cohen
2017 arXiv   pre-print
Explainable recommendation is an important task. Many methods have been proposed which generate explanations from the content and reviews written for items.  ...  Our method jointly ranks items and knowledge graph entities using a Personalized PageRank procedure to produce recommendations together with their explanations.  ...  [4] was an early work that assessed di erent ways of explaining recommendations in a collaborative ltering (CF) -based recommender system.  ... 
arXiv:1707.05254v1 fatcat:w5i3k4pdvjanpc3qs76doaztju

Explainable Recommendation: A Survey and New Perspectives [article]

Yongfeng Zhang, Xu Chen
2020 arXiv   pre-print
In this survey, we provide a comprehensive review for the explainable recommendation research.  ...  In recent years, a large number of explainable recommendation approaches -- especially model-based methods -- have been proposed and applied in real-world systems.  ...  Acknowledgements We sincerely thank the reviewers for providing the valuable reviews and constructive suggestions. The work is partially supported by National Science Foundation (IIS-1910154).  ... 
arXiv:1804.11192v10 fatcat:scsd3htz65brbiae35zd3nixe4

Measuring Vertex Centrality in Co-occurrence Graphs for Online Social Tag Recommendation

Iván Cantador, David Vallet, Joemon M. Jose
2009 European Conference on Principles of Data Mining and Knowledge Discovery  
The tags (vertices) of this reduced graph that have the highest vertex centrality constitute our recommendations, which are finally ranked based on TF-IDF and personalisation based techniques.  ...  We present a social tag recommendation model for collaborative bookmarking systems.  ...  For that purpose, a combination of tag co-occurrence graph centrality, tag frequency, and tag-based personalisation metrics was performed.  ... 
dblp:conf/pkdd/CantadorVJ09 fatcat:fqbklrchtbb3rcbe62xzk45zci

COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce [article]

Zuohui Fu, Yikun Xian, Yaxin Zhu, Yongfeng Zhang, Gerard de Melo
2020 arXiv   pre-print
In this work, we present a new dataset for conversational recommendation over knowledge graphs in e-commerce platforms called COOKIE.  ...  The dataset is constructed from an Amazon review corpus by integrating both user-agent dialogue and custom knowledge graphs for recommendation.  ...  and explainable recommendation.  ... 
arXiv:2008.09237v1 fatcat:agnch5bxxjcebcswpy3mkbltte
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