Monday, August 1, 2016

Explanation of recommender system: a literature review


The possible research directions…  
  1. Model Effectiveness (Effective)
    1. Trust ability of the system. (Trust)
    2. Personalized result explanation (Survey & Framework)
    3. Transparent issues (Transparency)*
    4. User satisfaction(Perception)
  2. Legal and social issue
    1. Privacy
    2. Accountability of the recommendation result (Decision Support & Issues)*
    3. Discrimination (Diversity)
  3. Educational Purpose
    1. Learning the advance techniques behind recommendation.
    2. A stepwise learning model for tuning the system (Debug).
    3. Training for using the recommender system (Comprehensive).

Comprehensive

  1. Al-Taie, Mohammed Z, and Seifedine Kadry. “Visualization of Explanations in Recommender Systems.” Journal of Advanced Management Science Vol 2, no. 2 (2014).
  2. Barbieri, Nicola, Francesco Bonchi, and Giuseppe Manco. “Who to Follow and Why: Link Prediction with Explanations.” In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1266–1275. ACM, 2014.
  3. Blanco, Roi, Diego Ceccarelli, Claudio Lucchese, Raffaele Perego, and Fabrizio Silvestri. “You Should Read This! Let Me Explain You Why: Explaining News Recommendations to Users.” In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 1995–1999. ACM, 2012.
  4. Cleger-Tamayo, Sergio, Juan M Fernandez-Luna, and Juan F Huete. “Explaining Neighborhood-Based Recommendations.” In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1063–1064. ACM, 2012.
  5. Françoise, Jules, Frédéric Bevilacqua, and Thecla Schiphorst. “GaussBox: Prototyping Movement Interaction with Interactive Visualizations of Machine Learning.” In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, 3667–3670. ACM, 2016.
  6. Freitas, Alex A. “Comprehensible Classification Models: A Position Paper.” ACM SIGKDD Explorations Newsletter 15, no. 1 (2014): 1–10.
  7. Hernando, Antonio, JesúS Bobadilla, Fernando Ortega, and Abraham GutiéRrez. “Trees for Explaining Recommendations Made through Collaborative Filtering.” Information Sciences 239 (2013): 1–17.
  8. Kahng, Minsuk, Dezhi Fang, and Duen Horng. “Visual Exploration of Machine Learning Results Using Data Cube Analysis.” In HILDA@ SIGMOD, 1, 2016.
  9. Krause, Josua, Adam Perer, and Kenney Ng. “Interacting with Predictions: Visual Inspection of Black-Box Machine Learning Models.” In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 5686–5697. ACM, 2016.
  10. “Understanding LSTM Networks,” n.d. http://colah.github.io/posts/2015-08-Understanding-LSTMs/.
  11. Yamaguchi, Yuto, Mitsuo Yoshida, Christos Faloutsos, and Hiroyuki Kitagawa. “Why Do You Follow Him?: Multilinear Analysis on Twitter.” In Proceedings of the 24th International Conference on World Wide Web, 137–138. ACM, 2015.

Debug

  1. Kulesza, Todd, Margaret Burnett, Weng-Keen Wong, and Simone Stumpf. “Principles of Explanatory Debugging to Personalize Interactive Machine Learning.” In Proceedings of the 20th International Conference on Intelligent User Interfaces, 126–137. ACM, 2015.
  2. McGregor, Sean, Hailey Buckingham, Thomas G Dietterich, Rachel Houtman, Claire Montgomery, and Ronald Metoyer. “Facilitating Testing and Debugging of Markov Decision Processes with Interactive Visualization.” In Visual Languages and Human-Centric Computing (VL/HCC), 2015 IEEE Symposium on, 53–61. IEEE, 2015.

Decision Support

  1. Ehrlich, Kate, Susanna E Kirk, John Patterson, Jamie C Rasmussen, Steven I Ross, and Daniel M Gruen. “Taking Advice from Intelligent Systems: The Double-Edged Sword of Explanations.” In Proceedings of the 16th International Conference on Intelligent User Interfaces, 125–134. ACM, 2011.
  2. Jameson, Anthony, Silvia Gabrielli, Per Ola Kristensson, Katharina Reinecke, Federica Cena, Cristina Gena, and Fabiana Vernero. “How Can We Support Users’ Preferential Choice?” In CHI’11 Extended Abstracts on Human Factors in Computing Systems, 409–418. ACM, 2011.
  3. Martens, David, and Foster Provost. “Explaining Data-Driven Document Classifications,” 2013.
  4. McSherry, David. “Explaining the Pros and Cons of Conclusions in CBR.” In European Conference on Case-Based Reasoning, 317–330. Springer, 2004.
  5. Tan, Wee-Kek, Chuan-Hoo Tan, and Hock-Hai Teo. “Consumer-Based Decision Aid That Explains Which to Buy: Decision Confirmation or Overconfidence Bias?” Decision Support Systems 53, no. 1 (2012): 127–141.

Diversity

  1. Graells-Garrido, Eduardo, Mounia Lalmas, and Ricardo Baeza-Yates. “Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles.” In Proceedings of the 21st International Conference on Intelligent User Interfaces, 228–240. ACM, 2016.
  2. Szpektor, Idan, Yoelle Maarek, and Dan Pelleg. “When Relevance Is Not Enough: Promoting Diversity and Freshness in Personalized Question Recommendation.” In Proceedings of the 22nd International Conference on World Wide Web, 1249–1260. ACM, 2013.
  3. Yu, Cong, Sihem Amer-Yahia, and Laks Lakshmanan. Diversifying Recommendation Results through Explanation. Google Patents, 2013.
  4. Yu, Cong, Laks VS Lakshmanan, and Sihem Amer-Yahia. “Recommendation Diversification Using Explanations.” In 2009 IEEE 25th International Conference on Data Engineering, 1299–1302. IEEE, 2009.

Effective

  1. Komiak, Sherrie YX, and Izak Benbasat. “The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents.” MIS Quarterly, 2006, 941–960.
  2. Nanou, Theodora, George Lekakos, and Konstantinos Fouskas. “The Effects of Recommendations’ Presentation on Persuasion and Satisfaction in a Movie Recommender System.” Multimedia Systems 16, no. 4–5 (2010): 219–230.
  3. Tan, Wee-Kek, Chuan-Hoo Tan, and Hock-Hai Teo. “When Two Is Better Than One–Product Recommendation with Dual Information Processing Strategies.” In International Conference on HCI in Business, 775–786. Springer, 2014.
  4. Tintarev, Nava, and Judith Masthoff. “Effective Explanations of Recommendations: User-Centered Design.” In Proceedings of the 2007 ACM Conference on Recommender Systems, 153–156. ACM, 2007.
  5. ———. “The Effectiveness of Personalized Movie Explanations: An Experiment Using Commercial Meta-Data.” In International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, 204–213. Springer, 2008.

Framework

  1. Ben-Elazar, Shay, and Noam Koenigstein. “A Hybrid Explanations Framework for Collaborative Filtering Recommender Systems.” In RecSys Posters. Citeseer, 2014.
  2. Berner, Christopher Eric Shogo, Jeremy Ryan Schiff, Corey Layne Reese, and Paul Kenneth Twohey. Recommendation Engine That Processes Data Including User Data to Provide Recommendations and Explanations for the Recommendations to a User. Google Patents, 2013.
  3. Charissiadis, Andreas, and Nikos Karacapilidis. “Strengthening the Rationale of Recommendations Through a Hybrid Explanations Building Framework.” In Intelligent Decision Technologies, 311–323. Springer, 2015.
  4. Chen, Wei, Wynne Hsu, and Mong Li Lee. “Tagcloud-Based Explanation with Feedback for Recommender Systems.” In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, 945–948. ACM, 2013.
  5. Chen, Yu-Chih, Yu-Shi Lin, Yu-Chun Shen, and Shou-De Lin. “A Modified Random Walk Framework for Handling Negative Ratings and Generating Explanations.” ACM Transactions on Intelligent Systems and Technology (TIST) 4, no. 1 (2013): 12.
  6. Du, Zhao, Lantao Hu, Xiaolong Fu, and Yongqi Liu. “Scalable and Explainable Friend Recommendation in Campus Social Network System.” In Frontier and Future Development of Information Technology in Medicine and Education, 457–466. Springer, 2014.
  7. El Aouad, Sara, Christophe Dupuy, Renata Teixeira, Christophe Diot, and Francis Bach. “Exploiting Crowd Sourced Reviews to Explain Movie Recommendation.” In 2nd Workshop on Recommendation Systems for ℡EVISION and ONLINE VIDEO, 2015.
  8. Jameson, Anthony, Martijn C Willemsen, Alexander Felfernig, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro, and Li Chen. “Human Decision Making and Recommender Systems.” In Recommender Systems Handbook, 611–648. Springer, 2015.
  9. Lamche, Béatrice, Ugur Adıgüzel, and Wolfgang Wörndl. “Interactive Explanations in Mobile Shopping Recommender Systems.” In Proc. Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS 2014), ACM Conference on Recommender Systems, Foster City, USA, 2014.
  10. Lawlor, Aonghus, Khalil Muhammad, Rachael Rafter, and Barry Smyth. “Opinionated Explanations for Recommendation Systems.” In Research and Development in Intelligent Systems XXXII, 331–344. Springer, 2015.
  11. Muhammad, Khalil. “Opinionated Explanations of Recommendations from Product Reviews,” 2015.
  12. Nagulendra, Sayooran, and Julita Vassileva. “Providing Awareness, Explanation and Control of Personalized Filtering in a Social Networking Site.” Information Systems Frontiers 18, no. 1 (2016): 145–158.
  13. Schaffer, James, Prasanna Giridhar, Debra Jones, Tobias Höllerer, Tarek Abdelzaher, and John O’Donovan. “Getting the Message?: A Study of Explanation Interfaces for Microblog Data Analysis.” In Proceedings of the 20th International Conference on Intelligent User Interfaces, 345–356. ACM, 2015.
  14. Tintarev, Nava. “Explanations of Recommendations.” In Proceedings of the 2007 ACM Conference on Recommender Systems, 203–206. ACM, 2007.
  15. Tintarev, Nava, and Judith Masthoff. “Explaining Recommendations: Design and Evaluation.” In Recommender Systems Handbook, 353–382. Springer, 2015.
  16. Vig, Jesse, Shilad Sen, and John Riedl. “Tagsplanations: Explaining Recommendations Using Tags.” In Proceedings of the 14th International Conference on Intelligent User Interfaces, 47–56. ACM, 2009.
  17. Zanker, Markus, and Daniel Ninaus. “Knowledgeable Explanations for Recommender Systems.” In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on, 1:657–660. IEEE, 2010.

Issues

  1. Bunt, Andrea, Matthew Lount, and Catherine Lauzon. “Are Explanations Always Important?: A Study of Deployed, Low-Cost Intelligent Interactive Systems.” In Proceedings of the 2012 ACM International Conference on Intelligent User Interfaces, 169–178. ACM, 2012.
  2. BURKE, BRIAN, and KEVIN QUEALY. “How Coaches and the NYT 4th Down Bot Compare.” New York Times, 2013. http://www.nytimes.com/newsgraphics/2013/11/28/fourth-downs/post.html.
  3. Diakopoulos, Nicholas. “Accountability in Algorithmic Decision-Making.” Queue 13, no. 9 (2015): 50.
  4. ———. “Algorithmic Accountability: Journalistic Investigation of Computational Power Structures.” Digital Journalism 3, no. 3 (2015): 398–415.
  5. Lokot, Tetyana, and Nicholas Diakopoulos. “News Bots: Automating News and Information Dissemination on Twitter.” Digital Journalism, 2015, 1–18.

Perception

  1. Gkika, Sofia, and George Lekakos. “The Persuasive Role of Explanations in Recommender Systems.” In 2nd Intl. Workshop on Behavior Change Support Systems (BCSS 2014), 1153:59–68, 2014.
  2. Hijikata, Yoshinori, Yuki Kai, and Shogo Nishida. “The Relation between User Intervention and User Satisfaction for Information Recommendation.” In Proceedings of the 27th Annual ACM Symposium on Applied Computing, 2002–2007. ACM, 2012.
  3. Kulesza, Todd, Simone Stumpf, Margaret Burnett, and Irwin Kwan. “Tell Me More?: The Effects of Mental Model Soundness on Personalizing an Intelligent Agent.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1–10. ACM, 2012.
  4. Kulesza, Todd, Simone Stumpf, Margaret Burnett, Sherry Yang, Irwin Kwan, and Weng-Keen Wong. “Too Much, Too Little, or Just Right? Ways Explanations Impact End Users’ Mental Models.” In 2013 IEEE Symposium on Visual Languages and Human Centric Computing, 3–10. IEEE, 2013.
  5. Valdez, André Calero, Simon Bruns, Christoph Greven, Ulrik Schroeder, and Martina Ziefle. “What Do My Colleagues Know? Dealing with Cognitive Complexity in Organizations Through Visualizations.” In International Conference on Learning and Collaboration Technologies, 449–459. Springer, 2015.
  6. Zanker, Markus. “The Influence of Knowledgeable Explanations on Users’ Perception of a Recommender System.” In Proceedings of the Sixth ACM Conference on Recommender Systems, 269–272. ACM, 2012.

Survey

  1. Al-Taie, MOHAMMED Z. “Explanations in Recommender Systems: Overview and Research Approaches.” In Proceedings of the 14th International Arab Conference on Information Technology, Khartoum, Sudan, ACIT, Vol. 13, 2013.
  2. Buder, Jürgen, and Christina Schwind. “Learning with Personalized Recommender Systems: A Psychological View.” Computers in Human Behavior 28, no. 1 (2012): 207–216.
  3. Cleger, Sergio, Juan M Fernández-Luna, and Juan F Huete. “Learning from Explanations in Recommender Systems.” Information Sciences 287 (2014): 90–108.
  4. Gedikli, Fatih, Dietmar Jannach, and Mouzhi Ge. “How Should I Explain? A Comparison of Different Explanation Types for Recommender Systems.” International Journal of Human-Computer Studies 72, no. 4 (2014): 367–382.
  5. Papadimitriou, Alexis, Panagiotis Symeonidis, and Yannis Manolopoulos. “A Generalized Taxonomy of Explanations Styles for Traditional and Social Recommender Systems.” Data Mining and Knowledge Discovery 24, no. 3 (2012): 555–583.
  6. Scheel, Christian, Angel Castellanos, Thebin Lee, and Ernesto William De Luca. “The Reason Why: A Survey of Explanations for Recommender Systems.” In International Workshop on Adaptive Multimedia Retrieval, 67–84. Springer, 2012.
  7. Tintarev, Nava, and Judith Masthoff. “A Survey of Explanations in Recommender Systems.” In Data Engineering Workshop, 2007 IEEE 23rd International Conference on, 801–810. IEEE, 2007.

Transparency

  1. El-Arini, Khalid, Ulrich Paquet, Ralf Herbrich, Jurgen Van Gael, and Blaise Agüera y Arcas. “Transparent User Models for Personalization.” In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 678–686. ACM, 2012.
  2. Hebrado, Januel L, Hong Joo Lee, and Jaewon Choi. “Influences of Transparency and Feedback on Customer Intention to Reuse Online Recommender Systems.” Journal of Society for E-Business Studies 18, no. 2 (2013).
  3. Kizilcec, René F. “How Much Information?: Effects of Transparency on Trust in an Algorithmic Interface.” In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 2390–2395. ACM, 2016.
  4. Radmacher, Mike. “Design Criteria for Transparent Mobile Event Recommendations.” AMCIS 2008 Proceedings, 2008, 304.
  5. Sinha, Rashmi, and Kirsten Swearingen. “The Role of Transparency in Recommender Systems.” In CHI’02 Extended Abstracts on Human Factors in Computing Systems, 830–831. ACM, 2002.

Trust

  1. Biran, Or, and Kathleen McKeown. “Generating Justifications of Machine Learning Predictions.” In 1st International Workshop on Data-to-Text Generation, Edinburgh, 2015.
  2. Cleger-Tamayo, Sergio, Juan M Fernández-Luna, Juan F Huete, and Nava Tintarev. “Being Confident about the Quality of the Predictions in Recommender Systems.” In European Conference on Information Retrieval, 411–422. Springer, 2013.
  3. Kang, Byungkyu, Tobias Höllerer, and John O’Donovan. “Believe It or Not? Analyzing Information Credibility in Microblogs.” In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, 611–616. ACM, 2015.
  4. Katarya, Rahul, Ivy Jain, and Hitesh Hasija. “An Interactive Interface for Instilling Trust and Providing Diverse Recommendations.” In Computer and Communication Technology (ICCCT), 2014 International Conference on, 17–22. IEEE, 2014.
  5. Muhammad, Khalil, Aonghus Lawlor, and Barry Smyth. “On the Use of Opinionated Explanations to Rank and Justify Recommendations.” In The Twenty-Ninth International Flairs Conference, 2016.
  6. O’Donovan, John, and Barry Smyth. “Trust in Recommender Systems.” In Proceedings of the 10th International Conference on Intelligent User Interfaces, 167–174. ACM, 2005.
  7. Shani, Guy, Lior Rokach, Bracha Shapira, Sarit Hadash, and Moran Tangi. “Investigating Confidence Displays for Top-N Recommendations.” Journal of the American Society for Information Science and Technology 64, no. 12 (2013): 2548–2563.
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