Clever Geek Handbook

Recommendations with rationale (2020). Part one

Hello dear audience! I bring to your attention the first part of the translation of a large review article on the topic of recommender systems, namely, one of its areas, recommendations with justification.





The article examines the problem of justification in recommender systems from several points of view, analyzes open problems and problems in this area, and touches on the topic of justification in deep learning and AI in general.





The article may be of interest to everyone who wants to get a complete and detailed understanding of the history of the development of recommendation systems, the methods that are used in them, methods for evaluating models with justification and look at examples of using recommendations with justification in applications.





To facilitate the perception of the text, stable phrases and clichés are translated into Russian, but in cases where the English-language term is popular, it means the name of an approach or area and can help in finding information, or it can be translated ambiguously, it is given in brackets immediately after the translated phrase ...





Table of contents

  1. annotation





  2. Introduction





    1. Recommendations with justification





    2. Historical reference





    3. Method classification





    4. Explainable and Effective





    5. Explainability and interpretability





    6. How to read this review





  3. List of references





:

. (explainable recommendation) , , . , ( , ). «» , . , , , , . – , .





. «5W»: , , , (what, when, who, where, why). :





  1. , , .





  2. : – , , – .





  3. , , , (point-of-interest or POI recommendation).





(information retrieval or IR), () (). .





1.

1.1. 

– , «», .. , , , . , , , , , , .





, , . , «5W», .. , , , , (what, when, who, where, why), , , , , , , .





(intristic models) (model-agnostic) (Lipton, 2018 [1]; Molnar, 2019 [2]). , , , (Zhang et al., 2014a [3]). (Wang et al., 2018d [4]), « » (Peake and Wang, 2018 [5]), , . – , , , , . (Lipton, 2018 [1]; Miller, 2019 [6])





, (information retrieval) (data mining), , .. [ ] . - , .





, . , .





1.2. 

. , « » (Zhang et al., 2014a [3]), , , . , Schafer et al. (1999) [7] , , , «, , , ». () (Collaborative filtering or CF), (item-based collaborative filtering or item-based F); Herlocker et al. (2000) [8] , , MovieLens, , Sinha and Swearingen (2002) [9] . , , , , « » (Tintarev and Masthoff, 2007a [10]).





, , .





(content-based) (Ricci et al., 2011 [11]). , , , , , , (Balabanovic and Shoham, 1997 [12]; Pazzani and Billsus, 2007 [13]). .. , , , , , . , , . Ferwerda et al. (2012) [14] , .





, . , , , , . . « » (Ekstrand et al., 2011 [15]). , (user-based CF) - , GroupLens (Resnick et al., 1994 [16]). , , , , . Sarwar et al. (2001) [17] , (item-based CF), Linden et al. (2003) [18] . , , , , .





, , , , , , . , , , , « », , , « , ». , , , , . (Herlocker and Konstan, 2000 [19]; Herlocker et al., 2000 [8]; Sinha and Swearingen, 2002 [9]).





2000 ., Koren (2008) [20] (Latent Factor Models or LFM). (Matrix Factorization or MF) (Koren et al., 2009 [21]). , , . , , , « » , , . , .. , .





, , (Explainable Recommendation Systems), .. , , . , Zhang et al. (2014a) [3] (Explicit Factor Model or EFM) .  , . , (deep learning or DL) . , (Dacrema et al., 2019 [22]) , . , , . .





, (explainability) 1980 «», , , (knowledge-based systems), , , . (Clancey, 1982 [23]) , , . , (Explainable AI or XAI) (Gunning, 2017) [24]. , , . , , (IR/RecSys) . , (Explainable Machine Learning).





1.3. 

, .





, :





  1. , (., ), (Human‑Computer Interaction or HCI) .





  2. , . (nearest-neighbor), , (topic modelling), (graph-models), , (knowledge reasoning), (association rule mining) .





, . , « ( )» « , ( )», . , , .. . . , , . . , .





1.1. . , (Zhang et al., 2014a [3]) , . , « ». (Seo et al., 2017 [25]), , . , « /». (Chen et al., 2019b [26]), -, « ». , .





.. , 1.1. . «-» . , 2 3 .





1.1 ()





















Herlocker et al., 2000 [8]

Abdollahi and Nasraoui, 2017 [32]

-

Heckel et al., 2017 [37]





Vig et al., 2009 [30]

Zhang et al., 2014a [3]

McAuley and Leskovec, 2013 [34]

He et al., 2015 [38]

,





-

Zhang et al., 2014a [3]

-

-





-

-

-

-





Sharma and Cosley, 2013 [31]

-

Ren et al., 2017 [35]

Park et al., 2018 [39]





-

Zhang, 2015 [33]

Wu and Ester 2015 [36]

-

 





1.1 ()





,

















Chen et al., 2018c [40]

Catherine et al., 2017 [42]

Peake and Wang 2018 [5]

Cheng et al., 2019a [47]





Seo et al., 2017 [25]

Huang et al., 2018 [43]

Davidson et al., 2010 [45]

McInerney et al., 2018 [48]

,

Li et al., 2017 [41]

Ai et al., 2018 [44]

Balog et al., 2019 [46]

Wang et al., 2018d [4]





Chen et al., 2019b [26]

-

-

-





-

-

-

-





-

-

-

-

1.4. 

(explainability) (effectiveness) , (Ricci et al., 2011 [11]). , , . , (Bilgic et al., 2004 [27]; Zhang et al., 2014a [3]). , – (deep representation learning) – , , . (explainable deep models) , , (explainable machine learning).





  .





1.5. 

(explainability) (interpretability) . , – . , , . , , . , ( ) , , , . , , , . , , , , (neural attention mechanisms), , , (IR), (NLP), (computer vision), (graph analysis) . .





1.6. 

, , . , , , (Pazzani and Billsus, 2007 [13]), (Ekstrand et al., 2011 [15]) (Shani and Gunawardana, 2011 [28]). , , (Tintarev and Masthoff, 2007a [10]) (Lipton, 2018 [1]; Molnar, 2019 [2]), (Gunning, 2017 [24]; Samek et al., 2017 [29]).





. 2 . , , , . 3 . 4 , 5 . 6 .





  1. Lipton, Z. C. (2018). “The mythos of model interpretability”. Communications of the ACM. 61(10): 36–43.





  2. Molnar, C. (2019). Interpretable Machine Learning. Leanpub.





  3. Zhang, Y., G. Lai, M. Zhang, Y. Zhang, Y. Liu, and S. Ma (2014a). “Explicit factor models for explainable recommendation based on phrase-level sentiment analysis”. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM. 83–92.





  4. Wang, X., Y. Chen, J. Yang, L. Wu, Z. Wu, and X. Xie (2018d). “A reinforcement learning framework for explainable recommendation”. In: 2018 IEEE International Conference on Data Mining (ICDM). IEEE. 587–596.





  5. Peake, G. and J. Wang (2018). “Explanation mining: Post hoc interpretability of latent factor models for recommendation systems”. In: Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 2005 International Conference on Intelligent User Interfaces, San Diego, CA, USA. ACM. 2060–2069.





  6. Miller, T. (2019). “Explanation in artificial intelligence: Insights from the social sciences”. Artificial Intelligence. 267: 1–38.





  7. Schafer, J. B., J. Konstan, and J. Riedl (1999). “Recommender systems in e-commerce”. In: Proceedings of the 1st ACM Conference on Electronic Commerce. ACM. 158–166.





  8. Herlocker, J. L., J. A. Konstan, and J. Riedl (2000). “Explaining collaborative filtering recommendations”. In: Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work. ACM. 241–250.





  9. Sinha, R. and K. Swearingen (2002). “The role of transparency in recommender systems”. In: CHI’02 Extended Abstracts on Human Factors in Computing Systems. ACM. 830–831.





  10. Tintarev, N. and J. Masthoff (2007a). “A survey of explanations in recommender systems”. In: Data Engineering Workshop, 2007 IEEE 23rd International Conference. IEEE. 801–810.





  11. Ricci, F., L. Rokach, and B. Shapira (2011). “Introduction to recommender systems handbook”. In: Recommender Systems Handbook. Springer. 1–35.





  12. Balabanovic, M. and Y. Shoham (1997). “Fab: Content-based, collaborative recommendation”. Communications of the ACM. 40(3): 66–72.





  13. Pazzani, M. J. and D. Billsus (2007). “Content-based recommendation systems”. In: The Adaptive Web. Springer. 325–341.





  14. Ferwerda, B., K. Swelsen, and E. Yang (2012). “Explaining contentbased recommendations”. New York. 1–24.





  15. Ekstrand, M. D. et al. (2011). “Collaborative filtering recommender systems”. Foundations and Trends¼ in Human–Computer Interaction. 4(2): 81–173.





  16. Resnick, P., N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl (1994). “GroupLens: An open architecture for collaborative filtering of netnews”. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work. ACM. 175–186.





  17. Sarwar, B., G. Karypis, J. Konstan, and J. Riedl (2001). “Item-based collaborative filtering recommendation algorithms”. In: Proceedings of the 10th International Conference on World Wide Web. ACM. 285–295.





  18. Linden, G., B. Smith, and J. York (2003). “Amazon.com recommendations: Item-to-item collaborative filtering”. IEEE Internet Computing. 7(1): 76–80.





  19. Herlocker, J. L. and J. A. Konstan (2000). Understanding and Improving Automated Collaborative Filtering Systems. University of Minnesota Minnesota.





  20. Koren, Y. (2008). “Factorization meets the neighborhood: A multifaceted collaborative filtering model”. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM. 426–434.





  21. Koren, Y., R. Bell, and C. Volinsky (2009). “Matrix factorization techniques for recommender systems”. Computer. 42(8): 42–49.





  22. Dacrema, M. F., P. Cremonesi, and D. Jannach (2019). “Are we really making much progress? A worrying analysis of recent neural recommendation approaches”. In: Proceedings of the 13th ACM Conference on Recommender Systems. ACM. 101–109.





  23. Clancey, W. J. (1982). “The epistemology of a rule-based expert system: A framework for explanation.” Tech. Rep. Department of Computer Science, Stanford University, CA.





  24. Gunning, D. (2017). “Explainable artificial intelligence (XAI)”. Defense Advanced Research Projects Agency (DARPA).





  25. Seo, S., J. Huang, H. Yang, and Y. Liu (2017). “Interpretable convolutional neural networks with dual local and global attention for review rating prediction”. In: Proceedings of the 11th ACM Conference on Recommender Systems. ACM. 297–305.





  26. Chen, X., H. Chen, H. Xu, Y. Zhang, Y. Cao, Z. Qin, and H. Zha (2019b). “Personalized fashion recommendation with visual explanations based on multimodal attention network: Towards visually explainable recommendation”. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM. 765–774.





  27. Bilgic, M., R. Mooney, and E. Rich (2004). “Explanation for recommender systems: Satisfaction vs. promotion”. Computer Sciences Austin, University of Texas. Undergraduate Honors. 27.





  28. Shani, G. and A. Gunawardana (2011). “Evaluating recommendation systems”. In: Recommender Systems Handbook. Springer. 257–297.





  29. Samek, W., T. Wiegand, and K.-R. MĂŒller (2017). “Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models”. arXiv preprint arXiv:1708.08296.





  30. Vig, J., S. Sen, and J. Riedl (2009). “Tagsplanations: Explaining recommendations using tags”. In: Proceedings of the 14th International Conference on Intelligent User Interfaces. ACM. 47–56.





  31. Sharma, A. and D. Cosley (2013). “Do social explanations work?: Studying and modeling the effects of social explanations in recommender systems”. In: Proceedings of the 22nd International Conference on World Wide Web. ACM. 1133–1144.





  32. Abdollahi, B. and O. Nasraoui (2017). “Using explainability for constrained matrix factorization”. In: Proceedings of the 11th ACM Conference on Recommender Systems. ACM. 79–83.





  33. Zhang, Y. (2015). “Incorporating phrase-level sentiment analysis on textual reviews for personalized recommendation”. In: Proceedings of the 8th ACM International Conference on Web Search and Data Mining. ACM. 435–440.





  34. McAuley, J. and J. Leskovec (2013). “Hidden factors and hidden topics: understanding rating dimensions with review text”. In: Proceedings of the 7th ACM Conference on Recommender Systems. ACM. 165–172.





  35. Ren, Z., S. Liang, P. Li, S. Wang, and M. de Rijke (2017). “Social collaborative viewpoint regression with explainable recommendations”. In: Proceedings of the 10th ACM International Conference on Web Search and Data Mining. ACM. 485–494.





  36. Wu, Y. and M. Ester (2015). “Flame: A probabilistic model combining aspect based opinion mining and collaborative filtering”. In: Proceedings of the 8th ACM International Conference on Web Search and Data Mining. ACM. 199–208.





  37. Heckel, R., M. Vlachos, T. Parnell, and C. Duenner (2017). “Scalable and interpretable product recommendations via overlapping coclustering”. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE). IEEE. 1033–1044.





  38. He, X., T. Chen, M.-Y. Kan, and X. Chen (2015). “Trirank: Review-aware explainable recommendation by modeling aspects”. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM. 1661–1670.





  39. Park, H., H. Jeon, J. Kim, B. Ahn, and U. Kang (2018). “UniWalk: Explainable and accurate recommendation for rating and network data”. arXiv preprint arXiv:1710.07134.





  40. Chen, X., H. Xu, Y. Zhang, Y. Cao, H. Zha, Z. Qin, and J. Tang (2018c). “Sequential recommendation with user memory networks”. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining. ACM.





  41. Li, P., Z. Wang, Z. Ren, L. Bing, and W. Lam (2017). “Neural rating regression with abstractive tips generation for recommendation”. In: Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM. 345–354.





  42. Catherine, R., K. Mazaitis, M. Eskenazi, and W. Cohen (2017). “Explainable entity-based recommendations with knowledge graphs”. In: Proceedings of the Poster Track of the 11th ACM Conference on Recommender Systems. ACM.





  43. Huang, J., W. X. Zhao, H. Dou, J.-R. Wen, and E. Y. Chang (2018). “Improving sequential recommendation with knowledge-enhanced memory networks”. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM. 505–514.





  44. Ai, Q., V. Azizi, X. Chen, and Y. Zhang (2018). “Learning heterogeneous knowledge base embeddings for explainable recommendation”. Algorithms. 11(9): 137.





  45. Davidson, J., B. Liebald, J. Liu, P. Nandy, T. Van Vleet, U. Gargi, S. Gupta, Y. He, M. Lambert, B. Livingston, et al. (2010). “The YouTube video recommendation system”. In: Proceedings of the 4th ACM conference on Recommender systems. ACM. 293–296.





  46. Balog, K., F. Radlinski, and S. Arakelyan (2019). “Transparent, scrutable and explainable user models for personalized recommendation”. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM.





  47. Cheng, W., Y. Shen, L. Huang, and Y. Zhu (2019a). “Incorporating interpretability into latent factor models via fast influence analysis”. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM. 885-893.





  48. McInerney, J., B. Lacker, S. Hansen, K. Higley, H. Bouchard, A. Gruson, and R. Mehrotra (2018). “Explore, exploit, and explain: Personalizing explainable recommendations with bandits”. In: Proceedings of the 12th ACM Conference on Recommender Systems. ACM. 31–39.







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