Abstract
In many real scenarios, the buying and rating behaviors of customers are associated with temporal information. For example, the ratings in the Netflix Prize data set are associated with a “GradeDate” variable, and it was eventually shown [310] how the temporal component could be used to improve the rating predictions.
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Notes
- 1.
The original work [186] does not use a modulus in the denominator. We have added it in Equation 9.3 because omitting it does not make much sense in the case of negative similarity. Nevertheless, negative similarities in the peer item-group are rare in practical settings because the peers are defined as the most similar items.
- 2.
In the discussion of section 3.6.4.6, the bias variables are absorbed within the factor matrices U and V by increasing the number of columns in each of the two factor matrices from k to (k + 2). However, in this exposition, we do not absorb the bias variables in the columns of the factor matrices. This is because of the more complex and special way in which bias variables are treated in temporal models. For example, Equation 3.21 of Chapter 3 and Equation 9.6 are identical, but they use somewhat different notations. It is important to keep these notational distinctions in mind to avoid confusion.
- 3.
The work in [293] uses time-varying item factors.
- 4.
Refer to the bibliographic notes for background on Markov chains.
Bibliography
G. Adomavicius, R. Sankaranarayanan, S. Sen, and A. Tuzhilin. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems, 23(1), pp. 103–145, 2005.
G. Adomavicius and A. Tuzhilin. Context-aware recommender systems. Recommender Systems handbook, pp. 217–253, Springer, NY, 2011.
C. Aggarwal. Data mining: the textbook. Springer, New York, 2015.
C. Aggarwal and J. Han. Frequent pattern mining. Springer, New York, 2014.
R. Agrawal and R. Srikant. Mining sequential patterns. International Conference on Data Engineering, pp. 3–14, 1995.
H. Ahn, K. Kim, and I. Han. Mobile advertisement recommender system using collaborative filtering: MAR-CF. Proceedings of the 2006 Conference of the Korea Society of Management Information Systems, 2006.
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.
W. G. Aref and H. Samet. Efficient processing of window queries in the pyramid data structure. ACM PODS Conference, pp. 265–272, 1990.
D. Ashbrook and T. Starner. Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing, 7(5), pp. 275–286, 2003.
L. Baltrunas and X. Amatriain. Towards time-dependant recommendation based on implicit feedback. RecSys Workshop on Context-Aware Recommender Systems, 2009.
J. Bao, Y. Zheng, and M. Mokbel. Location-based and preference-aware recommendation using sparse geo-social networking data. International Conference on Advances in Geographic Information Systems, pp. 199–208, 2012.
A. Bar, L. Rokach, G. Shani, B. Shapira, and A. Schclar. Boosting simple collaborative filtering models using ensemble methods. Arxiv Preprint, arXiv:1211.2891, 2012. Also appears in Multiple Classifier Systems, Springer, pp. 1–12, 2013. http://arxiv.org/ftp/arxiv/papers/1211/1211.2891.pdf
F. Bohnert, I. Zukerman, S. Berkovsky, T. Baldwin, and L. Sonenberg. Using interest and transition models to predict visitor locations in museums. AI Communications, 2(2), pp. 195–202, 2008.
B. Bouneffouf, A. Bouzeghoub, and A. Gancarski. A contextual-bandit algorithm for mobile context-aware recommender system. Neural Information Processing, pp. 324–331, 2012.
A. Brenner, B. Pradel, N. Usunier, and P. Gallinari. Predicting most rated items in weekly recommendation with temporal regression. Workshop on Context-Aware Movie Recommendation, pp. 24–27, 2010.
M. Brunato and R. Battiti. PILGRIM: A location broker and mobility-aware recommendation system. International Conference on Pervasive Computing and Communications, pp. 265–272, 2003.
P. Brusilovsky, A. Kobsa, and W. Nejdl. The adaptive web: methods and strategies of web personalization, Lecture Notes in Computer Sceince, Vol. 4321, Springer, 2007.
P. Campos, F. Diez, and I. Cantador. Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Modeling and User-Adapted Interaction, 24(1–2), pp. 67–119, 2014.
P. Campos, A. Bellogin, F. Diez, and J. Chavarriaga. Simple time-biased KNN-based recommendations. Workshop on Context-Aware Movie Recommendation, pp. 20–23, 2010.
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.
K. Cheverst, N. Davies, K. Mitchell, A. Friday, and C. Efstratiou. Developing a context-aware electronic tourist guide: some issues and experiences. ACM SIGCHI Conference on Human Factors in Computing Systems, pp. 17–24, 2000.
R. Cooley, B. Mobasher, and J. Srivastava. Data preparation for mining World Wide Web browsing patterns. Knowledge and Information Systems, 1(1), pp. 5–32, 1999.
B. De Carolis, I. Mazzotta, N. Novielli, and V. Silvestri. Using common sense in providing personalized recommendations in the tourism domain. Workshop on Context-Aware Recommender Systems, 2009.
M. Deshpande and G. Karypis. Selective Markov models for predicting Web page accesses. ACM Transactions on Internet Technology (TOIT), 4(2), pp. 163–184, 2004.
Y. Ding and X. Li. Time weight collaborative filtering. ACM International Conference on Information and Knowledge Management, pp. 485–492, 2005.
Y. Ding, X. Li, and M. Orlowska. Recency-based collaborative filtering. Australasian Database Conference, pp. 99–107, 2009.
R. A. Finkel and J. L. Bentley. Quad trees: A data structure for retrieval on composite keys. Acta Informatica, 4, pp. 1–9, 1974.
X. Fu, J. Budzik, and K. J. Hammond. Mining navigation history for recommendation. International Conference on Intelligent User Interfaces, 2000.
Z. Gantner, S. Rendle, and L. Schmidt-Thieme. Factorization models for context-/time-aware movie recommendations. Workshop on Context-Aware Movie Recommendation, pp. 14–19, 2010.
A. Garcia-Crespo, J. Chamizo, I. Rivera, M. Mencke, R. Colomo-Palacios, and J. M. Gomez-Berbis. SPETA: Social pervasive e-Tourism advisor. Telematics and Informatics 26(3), pp. 306–315. 2009.
M. Gery and H. Haddad. Evaluation of Web usage mining approaches for user’s next request prediction. ACM international workshop on Web information and data management, pp. 74–81, 2003.
S. Gordea and M. Zanker. Time filtering for better recommendations with small and sparse rating matrices. International Conference on Web Information Systems Engineering, pp. 171–183, 2007.
M. Gorgoglione and U. Panniello. Including context in a transactional recommender system using a pre- filtering approach: two real e-commerce applications. International Conference on Advanced Information Networking and Applications Workshops, pp. 667–672, 2009.
C. Hermann. Time-based recommendations for lecture materials. World Conference on Educational Multimedia, Hypermedia and Telecommunications, pp. 1028–1033, 2010.
D. Isaacson and R. Madsen. Markov chains, theory and applications, Wiley, 1976.
M. Jahrer, A. Toscher, and R. Legenstein. Combining predictions for accurate recommender systems. ACM KDD Conference, pp. 693–702, 2010.
A. Karatzoglou. Collaborative temporal order modeling. ACM Conference on Recommender Systems, pp. 313–316, 2011.
A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver. Multiverse recommendation: N-dimensional tensor factorization for context-aware collaborative filtering. ACM Conference on Recommender Systems, pp. 79–86, 2010.
J. Kemeny and J. Snell. Finite Markov chains. Springer, New York, 1983.
N. Koenigstein, G. Dror, and Y. Koren. Yahoo! Music recommendations: modeling music ratings with temporal dynamics and item taxonomy. ACM Conference on Recommender Systems, pp. 165–172, 2011.
Y. Koren. Collaborative filtering with temporal dynamics. ACM KDD Conference, pp. 447–455, 2009. Another version also appears in the Communications of the ACM,, 53(4), pp. 89–97, 2010.
Y. Koren and R. Bell. Advances in collaborative filtering. Recommender Systems Handbook, Springer, pp. 145–186, 2011. (Extended version in 2015 edition of handbook).
J. Krosche, J. Baldzer, and S. Boll. MobiDENK -mobile multimedia in monument conservation. IEEE MultiMedia, 11(2), pp. 72–77, 2004.
A. Krogh, M. Brown, I. Mian, K. Sjolander, and D. Haussler. Hidden Markov models in computational biology: Applications to protein modeling. Journal of molecular biology, 235(5), pp. 1501–1531, 1994.
L. Lathauwer, B. Moor, and J. Vandewalle. A multilinear singular value decomposition. SIAM Journal on Matrix Analysis and Applications, 21(4), pp. 1253–1278. 2000.
N. Lathia, S. Hailes, and L. Capra. Temporal collaborative filtering with adaptive neighbourhoods. ACM SIGIR Conference, pp. 796–797, 2009.
N. Lathia, S. Hailes, L. Capra, and X. Amatriain. Temporal diversity in recommender systems. ACM SIGIR Conference, pp. 210–217, 2010.
D. Lee, S. Park, M. Kahng, S. Lee, and S. Lee. Exploiting contextual information from event logs for personalized recommendation. Chapter in Computer and Information Science, Springer, 2010.
J. Levandoski, M. Sarwat, A. Eldawy, and M. Mokbel. LARS: A location-aware recommender system. IEEE ICDE Conference, pp. 450–461, 2012.
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.
N. Liu, M. Zhao, E. Xiang, and Q Yang. Online evolutionary collaborative filtering. ACM Conference on Recommender Systems, pp. 95–102, 2010.
S. Min and I. Han. Detection of the customer time-variant pattern for improving recommender systems. Expert Systems and Applications, 28(2), pp. 189–199, 2005.
B. Mobasher, R. Cooley, and J. Srivastava. Automatic personalization based on Web usage mining. Communications of the ACM, 43(8), pp. 142–151, 2000.
B. Mobasher, H. Dai, T. Luo, and H. Nakagawa. Using sequential and non-sequential patterns in predictive web usage mining tasks. International Conference on Data Mining, pp. 669–672, 2002.
B. Mobasher, H. Dai, M. Nakagawa, and T. Luo. Discovery and evaluation of aggregate usage profiles for web personalization. Data Mining and Knowledge Discovery, 6: pp. 61–82, 2002.
M. Mokbel and J. Levandoski. Toward context and preference-aware location-based services. ACM International Workshop on Data Engineering for Wireless and Mobile Access, pp. 25–32, 2009.
K. Oku, S. Nakajima, J. Miyazaki, and S. Uemura. Context-aware SVM for context-dependent information recommendation. International Conference on Mobile Data Management, pp. 109–109, 2006.
M. Park, J. Hong, and S. Cho. Location-based recommendation system using Bayesian user’s preference model in mobile devices. Ubiquitous Intelligence and Computing, pp. 1130–1139, 2007.
U. Panniello, A. Tuzhilin, M. Gorgoglione, C. Palmisano, and A. Pedone. Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems. ACM Conference on Recommender Systems, pp. 265–268, 2009.
J. Pitkow and P. Pirolli. Mining longest repeating subsequences to predict WWW surfing. USENIX Annual Technical Conference, 1999.
S. Rendle. Context-aware ranking with factorization models. Studies in Computational Intelligence, Chapter 9, Springer, 2011.
S. Rendle, Z. Gantner, C. Freudenthaler, and L. Schmidt-Thieme. Fast context-aware recommendations with factorization machines. ACM SIGIR Conference, pp. 635–644, 2011.
F. Ricci. Mobile recommender systems. Information Technology and Tourism, 12(3), pp. 205–213, 2010.
A. Said, S. Berkovsky, and E. de Luca. Putting things in context: challenge on context-aware movie recommendation. Proceedings of the Workshop on Context-Aware Movie Recommendation, 2010.
S. Schechter, M. Krishnan, and M. D. Smith. Using path profiles to predict http requests. World Wide Web Conference, 1998.
J. Srivastava, R. Cooley, M. Deshpande, and P.-N. Tan. Web usage mining: discovery and applications of usage patterns from Web data. ACM SIGKDD Explorations, 1(2), pp. 12–23, 2000.
H. Stormer. Improving e-commerce recommender systems by the identification of seasonal products. Conference on Artificial Intelligence, pp. 92–99, 2007.
T. Tang, P. Winoto, and K. C. C. Chan. On the temporal analysis for improved hybrid recommendations. International Conference on Web Intelligence, pp. 214–220, 2003.
M. van Setten, S. Pokraev, and J. Koolwaaij. Context-aware recommendations in the mobile tourist application compass. Adaptive Hypermedia, Springer, pp. 235–244, 2004.
V. Vlahakis, N. Ioannidis, J. Karigiannis, M. Tsotros, M. Gounaris, D. Stricker, T. Gleue, P. Daehne, and L. Almeida. Archeoguide: an augmented reality guide for archaeological sites. IEEE Computer Graphics and Applications, 22(5), pp. 52–60, 2002.
S.-S. Weng, L. Binshan, and W.-T. Chen. Using contextual information and multidimensional approach for recommendation. Expert Systems and Applications, 36, pp. 1268–1279, 2009.
W. Woerndl, C. Schueller, and R. Wojtech. A hybrid recommender system for context-aware recommendations of mobile applications. IEEE International Conference on Data Engineering Workshop, pp. 871–878, 2007.
P. Wu, C. Yeung, W. Liu, C. Jin, and Y. Zhang. Time-aware collaborative filtering with the piecewise decay function. arXiv preprint, arXiv:1010.3988, 2010. http://arxiv.org/pdf/1010.3988.pdf
L. Xiang, Q. Yuan, S. Zhao, L. Chen, X. Zhang, Q. Yang, and J. Sun. Temporal recommendation on graphs via long-and short-term preference fusion. ACM KDD Conference, pp. 723–732, 2010.
W. Yang, H. Cheng, and J. Dia. A location-aware recommender system for mobile shopping environments. Expert Systems with Applications, 34(1), pp. 437–445, 2008.
H. Yin, Y. Sun, B. Cui, Z. Hu, and L. Chen. LCARS: A location-content-aware recommender system. ACM KDD Conference, pp. 221–229, 2013.
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.
Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. Thalmann. Time-aware point-of-interest recommendation. ACM SIGIR Conference, pp. 363–372, 2013.
A. Zimdars, D. Chickering, and C. Meek. Using temporal data for making recommendations. Uncertainty in Artificial Intelligence, pp. 580–588, 2001.
A. Zimmermann, M. Specht, and A. Lorenz. Personalization and context management. User Modeling and User-Adapted Interaction, 15(3–4), pp. 275–302, 2005.
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Aggarwal, C.C. (2016). Time- and Location-Sensitive Recommender Systems. In: Recommender Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-29659-3_9
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