Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
An Introduction to Recommender Systems Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

13. Advanced Topics in Recommender Systems

verfasst von : Charu C. Aggarwal

Erschienen in: Recommender Systems

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Recommender systems are often used in a number of specialized settings that are not covered in previous chapters of this book. In many cases, the recommendations are performed in settings where there might be multiple users or multiple evaluation criteria. For example, consider a scenario where a group of tourists wish to take a vacation together. Therefore, they may want to obtain recommendations that match the overall interests of the various members in the group. In other scenarios, users may use multiple criteria to provide ratings to items. These variations in the problem formulation can sometimes make the prediction problem more challenging. In particular, we will study the following advanced variations of recommender systems in this chapter:

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Attack-Resistant Recommender Systems
Fußnoten
1
If the payoffs lie in the range [0, Δ], then the deviation also needs to be scaled up by Δ.
 
2
In traditional recommender systems, items are more likely to have descriptive profiles than users.
 
Literatur
[7]
G. Adomavicius and A. Tuzhilin. Context-aware recommender systems. Recommender Systems handbook, pp. 217–253, Springer, NY, 2011.
[11]
G. Adomavicius, N. Manouselis, and Y. Kwon. Multi-criteria recommender systems. Recommender Systems Handbook, Springer, pp. 769–803, 2011.
[12]
G. Adomavicius and Y. Kwon. New recommendation techniques for multicriteria rating systems. IEEE Intelligent Systems, 22(3), pp. 48–55, 2007.CrossRef
[15]
[18]
C. Aggarwal. Data classification: algorithms and applications. CRC Press, 2014.
[19]
C. Aggarwal. Data clustering: algorithms and applications. CRC Press, 2014.
[22]
C. Aggarwal. Data mining: the textbook. Springer, New York, 2015.
[27]
C. Aggarwal and P. Yu. On static and dynamic methods for condensation-based privacy-preserving data mining. ACM Transactions on Database Systems (TODS), 33(1), 2, 2008.
[28]
C. Aggarwal, J. Wolf, and P. Yu. A framework for the optimizing of WWW advertising. Trends in Distributed Systems for Electronic Commerce, pp. 1–10, 1998.
[29]
C. Aggarwal, S. Gates, and P. Yu. On using partial supervision for text categorization. IEEE Transactions on Knowledge and Data Engineering, 16(2), pp. 245–255, 2004.CrossRef
[30]
C. Aggarwal. On k-anonymity and the curse of dimensionality, Very Large Databases Conference, pp. 901–909, 2005.
[34]
C. Aggarwal and P. Yu. An automated system for Web portal personalization. Very Large Data Bases Conference, pp. 1031–1040, 2002.
[35]
D. Agrawal and C. Aggarwal. On the design and quantification of privacy-preserving data mining algorithms. ACM PODS Conference, pp. 247–255, 2001.
[38]
R. Agrawal, and R. Srikant. Privacy-preserving data mining. ACM SIGMOD Conference, pp. 439–450, 2000.
[52]
L. Ardissono, A. Goy, G. Petrone, M. Segnan, and P. Torasso. INTRIGUE: personalized recommendation of tourist attractions for desktop and hand-held devices. Applied Artificial Intelligence, 17(8), pp. 687–714, 2003.CrossRef
[57]
R. Baeza-Yates, C. Hurtado, and M. Mendoza. Query recommendation using query logs in search engines. EDBT 2004 Workshops on Current Trends in Database Technology, pp. 588–596, 2004.
[59]
S. Balakrishnan and S. Chopra. Collaborative ranking. Web Search and Data Mining Conference, pp. 143–152, 2012.
[75]
S. Berkovsky, Y. Eytani, T. Kuflik, and F. Ricci. Enhancing privacy and preserving accuracy of a distributed collaborative filtering. ACM Conference on Recommender Systems, pp. 9–16, 2007.
[90]
P. Boldi, F. Bonchi, C. Castillo, D. Donato, A. Gionis, and S. Vigna. The query-flow graph: model and applications. ACM Conference on Information and Knowledge Management, pp. 609–618, 2008.
[92]
B. Bouneffouf, A. Bouzeghoub, and A. Gancarski. A contextual-bandit algorithm for mobile context-aware recommender system. Neural Information Processing, pp. 324–331, 2012.
[103]
A. Brun, S. Castagnos, and A. Boyer. Social recommendations: mentor and leader detection to alleviate the cold-start problem in collaborative filtering. Social Network Mining, Analysis, and Research Trends: Techniques and Applications: Techniques and Applications, 270, 2011.
[105]
A. Broder, M. Fontoura, V. Josifovski, and L. Riedel. A semantic approach to contextual advertising. SIGIR Conference, pp. 559–566, 2007.
[106]
A. Broder. Computational advertising and recommender systems. ACM Conference on Recommender Systems, pp. 1–2, 2008.
[107]
[115]
C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. International Conference on Machine Learning, pp. 89–96, 2005.
[128]
Z. Cao, T. Qin, T. Liu, M. F. Tsai, and H. Li. Learning to rank: from pairwise approach to listwise approach. International Conference on Machine Learning, pp. 129–137, 2007.
[133]
J. Canny. Collaborative filtering with privacy via factor analysis. ACM SIGR Conference, pp. 238–245, 2002.
[134]
I. Cantador and P. Castells. Semantic contextualisation in a news recommender system. Workshop on Context-Aware Recommender Systems, 2009.
[136]
H. Cao, E. Chen, J. Yang, and H. Xiong. Enhancing recommender systems under volatile user interest drifts. ACM Conference on Information and Knowledge Management, pp. 1257–1266, 2009.
[137]
H. Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, and H. Li. Context-aware query suggestion by mining click-through and session data. ACM KDD Conference, pp. 875–883, 2008.
[142]
D. Chakrabarti, D. Agarwal, and V. Josifovski. Contextual advertising by combining relevance with click feedback. World Wide Web Conference, 2008.
[160]
W. Chu, L. Li, L. Reyzin, and R. Schapire. Contextual bandits with linear payoff functions. AISTATS Conference, pp. 208–214, 2011.
[168]
M. O’Connor, D. Cosley, J. Konstan, and J. Riedl. PolyLens: a recommender system for groups of users. European Conference on Computer Supported Cooperative Work, pp. 199–218, 2001.
[175]
A. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. World Wide Web Conference, pp. 271–280, 2007.
[189]
C. Dwork. Differential privacy. Encyclopedia of Cryptography and Security, Springer, pp. 338–340, 2011.
[190]
C. Dwork, R. Kumar, M. Naor, and D. Sivakumar. Rank aggregation methods for the web. World Wide Web Conference, pp. 613–622, 2010.
[192]
M. Elahi, V. Repsys, and F. Ricci. Rating elicitation strategies for collaborative filtering. E-Commerce and Web Technologies, pp. 160–171, 2011.
[194]
M. Elahi, M. Braunhofer, F. Ricci, and M. Tkalcic. Personality-based active learning for collaborative filtering recommender systems. Advances in Artificial Intelligence, pp. 360–371, 2013.
[244]
Q. He, D. Jiang, Z. Liao, S. Hoi, K. Chang, E. Lim, and H. Li. Web query recommendation via sequential query prediction. IEEE International Conference on Data Engineering, pp. 1443–1454, 2009.
[253]
W. Hong, S. Zheng, H. Wang, and J. Shi. A job recommender system based on user clustering. Journal of Computers, 8(8), 1960–1967, 2013.
[254]
J. Hopcroft, T. Lou, and J. Tang. Who will follow you back?: reciprocal relationship prediction. ACM International Conference on Information and Knowledge Management, pp. 1137–1146, 2011.
[257]
N. Houlsby, J. M. Hernandez-Lobato, and Z. Ghahramani. Cold-start active learning with robust ordinal matrix factorization. International Conference on Machine Learning (ICML), pp. 766–774, 2014.
[271]
A. Jameson and B. Smyth. Recommendation to groups. The Adaptive Web, pp. 596–627, 2007.
[272]
A. Jameson. More than the sum of its members: challenges for group recommender systems. Proceedings of the working conference on Advanced visual interfaces, pp. 48–54, 2004.
[276]
D. Jannach, Z. Karakaya, and F. Gedikli. Accuracy improvements for multi-criteria recommender systems. ACM Conference on Electronic Commerce, pp. 674–689, 2012.
[284]
T. Joachims. Optimizing search engines using click-through data. ACM KDD Conference, pp. 133–142, 2002.
[290]
S. Kale, L. Reyzin, and R. Schapire. Non-stochastic bandit slate problems. Advances in Neural Information Processing Systems, pp. 1054–1062, 2010.
[295]
R. Karimi, C. Freudenthaler, A. Nanopoulos, L. Schmidt-Thieme. Exploiting the characteristics of matrix factorization for active learning in recommender systems. ACM Conference on Recommender Systems, pp. 317–320, 2012.
[303]
J. Kleinberg, C. Papadimitriou, and P. Raghavan. On the value of private information. Proceedings of the 8th Conference on Theoretical Aspects of Rationality and Knowledge, pp. 249–257, 2001.
[314]
Y. Koren and J. Sill. Collaborative filtering on ordinal user feedback. IJCAI Conference, pp. 3022–3026, 2011.
[323]
[327]
A. Lacerda, M. Cristo, W. Fan, N. Ziviani, and B. Ribeiro-Neto. Learning to advertise. ACM SIGIR Conference, pp. 549–556, 2006.
[328]
K. Lakiotaki, S. Tsafarakis, and N. Matsatsinis. UTA-Rec: a recommender system based on multiple criteria analysis. ACM Conference on Recommender Systems, pp. 219–226, 2008.
[330]
B. Lamche, U. Trottmann, and W. Worndl. Active learning strategies for exploratory mobile recommender systems. Proceedings of the 4th Workshop on Context-Awareness in Retrieval and Recommendation, pp. 10–17, 2014.
[334]
N. Lathia, S. Hailes, and L. Capra. Private distributed collaborative filtering using estimated concordance measures. ACM Conference on Recommender Systems, pp. 1–8, 2007.
[340]
H. Lee and W. Teng. Incorporating multi-criteria ratings in recommendation systems. IEEE International Conference on Information Reuse and Integration (IRI), pp. 273–278, 2007.
[348]
L. Li, W. Chu, J. Langford, and R. Schapire. A contextual-bandit approach to personalized news article recommendation. World Wide Web Conference, pp. 661–670, 2010.
[349]
L. Li, W. Chu, J. Langford, and X. Wang. Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms. International Conference on Web Search and Data Mining, pp. 297–306, 2011.
[352]
N. Li, T. Li, and S. Venkatasubramanian. t-closeness: Privacy beyond k-anonymity and ℓ-diversity. IEEE International Conference on Data Enginering, pp. 106–115, 2007.
[353]
Q. Li, C. Wang, and G. Geng. Improving personalized services in mobile commerce by a novel multicriteria rating approach. World Wide Web Conference, pp. 1235–1236, 2008.
[367]
N. Liu and Q. Yang. Eigenrank: a ranking-oriented approach to collaborative filtering. ACM SIGIR Conference, pp. 83–90, 2008.
[368]
N. Liu, M. Zhao, and Q. Yang. Probabilistic latent preference analysis for collaborative filtering. ACM Conference on Information and Knowledge Management, pp. 759–766, 2009.
[370]
T. Y. Liu. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3(3), pp. 225–331, 2009.CrossRef
[372]
Z. Liu, Y.-X. Wang, and A. Smola. Fast differentially private matrix factorization. ACM Conference on Recommender Systems, 2015.
[386]
A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam. ℓ-diversity: privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(3), 2007.
[398]
N. Manouselis and C. Costopoulou. Analysis and classification of multi-criteria recommender systems. World Wide Web, 10(4), pp. 415–441, 2007.CrossRef
[399]
N. Manouselis and Costopoulou. Experimental Analysis of Design Choices in a Multi-Criteria Recommender System. International Journal of Pattern Recognition and AI, 21(2), pp. 311–332, 2007.
[407]
J. Masthoff. Group recommender systems: combining individual models. Recommender Systems Handbook, Springer, pp. 677–702, 2011.
[408]
J. Masthoff. Group modeling: Selecting a sequence of television items to suit a group of viewers. Personalized Digital Television, pp. 93–141, 2004.
[409]
J. Masthoff and A. Gatt. In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems. User Modeling and User-Adapted Interactio, 16(3–4), pp. 281–319, 2006.CrossRef
[410]
J. Masthoff. Modeling the multiple people that are me. International Conference on User Modeling, Also appears in Lecture Notes in Computer Science, Springer, Vol. 2702, pp. 258–262, 2003.MATH
[412]
J. McCarthy and T. Anagnost. MusicFX: An Arbiter of Group Preferences for Computer Supported Collaborative Workouts. ACM Conference on Computer Supported Cooperative Work, pp. 363–372, 1998.
[413]
K. McCarthy, L. McGinty, B. Smyth, and M. Salamo. The needs of the many: a case-based group recommender system. Advances in Case-Based Reasoning, pp. 196–210, 2004.
[415]
K. McCarthy, M. Salamo, L. McGinty, B. Smyth, and P. Nicon. Group recommender systems: a critiquing based approach. International Conference on Intelligent User Interfaces, pp. 267–269, 2006.
[429]
Q. Mei, D. Zhou, and K. Church. Query suggestion using hitting time. ACM Conference on Information and Knowledge Management, pp. 469–478, 2009..
[432]
A. K. Menon, and C. Elkan. Link prediction via matrix factorization. Machine Learning and Knowledge Discovery in Databases, pp. 437–452, 2011.
[451]
[480]
L. Pizzato, T. Rej, T. Chung, I. Koprinska, and J. Kay. RECON: a reciprocal recommender for online dating. ACM Conference on Recommender systems, pp. 207–214, 2010.
[481]
L. Pizzato, T. Rej, T. Chung, K. Yacef, I. Koprinska, and J. Kay. Reciprocal recommenders. Workshop on Intelligent Techniques for Web Personalization and Recommender Systems, pp. 20–24, 2010.
[482]
L. Pizzato, T. Rej, K. Yacef, I. Koprinska, and J. Kay. Finding someone you will like and who won’t reject you. User Modeling, Adaption and Personalization, Springer, pp. 269–280, 2011.
[484]
H. Polat and W. Du. Privacy-preserving collaborative filtering using randomized perturbation techniques. IEEE International Conference on Data Mining, pp. 625–628, 2003.
[485]
H. Polat and W. Du. SVD-based collaborative filtering with privacy. ACM symposium on Applied Computing, pp. 791–795, 2005.
[489]
L. Quijano-Sanchez, J. Recio-Garcia, B. Diaz-Agudo,and G. Jimenez-Diaz. Social factors in group recommender systems. ACM Transactions on Intelligent Systems and Technology (TIST), 4(1), 8, 2013.
[490]
C. Quoc and V. Le. Learning to rank with nonsmooth cost functions. Advances in Neural Information Processing Systems, 19, pp. 193–200, 2007.
[493]
S. Rendle. Factorization machines. IEEE International Conference on Data Mining, pp. 995–100, 2010.
[499]
S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. BPR: Bayesian personalized ranking from implicit feedback. Uncertainty in Artificial Intelligence (UAI), pp. 452–451, 2009.
[504]
F. Ricci. Mobile recommender systems. Information Technology and Tourism, 12(3), pp. 205–213, 2010.CrossRef
[513]
N. Rubens, D. Kaplan, and M. Sugiyama. Active learning in recommender systems. Recommender Systems Handbook, Springer, pp. 735–767, 2011.
[514]
N. Sahoo, R. Krishnan, G. Duncan, and J. Callan. Collaborative filtering with multi-component rating for recommender systems. Proceedings of the sixteenth workshop on information technologies and systems, 2006.
[521]
P. Samarati. Protecting respondents identities in microdata release. IEEE Transaction on Knowledge and Data Engineering, 13(6), pp. 1010–1027, 2001.CrossRef
[545]
Y. Shi, M. Larson, and A. Hanjalic. List-wise learning to rank with matrix factorization for collaborative filtering. ACM Conference on Recommender Systems, 2010.
[546]
Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, N. Oliver, and A. Hanjalic. CLiMF: Learning to maximize reciprocal rank with collaborative less-is-more collaborative filtering. ACM Conference on Recommender Systems, pp. 139–146, 2012.
[548]
Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, and A. Hanjalic. xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance. ACM Conference on Recommender Systems, pp. 431–434, 2013.
[549]
Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, A. Hanjalic, and N. Oliver. TFMAP: Optimizing MAP for top-n context-aware recommendation. ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 155–164, 2012.
[551]
R. Shokri, P. Pedarsani, G. Theodorakopoulos, and J. Hubaux. Preserving privacy in collaborative filtering through distributed aggregation of offline profiles. ACM Conference on Recommender Systems, pp. 157–164, 2009.
[578]
D. Sutherland, B. Poczos, and J. Schneider. Active learning and search on low-rank matrices. ACM KDD Conference, pp. 212–220, 2013.
[579]
R. Sutton and A. Barto. Reinforcement learning: An introduction, MIT Press, Cambridge, 1998.
[596]
T. Tang and G. McCalla. The pedagogical value of papers: a collaborative-filtering based paper recommender. Journal of Digital Information, 10(2), 2009.
[604]
A. Tsoukias, N. Matsatsinis, and K. Lakiotaki. Multi-criteria user modeling in recommender systems. IEEE Intelligent Systems, 26(2), pp. 64–76, 2011.CrossRef
[606]
A. Tveit. Peer-to-peer based recommendations for mobile commerce. Proceedings of the International Workshop on Mobile Commerce, pp. 26–29, 2001.
[621]
C. Wang, J. Han, Y. Jia, J. Tang, D. Zhang, Y. Yu, and J. Guo. Mining advisor-advisee relationships from research publication networks. ACM KDD Conference, pp. 203–212, 2010.
[624]
M. Weimer, A. Karatzoglou, Q. Le, and A. Smola. CoFiRank: Maximum margin matrix factorization for collaborative ranking. Advances in Neural Information Processing Systems, 2007.
[625]
M. Weimer, A. Karatzoglou, and A. Smola. Improving maximum margin matrix factorization. Machine Learning, 72(3), pp. 263–276, 2008.CrossRef
[628]
J. White. Bandit algorithms for Website optimization. O’Reilly Media, Inc, 2012.
[636]
[642]
Y. Xin and T. Jaakkola. Controlling privacy in recommender systems. Advances in Neural Information Processing Systems, pp. 2618–2626, 2014.
[653]
Z. Yu, X. Zhou, Y. Hao, and J. Gu. TV program recommendation for multiple viewers based on user profile merging. User Modeling and User-Adapted Interaction, 16(1), pp. 63–82, 2006.CrossRef
[654]
Z. Yu, X. Zhou, D. Zhang, C. Y. Chin, and X. Wang. Supporting context-aware media recommendations for smart phones. IEEE Pervasive Computing, 5(3), pp. 68–75, 2006.CrossRef
[657]
H. Zakerzadeh, C. Aggarwal and K. Barker. Towards breaking the curse of dimensionality for high-dimensional privacy. SIAM Conference on Data Mining, pp. 731–739, 2014.
[714]
Metadaten
Titel
Advanced Topics in Recommender Systems
verfasst von
Charu C. Aggarwal
Copyright-Jahr
2016
Buch
Recommender Systems

Print ISBN: 978-3-319-29657-9

Electronic ISBN: 978-3-319-29659-3

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-29659-3_13