Clever Geek Handbook

Empfehlungen mit BegrĂŒndung (2020). Teil eins

Hallo liebes Publikum! Ich mache Sie auf den ersten Teil der Übersetzung eines großen Übersichtsartikels zum Thema Empfehlungssysteme aufmerksam, nĂ€mlich auf einen seiner Bereiche, Empfehlungen mit BegrĂŒndung.





Der Artikel untersucht das Problem der Rechtfertigung in Empfehlungssystemen unter verschiedenen Gesichtspunkten, analysiert offene Probleme und Probleme in diesem Bereich und geht auf das Thema Rechtfertigung in Deep Learning und KI im Allgemeinen ein.





Der Artikel kann fĂŒr alle von Interesse sein, die ein vollstĂ€ndiges und detailliertes VerstĂ€ndnis der Geschichte der Entwicklung von Empfehlungssystemen, der darin verwendeten Methoden, Methoden zur Bewertung von Modellen mit BegrĂŒndung und Beispiele fĂŒr die Verwendung von Empfehlungen mit BegrĂŒndung erhalten möchten in Anwendungen.





Um die Wahrnehmung des Textes zu erleichtern, werden stabile Phrasen und Klischees ins Russische ĂŒbersetzt. In FĂ€llen, in denen der englischsprachige Begriff beliebt ist, bedeutet er den Namen eines Ansatzes oder Bereichs und kann beim Auffinden von Informationen helfen oder ĂŒbersetzt werden mehrdeutig wird es unmittelbar nach der ĂŒbersetzten Phrase in Klammern angegeben ...





Inhaltsverzeichnis

  1. Anmerkung





  2. EinfĂŒhrung





    1. Empfehlungen mit BegrĂŒndung





    2. Historischer Bezug





    3. Methodenklassifizierung





    4. ErklÀrbar und effektiv





    5. ErklÀrbarkeit und Interpretierbarkeit





    6. Wie man diese Bewertung liest





  3. Referenzliste





:

. (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 .





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





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  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.





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





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





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  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.





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