Abstract
Recommender systems have been proven as an essential tool to solve the information overload problem due to the burst of Internet traffic, however traditional approaches only consider to recommend items that users have not seen before, and thus ignore the significance of those items in a user’s historical records. This is motivated by the fact that users often revisit those items they have watched before, especially for TV series. Based on this, in this paper, we introduce a new concept called “revisiting ratio”, to uniquely represent the ratio between the new and old items. We also propose a “preference model” to aid selecting the most related historical records. Finally, theoretical analysis and extensive results are supplemented to show the advantages of the proposed system.
This work is financially sponsored by National Natural Science Foundation of China (Grant No. 61300179).
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Chen, M., Mao, S., Liu, Y.: Big data: a survey. ACM/Springer Mob. Netw. Appl. (ACM MONET) 19, 171–209 (2014)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: ACM WWW 2001, pp. 285–295 (2001)
Song, S., Wu, K.: A creative personalized recommendation algorithm; user-based slope one algorithm. In: IEEE Systems and Informatics (ICSAI 2012), pp. 2203–2207 (2012)
Shi, Y., Larson, M., Hanjalic, A.: Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges. ACM Comput. Surv. (CSUR) 47, 3:1–3:45 (2014)
Ba, Q., Li, X., Bai, Z.: Clustering collaborative filtering recommendation system based on svd algorithm. In: IEEE Software Engineering and Service Sciences (ICSESS 2013), pp. 963–967 (2013)
Zhang, D., Hsu, C.H., Chen, M., Chen, Q., Xiong, N., Lloret, J.: Cold-start recommendation using bi-clustering and fusion for large-scale social recommender systems. IEEE Trans. Emerg. Top. Comput. 2, 239–250 (2014)
Song, J., He, L., Lin, X.: Improving the accuracy of tagging recommender system by using classification. In: IEEE Advanced Communication Technology (ICACT 2010), vol. 1, pp. 387–391 (2010)
Bedi, P., Agarwal, S.K.: Preference learning in aspect-oriented recommender system. In: IEEE Computing Intelligence and Communication Networks (CICN 2011), pp. 611–615 (2011)
Ghazanfar, M.A., Prugel-Bennett, A.: A scalable, accurate hybrid recommender system. In: IEEE Knowledge Discovery and Data Mining (WKDD 2010), pp. 94–98 (2010)
Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. Springer, Boston (2011)
Jafarkarimi, H., Sim, A.T.H., Saadatdoost, R.: A naive recommendation model for large databases. Int. J. Inf. Educ. Technol. 2, 216–219 (2012)
Talabeigi, M., Forsati, R., Meybodi, M.R.: A hybrid web recommender system based on cellular learning automata. In: IEEE Granular Computing (GrC 2010), pp. 453–458 (2010)
Diaby, M., Viennet, E., Launay, T.: Toward the next generation of recruitment tools: an online social network-based job recommender system. In: ACM ASONAM 2013, pp. 821–828 (2013)
Xia, P., Xiao, J., Shu, C.: An application of recommender system with mingle-topn algorithm on b2b platform. In: IEEE Advanced Cloud and Big Data (CBD 2013), pp. 170–176 (2013)
Zhang, Y., Wang, L., Hu, L., Wang, X., Chen, M.: Comer: cloud-based medicine recommendation. In: QShine 2014, pp. 18–19 (2014)
Bouneffouf, D., Bouzeghoub, A., Gançarski, A.L.: Following the user’s interests in mobile context-aware recommender systems: the hybrid-e-greedy algorithm. In: IEEE Advanced Information Networking and Applications Workshops (WAINA 2012), pp. 657–662 (2012)
Verma, S.K., Mittal, N., Agarwal, B.: Hybrid recommender system based on fuzzy clustering and collaborative filtering. In: International Conference Computing and Communication Technology (ICCCT 2013), pp. 116–120 (2013)
Jin, J., Chen, Q.: A trust-based top-k recommender system using social tagging network. In: Fuzzy Systems and Knowledge Discovery (FSKD 2012). pp. 1270–1274 (2012)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.T.: Application of dimensionality reduction in recommender system-a case study. Technical report, DTIC Document (2000)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22, 5–53 (2004)
Beel, J., Langer, S., Gipp, B.: A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation. In: ACM RepSys 2013, pp. 7–14 (2013)
Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: ACM Conference on Digital Libraries, pp. 195–204 (2000)
Gupta, R., Jain, A., Rana, S., Singh, S.: Contextual information based recommender system using singular value decomposition. In: Advances in Computing, Communications and Informatics (ICACCI 2013), pp. 2084–2089 (2013)
CSDN: Item-Based Collaborative Filtering Recommendation Algorithms. http://blog.csdn.net/huagong_adu/article/details/7362908
Hamilton, J.D.: Time Series Analysis, vol. 2. Princeton University Press, Princeton (1994)
Alsultanny, Y.: Successful forecasting for knowledge discovery by statistical methods. In: IEEE Information Technology: New Generations (ITNG 2012), pp. 584–588 (2012)
Apache: Myrrix. http://myrrix.com/
Apache: Hadoop. http://hadoop.apache.org/
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Zhang, Z., Huang, Z., Gao, G., Liu, C.H. (2015). Personalized Video Recommendations with Both Historical and New Items. In: Leung, V., Lai, R., Chen, M., Wan, J. (eds) Cloud Computing. CloudComp 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 142. Springer, Cham. https://doi.org/10.1007/978-3-319-16050-4_3
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DOI: https://doi.org/10.1007/978-3-319-16050-4_3
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