Q. Ai, V. Azizi, X. Chen, and Y. Zhang, Learning heterogeneous knowledge base embeddings for explainable recommendation, Algorithms, vol.11, p.9, 2018.

R. Andersen, C. Borgs, J. Chayes, J. Hopcraft, S. Vahab et al., Local computation of PageRank contributions, WAW, 2007.

R. Andersen, F. Chung, and K. Lang, Local graph partitioning using Pagerank vectors, FOCS, 2006.

K. Avrachenkov, N. Litvak, D. Nemirovsky, and N. Osipova,

, Monte Carlo methods in PageRank computation: When one iteration is sufficient, SIAM J. Numer. Anal, vol.45, 2007.

B. Bahmani, A. Chowdhury, and A. Goel, Fast incremental and personalized PageRank, VLDB, 2010.

K. Balog, F. Radlinski, and S. Arakelyan, Transparent, Scrutable and Explainable User Models for Personalized Recommendation, SIGIR, 2019.

D. Cer, Y. Yang, S. Kong, N. Hua, N. Limtiaco et al., Chris Tar, Brian Strope, and Ray Kurzweil, EMNLP, 2018.

C. Chen, M. Zhang, Y. Liu, and S. Ma, Neural attentional rating regression with review-level explanations, 2018.

D. Chen, S. P. Fraiberger, R. Moakler, and F. Provost, Enhancing transparency and control when drawing data-driven inferences about individuals, Big data, vol.5, p.3, 2017.

X. Chen, H. Chen, H. Xu, and Y. Zhang, Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network: Towards Visually Explainable Recommendation, SIGIR, 2019.

X. Chen, Z. Qin, Y. Zhang, and T. Xu, Learning to Rank Features for Recommendation over Multiple Categories, SIGIR, 2016.

X. Chen, H. Xu, Y. Zhang, and J. Tang, Sequential recommendation with user memory networks, WSDM, 2018.

F. Christoffel, B. Paudel, C. Newell, and A. Bernstein, Blockbusters and Wallflowers: Accurate, Diverse, and Scalable Recommendations with Random Walks, RecSys, 2015.

C. Cooper and S. Lee, Random walks in recommender systems: Exact computation and simulations, Tomasz Radzik, and Yiannis Siantos, 2014.

. Balázs-csanád-csáji, M. Raphaël, V. Jungers, and . Blondel, PageRank optimization by edge selection, Discrete Applied Mathematics, vol.169, 2014.

C. Desrosiers and G. Karypis, A Comprehensive Survey of Neighborhood-based Recommendation Methods, Recommender Systems Handbook, 2011.

C. Eksombatchai, P. Jindal, J. Z. Liu, Y. Liu, R. Sharma et al., Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time, WWW, 2018.

A. Ghazimatin, R. Saha-roy, and G. Weikum, FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social Feeds, WSDM, 2019.

H. Taher and . Haveliwala, Topic-sensitive Pagerank: A context-sensitive ranking algorithm for Web search, TKDE, vol.15, p.4, 2003.

J. L. Herlocker, J. A. Konstan, and J. Riedl, Explaining Collaborative Filtering Recommendations, CSCW, 2000.

M. Jamali and M. Ester, TrustWalker: A random walk model for combining trust-based and item-based recommendation, KDD, 2009.

G. Jeh and J. Widom, Scaling personalized Web search, 2003.

Z. Jiang, H. Liu, B. Fu, Z. Wu, and T. Zhang, Recommendation in heterogeneous information networks based on generalized random walk model and Bayesian personalized ranking, WSDM, 2018.

J. Kang, M. Wang, N. Cao, Y. Xia, W. Fan et al., AURORA: Auditing PageRank on Large Graphs, Big Data, 2018.

Y. Koren, R. Bell, and C. Volinsky, Matrix factorization techniques for recommender systems, Computer, vol.8, 2009.

P. Kouki, J. Schaffer, J. Pujara, O. John, L. Donovan et al., Personalized explanations for hybrid recommender systems, IUI, 2019.

J. Kunkel, T. Donkers, L. Michael, C. Barbu, and J. Ziegler, Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems, CHI, 2019.

P. Li, Z. Wang, Z. Ren, L. Bing, and W. Lam, Neural rating regression with abstractive tips generation for recommendation, SIGIR, 2017.

Y. Lu, R. Dong, and B. Smyth, Why I like it: Multi-task learning for recommendation and explanation, RecSys, 2018.

W. Ma, M. Zhang, Y. Cao, W. Jin, C. Wang et al., Jointly Learning Explainable Rules for Recommendation with Knowledge Graph, WWW, 2019.

A. Machanavajjhala, A. Korolova, and A. Sarma, Personalized social recommendations: Accurate or private, VLDB, 2011.

D. Martens and F. Provost, Explaining Data-Driven Document Classifications, MIS Quarterly, vol.38, p.1, 2014.

T. Miller, R. Weber, D. Aha, and D. Magazzeni, IJCAI 2019 Workshop on Explainable AI (XAI), 2019.

J. Moeyersoms, F. Brian-d'alessandro, D. Provost, and . Martens, Explaining classification models built on high-dimensional sparse data, 2016.

N. Athanasios, G. Nikolakopoulos, and . Karypis, Recwalk: Nearly uncoupled random walks for top-n recommendation, WSDM, 2019.

L. Page, S. Brin, R. Motwani, and T. Winograd, The PageRank citation ranking: Bringing order to the Web, 1999.

G. Peake and J. Wang, Explanation mining: Post hoc interpretability of latent factor models for recommendation systems, In KDD, 2018.

S. Marco-tulio-ribeiro, C. Singh, and . Guestrin, Why should I trust you?: Explaining the predictions of any classifier, KDD, 2016.

J. Rimchala, J. Doshi, Q. Zhu, D. Chang, N. Hoh et al., Shir Meir Lador, and Sambarta Dasgupta. 2019. KDD Workshop on Explainable AI for Fairness, Accountability, and Transparency

S. Seo, J. Huang, H. Yang, and Y. Liu, Interpretable convolutional neural networks with dual local and global attention for review rating prediction, RecSys, 2017.

C. Shi, Y. Li, J. Zhang, Y. Sun, and S. Philip, A survey of heterogeneous information network analysis, TKDE, vol.29, p.1, 2016.

C. Shi, Y. Li, J. Zhang, Y. Sun, and P. S. Yu, A Survey of Heterogeneous Information Network Analysis, TKDE, vol.29, p.1, 2017.

C. Shi, Z. Zhang, P. Luo, S. Philip, Y. Yu et al., Semantic path based personalized recommendation on weighted heterogeneous information networks, CIKM, 2015.

N. Tintarev and J. Masthoff, A survey of explanations in recommender systems, Workshop on Ambient Intelligence, Media and Sensing, 2007.

M. Wan and J. Mcauley, Item recommendation on monotonic behavior chains, 2018.

H. Wang, F. Zhang, J. Wang, M. Zhao, W. Li et al., Ripplenet: Propagating user preferences on the knowledge graph for recommender systems, CIKM, 2018.

N. Wang, H. Wang, Y. Jia, and Y. Yin, Explainable recommendation via multi-task learning in opinionated text data, SIGIR, 2018.

X. Wang, Y. Chen, J. Yang, L. Wu, Z. Wu et al., A Reinforcement Learning Framework for Explainable Recommendation, ICDM, 2018.

X. Wang, D. Wang, and C. Xu, Explainable reasoning over knowledge graphs for recommendation, AAAI, 2019.

Y. Xian, Z. Fu, S. Muthukrishnan, G. De-melo, and Y. Zhang, Reinforcement Knowledge Graph Reasoning for Explainable Recommendation, SIGIR, 2019.

F. Yang, N. Liu, S. Wang, and X. Hu, Towards Interpretation of Recommender Systems with Sorted Explanation Paths, ICDM, 2018.

X. Yu, X. Ren, Y. Sun, Q. Gu, B. Sturt et al., Personalized entity recommendation: A heterogeneous information network approach, WSDM, 2014.

X. Yu, X. Ren, Y. Sun, B. Sturt, U. Khandelwal et al., Recommendation in heterogeneous information networks with implicit user feedback, RecSys, 2013.

C. Zhang, A. Swami, and N. Chawla, SHNE: Representation Learning for Semantic-Associated Heterogeneous Networks, WSDM, 2019.

H. Zhang, P. Lofgren, and A. Goel, Approximate personalized pagerank on dynamic graphs, KDD, 2016.

Q. Zhang, L. Fan, B. Zhou, S. Todorovic, T. Wu et al., CVPR-19 Workshop on Explainable AI, 2019.

Y. Zhang and X. Chen, Explainable recommendation: A survey and new perspectives, 2018.

Y. Zhang, G. Lai, M. Zhang, Y. Zhang, Y. Liu et al., Explicit factor models for explainable recommendation based on phrase-level sentiment analysis, SIGIR, 2014.

Y. Zhang, Y. Zhang, M. Zhang, and C. Shah, The 2nd International Workshop on ExplainAble Recommendation and Search, vol.2019, 2019.