Abstract
The purpose of this paper is to propose a novel latent factor model that generates a ranked list of items in the recommendation list based on prior interaction with system on e-commerce platforms. The ranking of items in recommendation list is exhibited as an optimization model that optimizes the ranking metrics. The latent features of user and items are learnt using cosine based latent factor model which in turn are used to learn the ranking metric. This paper proposes cosine based latent factor model to learn the implicit features, and corresponding surrogate ranking loss function is optimized. Comprehensive evaluation on three benchmark datasets shows the considerable improvement of the proposed model on ranking metric.
Similar content being viewed by others
References
Adolphs C, Winkelmann A (2010) Personalization research in e-commerce–a state of the art review (2000–2008). J Electron Commer Res 11(4):326–341
Agarwal D, Chen B-C (2009) Regression-based latent factor models. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 19–28
Ahmed A, Kanagal B, Pandey S, Josifovski V, Pueyo LG, Yuan J (2013) Latent factor models with additive and hierarchically-smoothed user preferences. In: Proceedings of the sixth ACM international conference on web search and data mining—WSDM’13. https://doi.org/10.1145/2433396.2433445
Anand D, Bharadwaj KK (2011) Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities. Expert Syst Appl 38(5):5101–5109
Balakrishnan S, Chopra S, Ave P, Park F, Ave P, Park F (2012) Collaborative ranking. In: Proceedings of the fifth ACM international conference on web search and data mining—WSDM’12, p 143. https://doi.org/10.1145/2124295.2124314
Bauer J, Nanopoulos A (2014) Recommender systems based on quantitative implicit customer feedback. Decis Support Syst 68:77–88
Bell RM, Koren Y, Ave P, Park F (2007) Scalable collaborative filtering with jointly derived neighborhood interpolation weights. https://doi.org/10.1109/ICDM.2007.90
Bellogin A, Castells P, Cantador I (2011) Precision-oriented evaluation of recommender systems: an algorithmic comparison. In: Proceedings of the fifth ACM conference on recommender systems, ACM, New York, pp 333–336. https://doi.org/10.1145/2043932.2043996
Burges C, Burges C, Renshaw E, Renshaw E, Deeds M, Deeds M, Hullender G (1998) Learning to rank using gradient descent. World. doi:10.1145/1102351.1102363
Cao Z, Qin T, Liu T-Y, Tsai M-F, Li H (2007) Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the 24th international conference on machine learning, pp 129–136. https://doi.org/10.1145/1273496.1273513
Chen W, Liu T, Lan Y, Ma Z, Li H (2009) Ranking measures and loss functions in learning to rank. In: Nips (pp. 1–9). Retrieved from https://papers.nips.cc/paper/3708-ranking-measures-and-loss-functions-in-learning-to-rank.pdf
Cheung KW, Kwok JT, Law MH, Tsui KC (2003) Mining customer product ratings for personalized marketing. Decis Support Syst 35(2):231–243. doi:10.1016/S0167-9236(02)00108-2
Chung CY, Hsu PY, Huang SH (2013) Bp: a novel approach to filter out malicious rating profiles from recommender systems. Decis Support Syst 55(1):314–325. doi:10.1016/j.dss.2013.01.020
Crammer K, Singer Y (2002) Pranking with ranking. In: Advances in neural information processing systems 14, Vol. 14, pp. 641–647. https://doi.org/10.1.1.20.378
Deng X, Wang X (2009) Mining rank-correlated associations for recommendation systems. In: International Conference on web information systems and mining, 2009, WISM 2009, IEEE, pp 625–629
du Boucher-Ryan P, Bridge D (2006) Collaborative recommending using formal concept analysis. Knowl Based Syst 19(5):309–315. doi:10.1016/j.knosys.2005.11.017
Ekstrand MD (2010) Collaborative filtering recommender systems. Found Trends® Hum Comput Interact 4(2):81–173. doi:10.1561/1100000009
Freund Y, Iyer R, Schapire RE, Singer Y (2003) An Efficient boosting algorithm for combining preferences. J Mach Learn Res 4:933–969. doi:10.1162/jmlr.2003.4.6.933
Gan M, Jiang R (2013) Improving accuracy and diversity of personalized recommendation through power law adjustments of user similarities. Decis Support Syst 55(3):811–821. doi:10.1016/j.dss.2013.03.006
Gao M, Liu K, Wu Z (2010) Personalisation in web computing and informatics: theories, techniques, applications, and future research. Inf Syst Front 12(5):607–629
Gentile C, Li S, Giovanni Z (2014) Online Clustering of Bandits. Icml 32:757–765
Goldberg K, Roeder T, Gupta D, Perkins C (2001) A constant time collaborative filtering algorithm. Inf Retrieval 4(2):133–151
Hao F, Li S, Min G, Kim HC, Yau SS, Yang LT (2015) An Efficient approach to generating location-sensitive recommendations in ad-hoc social network environments. IEEE Trans Serv Comput. doi:10.1109/TSC.2015.2401833
Harrington EF (2003) Online Ranking/Collaborative Filtering Using the Perceptron Algorithm. In: Proceedings of the 20th international conference on machine learning, pp 250–257
Hernández del Olmo F, Gaudioso E (2008) Evaluation of recommender systems: a new approach. Expert Syst Appl 35(3):790–804. doi:10.1016/j.eswa.2007.07.047
Ho Y, Kyeong J, Hie S (2002) A personalized recommender system based on web usage mining and decision tree induction. Expert Syst Appl 23:329–342
Huang J, Zhong N, Yao Y (2014) A unified framework of targeted marketing using customer preferences. Comput Intell 30(3):451–472. doi:10.1111/coin.12003
Improvement of Collaborative Filtering with the Simple Bayesian Classifier. (n.d.), pp 1–28
Järvelin K, Kekäläinen J (2002) Cumulated gain-based evaluation of IR techniques. ACM Trans Inf Syst 20(4):422–446. doi:10.1145/582415.582418
Jawaheer G, Weller P, Kostkova P (2014) Modeling user preferences in recommender systems: a classification framework for explicit and implicit user feedback. ACM Trans Interact Intell Syst 4(2):8:1–8:26. doi:10.1145/2512208
Joachims T (2002) Optimizing search engines using clickthrough data. In: Proceedings of the eighth acm sigkdd international conference on knowledge discovery and data mining, ACM, New York, pp 133–142. https://doi.org/10.1145/775047.775067
Kagie M, Van Der Loos M, Van Wezel M (2009) Including item characteristics in the probabilistic latent semantic analysis model for collaborative filtering. Ai Commun 22(4):249–265
Kannan R, Ishteva M, Park H (2012) Bounded matrix low rank approximation. In: Proceedings—IEEE international conference on data mining, ICDM, pp 319–328. https://doi.org/10.1109/ICDM.2012.131
Kar P, Li S, Narasimhan H, Chawla S, Sebastiani F. (2016) Online optimization methods for the quantification problem. https://doi.org/10.1145/2939672.2939832
Karatzoglou A, Larson M, Oliver N, Hanjalic A (2012) CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering. In: Proceedings of the 6th ACM conference on recommender systems—RecSys’12, pp 139–146. https://doi.org/10.1145/2365952.2365981
Karatzoglou A, Baltrunas L, Shi Y (2013) Learning to rank for recommender systems. In Proceedings of the 7th ACM conference on Recommender systems, ACM, pp. 493–494
Karypis G (2001) Evaluation of item-based top-n recommendation algorithms. In: Proceedings of the tenth international conference on Information and knowledge management, ACM, pp 247–254
Kelleher J, Bridge D (2004) An accurate and scalable collaborative recommender. Artif Intell Rev 21(3–4):193–213
Kim YS, Yum B-J, Song J, Kim SM (2005) Development of a recommender system based on navigational and behavioral patterns of customers in e-commerce sites. Expert Syst Appl 28(2):381–393
Korda N, Szörényi B, Shuai L (2016) Distributed clustering of linear bandits in peer to peer networks. J Mach Learn Res Workshop Conf Proc 48:1301–1309
Koren Y, Bell R (2011) Advances in collaborative filtering. In: Recommender systems handbook, pp 145–186. https://doi.org/10.1007/978-0-387-85820-3
Koren Y, Sill J (2011) OrdRec: an ordinal model for predicting personalized item rating distributions. In: Proceedings of the fifth ACM conference on Recommender systems, ACM, pp 117–124
Lee J, Bengio S, Kim S (2014) Local collaborative ranking. In: Proceedings of the 23rd international conference on World wide web, pp. 85–95. https://doi.org/10.1145/2566486.2567970
Li S, Hao F, Li M, Kim H-C (2013) Medicine rating prediction and recommendation in mobile social networks. In: Park JJJH, Arabnia HR, Kim C, Shi W, Gil J-M (eds) Grid and pervasive computing: 8th international conference, GPC 2013 and colocated workshops, Seoul, Korea, May 9–11, 2013. Proceedings, Springer, Berlin, Heidelberg, pp 216–223. https://doi.org/10.1007/978-3-642-38027-3_23
Li S, Karatzoglou A, Gentile C (2016) Collaborative filtering bandits. Sigir. doi:10.1145/2911451.2911548
Liu D, Liou C (2011) Mobile commerce product recommendations based on hybrid multiple channels. Electron Commer Res Appl 10(1):94–104. doi:10.1016/j.elerap.2010.08.004
Liu NN, Yang Q (2008) Eigen rank. In: Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval—SIGIR’08, p 83. https://doi.org/10.1145/1390334.1390351
Liu Q, Chen E, Xiong H, Ding CHQ, Chen J (2012) Enhancing collaborative filtering by user interest expansion via personalized ranking. IEEE Trans Syst Man Cybern Part B (Cybern) 42(1):218–233
Liu J, Wu C, Xiong Y, Liu W (2014) List-wise probabilistic matrix factorization for recommendation. Inf Sci 278:434–447. doi:10.1016/j.ins.2014.03.063
McAuley J, Leskovec J (2013) Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM conference on recommender systems—RecSys’13, pp 165–172. https://doi.org/10.1145/2507157.2507163
Niemann K, Wolpers M (2013) A new collaborative filtering approach for increasing the aggregate diversity of recommender systems. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’13, pp 955–963. doi:10.1145/2487575.2487656
Ning X, Karypis G (2011) Slim: sparse linear methods for top-n recommender systems. In: 2011 IEEE 11th international conference on data mining, IEEE, pp 497–506
Paterek A (2007) Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of the KDD cup workshop at SIGKDD’07, 13th ACM International Conference on knowledge discovery and data mining, pp 39–42. Retrieved from http://serv1.ist.psu.edu:8080/viewdoc/summary;jsessionid=CBC0A80E61E800DE518520F9469B2FD1?doi=10.1.1.96.7652
Pennock DM, Horvitz E, Lawrence S, Giles CL (2000) Collaborative filtering by personality diagnosis: A hybrid memory-and model-based approach. In: Proceedings of the 16th conference on uncertainty in artificial intelligence 64(10):473–480
Polytechnique É, De Lausanne F, Aberer, K. (2014). Towards a dynamic top-N recommendation framework. In: Proceedings of the 8th ACM conference on recommender systems—RecSys’14, pp 217–224. https://doi.org/10.1145/2645710.2645720
Qin T, Zhang XD, Tsai MF, Wang DS, Liu TY, Li H (2008) Query-level loss functions for information retrieval. Inf Process Managt 44(2):838–855. doi:10.1016/j.ipm.2007.07.016
Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR: bayesian personalized ranking from implicit feedback. In: Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, AUAI Press, pp. 452–461
Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM conference on computer supported cooperative work, ACM, pp 175–186
Rupnik J (2010) Learning to rank for personalized news article retrieval. J Mach Learn Res Proc Track 11:152–159
Salakhutdinov R, Mnih A (2007) Probabilistic Matrix Factorization. In: Proceedings of the 20th international conference on neural information processing systems, pp 1257–1264
Sarwar BM, Karypis G, Konstan JA, Riedl JT (2000) Application of dimensionality reduction in recommender systems: a case study. In: ACM WebKDD workshop, 67, 12. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.744
Shi Y, Larson M, Hanjalic A (2010) List-wise learning to rank with matrix factorization for collaborative filtering. In: Proceedings of the fourth ACM conference on recommender systems, ACM, pp 269–272
Steck H, Labs B, Ave M, Hill M (2010) Training and testing of recommender systems on data missing not at random. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, pp 713–722. https://doi.org/10.1145/1835804.1835895
Takács G, Pilászy I, Németh B, Tikk D (2009) Scalable collaborative filtering approaches for large recommender systems. J Mach Learn Res 10: pp 623–656. Retrieved from http://portal.acm.org/citation.cfm?id=1577069.1577091
Taylor M, Guiver J, Robertson S, Minka T (2008) Softrank: optimizing non-smooth rank metrics. In: Proceedings of the 2008 international conference on web search and data mining, ACM, pp 77–86
Vahidov R, Ji F (2005) A diversity-based method for infrequent purchase decision support in e-commerce. 4:143–158. https://doi.org/10.1016/j.elerap.2004.09.001
Valizadegan H, Jin R (2009) Learning to rank by optimizing ndcg measure. Adv Neural. doi:10.1561/1500000016
Weimer M, Karatzoglou A (2007) Cofirank-maximum margin matrix factorization for collaborative ranking. Adv Neural pp 1–30. Retrieved from http://hal.archives-ouvertes.fr/docs/00/48/27/40/PDF/NIPS2007_0612.pdf
Weimer M, Karatzoglou A, Bruch M (2009) Maximum margin matrix factorization for collaborative Ranking. In: Proceedings of the RecSys, pp 309–312. https://doi.org/10.1145/1639714.1639775
Xia F, Liu T-Y, Wang J, Zhang W, Li H (2008) Listwise approach to learning to rank. In: Proceedings of the 25th international conference on machine learning—ICML’08, pp 1192–1199. https://doi.org/10.1145/1390156.1390306
Yang X, Guo Y, Liu Y, Steck H (2014) A survey of collaborative filtering based social recommender systems. Comput Commun 41:1–10. doi:10.1016/j.comcom.2013.06.009
Yu S, Yu K, Tresp V, Kriegel H-P (2006) Collaborative ordinal regression. In: Proceedings of the 23rd international conference on machine learning—ICML’06, pp 1089–1096. https://doi.org/10.1145/1143844.1143981
Yu K, Zhu S, Lafferty J, Gong Y (2009) Fast nonparametric matrix factorization for large-scale collaborative filtering. In: Proceedings of the 32Nd international ACM SIGIR conference on research and development in information retrieval, ACM, New York, pp 211–218. https://doi.org/10.1145/1571941.1571979
Zhang M (2009) Enhancing diversity in top-N recommendation. In: Proceedings of the third ACM conference on recommender systems—RecSys’09, p 397. https://doi.org/10.1145/1639714.1639798
Ziegler C-N, McNee SM, Konstan JA, Lausen G (2005) Improving recommendation lists through topic diversification. In: Proceedings of the 14th international conference on World Wide Web, ACM, pp. 22–32. https://doi.org/10.1109/ICICISYS.2009.5358201
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kumar, B., Bala, P.K. Cosine based latent factor model for ranking the recommendation. Oper Res Int J 20, 297–317 (2020). https://doi.org/10.1007/s12351-017-0325-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12351-017-0325-6