›› 2018,Vol. 33 ›› Issue (4): 739-755.doi: 10.1007/s11390-018-1853-0

所属专题: Artificial Intelligence and Pattern Recognition Data Management and Data Mining

• Special Section on Computer Networks and Distributed Computing • 上一篇    下一篇

通过物品的显式关系理解推荐系统

Qi Liu1, Member, ACM, IEEE, Hong-Ke Zhao1, Le Wu2, Zhi Li1,3, En-Hong Chen1,*, Fellow, CCF, Senior Member, IEEE   

  1. 1 Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China Hefei 230027, China;
    2 School of Computer and Information, Hefei University of Technology, Hefei 230009, China;
    3 School of Software Engineering, University of Science and Technology of China, Hefei 230051, China
  • 收稿日期:2018-01-15 修回日期:2018-06-01 出版日期:2018-07-05 发布日期:2018-07-05
  • 通讯作者: En-Hong Chen,E-mail:cheneh@ustc.edu.cn E-mail:cheneh@ustc.edu.cn
  • 作者简介:Qi Liu received his Ph.D. degree in computer science from University of Science and Technology of China (USTC), Hefei, in 2013. He is an associate professor with USTC, Hefei. His general area of research is data mining and knowledge discovery. He has published prolifically in refereed journals and conference proceedings, e.g., IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Information Systems, ACM Transactions on Knowledge Discovery from Data, ACM Transactions on Intelligent Systems and Technology, ACM SIGKDD, IJCAI, AAAI, IEEE ICDM, SDM, and ACM CIKM. He has served regularly on the program committees of a number of conferences, and is a reviewer for the leading academic journals in his fields. He received the ICDM 2011 Best Research Paper Award. He is a member of ACM and IEEE.
  • 基金资助:

    This research was partially supported by the National Natural Science Foundation of China under Grant Nos. U1605251, 61672483 and 61602147, and the Fundamental Research Funds for the Central Universities of China under Grant No. JZ2016HGBZ0749. Qi Liu gratefully acknowledges the support of the Young Elite Scientist Sponsorship Program of China Association for Science and Technology (CAST) and the Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) under Grant No. 2014299.

Illuminating Recommendation by Understanding the Explicit Item Relations

Qi Liu1, Member, ACM, IEEE, Hong-Ke Zhao1, Le Wu2, Zhi Li1,3, En-Hong Chen1,*, Fellow, CCF, Senior Member, IEEE   

  1. 1 Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China Hefei 230027, China;
    2 School of Computer and Information, Hefei University of Technology, Hefei 230009, China;
    3 School of Software Engineering, University of Science and Technology of China, Hefei 230051, China
  • Received:2018-01-15 Revised:2018-06-01 Online:2018-07-05 Published:2018-07-05
  • Contact: En-Hong Chen,E-mail:cheneh@ustc.edu.cn E-mail:cheneh@ustc.edu.cn
  • About author:Qi Liu received his Ph.D. degree in computer science from University of Science and Technology of China (USTC), Hefei, in 2013. He is an associate professor with USTC, Hefei. His general area of research is data mining and knowledge discovery. He has published prolifically in refereed journals and conference proceedings, e.g., IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Information Systems, ACM Transactions on Knowledge Discovery from Data, ACM Transactions on Intelligent Systems and Technology, ACM SIGKDD, IJCAI, AAAI, IEEE ICDM, SDM, and ACM CIKM. He has served regularly on the program committees of a number of conferences, and is a reviewer for the leading academic journals in his fields. He received the ICDM 2011 Best Research Paper Award. He is a member of ACM and IEEE.
  • Supported by:

    This research was partially supported by the National Natural Science Foundation of China under Grant Nos. U1605251, 61672483 and 61602147, and the Fundamental Research Funds for the Central Universities of China under Grant No. JZ2016HGBZ0749. Qi Liu gratefully acknowledges the support of the Young Elite Scientist Sponsorship Program of China Association for Science and Technology (CAST) and the Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) under Grant No. 2014299.

近些年见证了推荐系统在众多应用领域的普及。推荐系统基于多源信息为每个用户生成一个可供选择的推荐物品列表。在很长一段时间里,大多数的研究者追求推荐系统在特定指标上的表现效果,例如,准确性。然而,在现实社会中,用户从大量的商品中选择物品时会主要考虑他们内部的需求和外部的约束。因此,我们认为,在具体的应用领域中显式地建模物品关系对于理解推荐系统而言十分有必要。事实上,在该领域,研究者已经做了一些相关工作,对推荐过程的理解也逐步从隐式转向显式的角度。因此,在这篇文章中,我们从物品显式关系理解的角度整理了推荐系统领域最近的研究进展。我们主要从三个方面来组织相关工作,即:物品组合效应关系,序列依赖关系和外部约束关系。具体来说,组合效应关系和序列依赖关系的相关工作从用户的需求角度建模物品的内部关系,而外部约束关系则强调物品之间的外部要求关系。在此之后,我们也提出了在物品显性关系方面的开放性问题和在推荐系统领域未来的研究建议。

Abstract: Recent years have witnessed the prevalence of recommender systems in various fields, which provide a personalized recommendation list for each user based on various kinds of information. For quite a long time, most researchers have been pursing recommendation performances with predefined metrics, e.g., accuracy. However, in real-world applications, users select items from a huge item list by considering their internal personalized demand and external constraints. Thus, we argue that explicitly modeling the complex relations among items under domain-specific applications is an indispensable part for enhancing the recommendations. Actually, in this area, researchers have done some work to understand the item relations gradually from "implicit" to "explicit" views when recommending. To this end, in this paper, we conduct a survey of these recent advances on recommender systems from the perspective of the explicit item relation understanding. We organize these relevant studies from three types of item relations, i.e., combination-effect relations, sequence-dependence relations, and external-constraint relations. Specifically, the combination-effect relation and the sequence-dependence relation based work models the intra-group intrinsic relations of items from the user demand perspective, and the external-constraint relation emphasizes the external requirements for items. After that, we also propose our opinions on the open issues along the line of understanding item relations and suggest some future research directions in recommendation area.

[1] Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems:A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6):734-749.

[2] Ricci F, Rokach L, Shapira B. Introduction to recommender systems handbook. In Recommender Systems Handbook, Ricci F, Rokach L, Shapira B, Kantor P B (eds.), Springer, 2011, pp.1-35.

[3] Lops P, de Gemmis M, Semeraro G. Content-based recommender systems:State of the art and trends. In Recommender Systems Handbook, Ricci F, Rokach L, Shapira B, Kantor P B (eds.), Springer US, 2011, pp.73-105.

[4] Herlocker J L, Konstan J A, Terveen L G, Riedl J T. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 2004, 22(1):5-53.

[5] Burke R. Hybrid recommender systems:Survey and experiments. User Modeling and User-Adapted Interaction, 2002, 12(4):331-370.

[6] Adomavicius G, Tuzhilin A. Context-aware recommender systems. In Recommender Systems Handbook, Ricci F, Rokach L, Shapira B, Kantor P B (eds.), Springer, 2015, pp.191-226.

[7] Liu Q, Ma H, Chen E, Xiong H. A survey of context-aware mobile recommendations. International Journal of Information Technology & Decision Making, 2013, 12(1):139-172.

[8] Wu R, Liu Q, Liu Y, Chen E, Su Y, Chen Z, Hu G. Cognitive modelling for predicting examinee performance. In Proc. the 24th International Joint Conference on Artificial Intelligence, July 2015, pp.1017-1024.

[9] McAuley J, Pandey R, Leskovec J. Inferring networks of substitutable and complementary products. In Proc. the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2015, pp.785-794.

[10] He R, Packer C, McAuley J. Learning compatibility across categories for heterogeneous item recommendation. In Proc. the 16th IEEE International Conference on Data Mining, December 2016, pp.937-942.

[11] Xie M, Lakshmanan L V, Wood P T. Breaking out of the box of recommendations:From items to packages. In Proc. the 4th ACM Conference on Recommender Systems, September 2010, pp.151-158.

[12] Xie M, Lakshmanan L V, Wood P T. Comprec-trip:A composite recommendation system for travel planning. In Proc. the 27th IEEE International Conference on Data Engineering, April 2011, pp.1352-1355.

[13] Zhu T, Harrington P, Li J, Tang L. Bundle recommendation in ecommerce. In Proc. the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2014, pp.657-666.

[14] Kamishima T. Nantonac collaborative filtering:Recommendation based on order responses. In Proc. the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2003, pp.583-588.

[15] Rokach L, Kisilevich S. Initial profile generation in recommender systems using pairwise comparison. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2012, 42(6):1854-1859.

[16] Liu Q, Zeng X, Liu C, Zhu H, Chen E, Xiong H, Xie X. Mining indecisiveness in customer behaviors. In Proc. the IEEE International Conference on Data Mining, November 2015, pp.281-290.

[17] Wang P, Guo J, Lan Y, Xu J, Wan S, Cheng X. Learning hierarchical representation model for next basket recommendation. In Proc. the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, August 2015, pp.403-412.

[18] Figueiredo F, Ribeiro B, Almeida J M, Faloutsos C. TribeFlow:Mining and predicting user trajectories. In Proc. the 25th International Conference on World Wide Web, April 2016, pp.695-706.

[19] Wu X, Liu Q, Chen E, He L, Lv J, Cao C, Hu G. Personalized next-song recommendation in online karaokes. In Proc. the 7th ACM Conference on Recommender Systems, October 2013, pp.137-140.

[20] Parameswaran A, Venetis P, Garcia-Molina H. Recommendation systems with complex constraints:A course recommendation perspective. ACM Transactions on Information Systems, 2011, 29(4):Article No. 20.

[21] Chen J, Wang X, Wang C. Understanding item consumption orders for right-order next-item recommendation. Knowledge and Information Systems. doi:10.1007/s10115-017-1122-5.

[22] Dai J, Yang B, Guo C, Ding Z. Personalized route recommendation using big trajectory data. In Proc. the 31st IEEE International Conference on Data Engineering, April 2015, pp.543-554.

[23] Zhao W X, Zhou N, Sun A, Wen J R, Han J, Chang E Y. A time-aware trajectory embedding model for nextlocation recommendation. Knowledge and Information Systems. doi:10.1007/s10115-017-1107-4.

[24] Rendle S, Freudenthaler C, Schmidt-Thieme L. Factorizing personalized Markov chains for next-basket recommendation. In Proc. the 19th International Conference on World Wide Web, April 2010, pp.811-820.

[25] Jannach D, Ludewig M. When recurrent neural networks meet the neighborhood for session-based recommendation. In Proc. the 11th ACM Conference on Recommender Systems, August 2017, pp.306-310.

[26] Loyola P, Liu C, Hirate Y. Modeling user session and intent with an attention-based ancoder-decoder architecture. In Proc. the 11th ACM Conference on Recommender Systems, August 2017, pp.147-151.

[27] Zhong W, Jin R, Yang C, Yan X, Zhang Q, Li Q. Stock constrained recommendation in Tmall. In Proc. the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2015, pp.2287-2296.

[28] Zhao H, Liu Q, Wang G, Ge Y, Chen E. Portfolio selections in P2P lending:A multi-objective perspective. In Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016, pp.2075-2084.

[29] Yang D, Adamson D, Rosé C P. Question recommendation with constraints for massive open online courses. In Proc. the 8th ACM Conference on Recommender Systems, October 2014, pp.49-56.

[30] 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 Computing Surveys, 47(1):Article No. 3.

[31] Zhang S, Yao L, Sun A. Deep learning based recommender system:A survey and new perspectives. arXiv:1707.07435, 2017. https://arxiv.org/abs/1707.07435, May 2018.

[32] Liu Q, Ge Y, Li Z, Chen E, Xiong H. Personalized travel package recommendation. In Proc. the 11th IEEE International Conference on Data Mining, December 2011, pp.407-416.

[33] Pazzani M J, Billsus D. Content-based recommendation systems. In The Adaptive Web, Brusilovsky P, Kobsa A, Nejdl W (eds.), Springer-Verlag, 2007, pp.325-341.

[34] Manzato M G, Domingues M A, Marcacini R M, Rezende S O. Improving personalized ranking in recommender systems with topic hierarchies and implicit feedback. In Proc. the 22nd International Conference on Pattern Recognition, August 2014, pp.3696-3701.

[35] Wang H, Wang N, Yeung D Y. Collaborative deep learning for recommender systems. In Proc. the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2015, pp.1235-1244.

[36] Schein A I, Popescul A, Ungar L H, Pennock D M. Methods and metrics for cold-start recommendations. In Proc. the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, August 2002, pp.253-260.

[37] Zuva T, Olugbara O O, Ojo S O, Ngwira S M. Image content in location-based shopping recommender systems for mobile users. Advanced Computing:An International Journal, 2012, 3(4):1-8.

[38] Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. In Proc. the 10th International Conference on World Wide Web, May 2001, pp.285-295.

[39] Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L. BPR:Bayesian personalized ranking from implicit feedback. In Proc. the 25th Conference on Uncertainty in Artificial Intelligence, June 2009, pp.452-461.

[40] Huang Z, Chung W, Ong T H, Chen H. A graph-based recommender system for digital library. In Proc. the 2nd ACM/IEEE-CS Joint Conference on Digital Libraries, July 2002, pp.65-73.

[41] Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer, 2009, 42(8):30-37.

[42] Wu L, Chen E, Liu Q, Xu L, Bao T, Zhang L. Leveraging tagging for neighborhood-aware probabilistic matrix factorization. In Proc. the 21st ACM International Conference on Information and Knowledge Management, October 2012, pp.1854-1858.

[43] Wu L, Ge Y, Liu Q, Chen E, Hong R, Du J, Wang M. Modeling the evolution of users preferences and social links in social networking services. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(6):1240-1253.

[44] Zhang H, Zhao H, Liu Q, Xu T, Chen E, Huang X. Finding potential lenders in P2P lending:A hybrid random walk approach. Information Sciences, 2018, 432:376-391.

[45] Wu L, Liu Q, Chen E, Yuan N J, Guo G, Xie X. Relevance meets coverage:A unified framework to generate diversified recommendations. ACM Transactions on Intelligent Systems and Technology, 2016, 7(3):Article No. 39.

[46] Adomavicius G, Kwon Y. Improving aggregate recommendation diversity using ranking-based techniques. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(5):896-911.

[47] Mas-Colell A, Whinston M D, Green J R. Microeconomic Theory (1st edition). Oxford University Press, 1995.

[48] Jiang S, Qian X, Mei T, Fu Y. Personalized travel sequence recommendation on multi-source big social media. IEEE Transactions on Big Data, 2016, 2(1):43-56.

[49] Kurashima T, Iwata T, Irie G, Fujimura K. Travel route recommendation using geotags in photo sharing sites. In Proc. the 19th ACM International Conference on Information and Knowledge Management, October 2010, pp.579-588.

[50] Liu Q, Chen E, Xiong H, Ge Y, Li Z, Wu X. A cocktail approach for travel package recommendation. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(2):278-293.

[51] Cao D, Nie L, He X, Wei X, Zhu S, Chua T S. Embedding factorization models for jointly recommending items and user generated lists. In Proc. the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, August 2017, pp.585-594.

[52] Pathak A, Gupta K, McAuley J. Generating and personalizing bundle recommendations on Steam. In Proc. the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, August 2017, pp.1073-1076.

[53] Zhao H, Wu L, Liu Q, Ge Y, Chen E. Investment recommendation in P2P lending:A portfolio perspective with risk management. In Proc. IEEE International Conference on Data Mining, December 2014, pp.1109-1114.

[54] Yu F, Liu Q, Wu S, Wang L, Tan T. A dynamic recurrent model for next basket recommendation. In Proc. the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2016, pp.729-732.

[55] Hidasi B, Karatzoglou A, Baltrunas L, Tikk D. Sessionbased recommendations with recurrent neural networks. arXiv:1511.06939, 2016. https://arxiv.org/abs/1511.06939, May. 2018.

[56] Bao J, Zheng Y, Wilkie D, Mokbel M. Recommendations in location-based social networks:A survey. GeoInformatica, 2015, 19(3):525-565.

[57] Felfernig A, Gordea S, Jannach D, Teppan E, Zanker M. A short survey of recommendation technologies in travel and tourism. Oesterreichische Gesellschaft fuer Artificial Intelligence Journal, 2007, 25(7):17-22.

[58] Feng S, Li X, Zeng Y, Cong G, Chee Y M, Yuan Q. Personalized ranking metric embedding for next new POI recommendation. In Proc. the 24th International Joint Conference on Artificial Intelligence, July 2015, pp.2069-2075.

[59] Liu Y, Liu C, Liu B, Qu M, Xiong H. Unified pointof-interest recommendation with temporal interval assessment. In Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016, pp.1015-1024.

[60] Yao Z, Fu Y, Liu B, Liu Y, Xiong H. POI recommendation:A temporal matching between POI popularity and user regularity. In Proc. the 16th IEEE International Conference on Data Mining, December 2016, pp.549-558.

[61] Zhao P, Xu X, Liu Y, Zhou Z, Zheng K, Sheng V S, Xiong H. Exploiting hierarchical structures for POI recommendation. In Proc. the IEEE International Conference on Data Mining, November 2017, pp.655-664.

[62] Zheng Y. Trajectory data mining:An overview. ACM Transactions on Intelligent Systems and Technology, 2015, 6(3):Article No. 29.

[63] Figueiredo F, Ribeiro B, Faloutsos C, Andrade N, Almeida J M. Mining online music listening trajectories. In Proc. the 17th International Society for Music Information Retrieval Conference, August 2016, pp.688-694.

[64] Hariri N, Mobasher B, Burke R. Context-aware music recommendation based on latent topic sequential patterns. In Proc. the 6th ACM Conference on Recommender Systems, September 2012, pp.131-138.

[65] Xu J, Xing T, van der Schaar M. Personalized course sequence recommendations. IEEE Transactions on Signal Processing, 2016, 64(20):5340-5352.

[66] Agrawal R, Imieliński T, Swami A. Mining association rules between sets of items in large databases. In Proc. the ACM SIGMOD International Conference on Management of Data, May 1993, pp.207-216.

[67] Parameswaran A G, Garcia-Molina H. Recommendations with prerequisites. In Proc. the 3rd ACM Conference on Recommender Systems, October 2009, pp.353-356.

[68] Parameswaran A G, Garcia-Molina H, Ullman J D. Evaluating, combining and generalizing recommendations with prerequisites. In Proc. the 19th ACM International Conference on Information and Knowledge Management, October 2010, pp.919-928.

[69] Birtolo C, de Chiara D, Losito S, Ritrovato P, Veniero M. Searching optimal product bundles by means of GA-based engine and market basket analysis. In Proc. the Joint IFSA World Congress and NAFIPS Annual Meeting, June 2013, pp.448-453.

[70] Le D T, Lauw H W, Fang Y. Basket-sensitive personalized item recommendation. In Proc. the 26th International Joint Conference on Artificial Intelligence, August 2017, pp.2060-2066.

[71] Bakos Y, Brynjolfsson E. Bundling information goods:Pricing, profits, and efficiency. Management Science, 1999, 45(12):1613-1630.

[72] Stremersch S, Tellis G J. Strategic bundling of products and prices:A new synthesis for marketing. Journal of Marketing, 2002, 66(1):55-72.

[73] Liu G, Fu Y, Chen G, Xiong H, Chen C. Modeling buying motives for personalized product bundle recommendation. ACM Transactions on Knowledge Discovery from Data, 2017, 11(3):Article No. 28.

[74] Liu Y, Xie M, Lakshmanan L V. Recommending user generated item lists. In Proc. the 8th ACM Conference on Recommender Systems, October 2014, pp.185-192.

[75] Beladev M, Rokach L, Shapira B. Recommender systems for product bundling. Knowledge-Based Systems, 2016, 111:193-206.

[76] Qi S, Mamoulis N, Pitoura E, Tsaparas P. Recommending packages to groups. In Proc. the 16th IEEE International Conference on Data Mining, December 2016, pp.449-458.

[77] Qi S, Mamoulis N, Pitoura E, Tsaparas P. Recommending packages with validity constraints to groups of users. Knowledge and Information Systems, 2018, 54(2):345-374.

[78] Serbos D, Qi S, Mamoulis N, Pitoura E, Tsaparas P. Fairness in package-to-group recommendations. In Proc. the 26th International Conference on World Wide Web, April 2017, pp.371-379.

[79] Balakrishnan S, Chopra S. Two of a kind or the ratings game? Adaptive pairwise preferences and latent factor models. Frontiers of Computer Science, 2012, 6(2):197-208.

[80] Joachims T, Granka L, Pan B, Hembrooke H, Radlinski F, Gay G. Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search. ACM Transactions on Information Systems, 2007, 25(2):1-27.

[81] Rendle S, Freudenthaler C. Improving pairwise learning for item recommendation from implicit feedback. In Proc. the 7th ACM International Conference on Web Search and Data Mining, February 2014, pp.273-282.

[82] Garfinkel R, Gopal R, Pathak B, Yin F. Shopbot 2.0:Integrating recommendations and promotions with comparison shopping. Decision Support Systems, 2008, 46(1):61-69.

[83] Wan Y, Menon S, Ramaprasad A. A classification of product comparison agents. Communications of the ACM, 2007, 50(8):65-71.

[84] Yap G E, Li X L, Yu P. Effective next-items recommendation via personalized sequential pattern mining. In Proc. the 17th International Conference on Database Systems for Advanced Applications, April 2012, pp.48-64.

[85] Koenigstein N, Koren Y. Towards scalable and accurate item-oriented recommendations. In Proc. the 7th ACM Conference on Recommender Systems, October 2013, pp.419-422.

[86] Tan Y K, Xu X, Liu Y. Improved recurrent neural networks for session-based recommendations. In Proc. the 1st Workshop on Deep Learning for Recommender Systems, September 2016, pp.17-22.

[87] Greenstein-Messica A, Rokach L, Friedman M. Sessionbased recommendations using item embedding. In Proc. the 22nd International Conference on Intelligent User Interfaces, March 2017, pp.629-633.

[88] Tuan T X, Phuong T M. 3D convolutional networks for session-based recommendation with content features. In Proc. the 11th ACM Conference on Recommender Systems, August 2017, pp.138-146.

[89] Li J, Ren P, Chen Z, Ren Z, Lian T, Ma J. Neural attentive session-based recommendation. In Proc. the ACM Conference on Information and Knowledge Management, November 2017, pp.1419-1428.

[90] Lian D, Zhao C, Xie X, Sun G, Chen E, Rui Y. GeoMF:Joint geographical modeling and matrix factorization for point-of-interest recommendation. In Proc. the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2014, pp.831-840.

[91] Cheng C, Yang H, Lyu M R, King I. Where you like to go next:Successive point-of-interest recommendation. In Proc. the 23rd International Joint Conference on Artificial Intelligence, August 2013, pp.2605-2611.

[92] Ye M, Yin P, Lee W C, Lee D L. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proc. the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2011, pp.325-334.

[93] Shang S, Ding R, Yuan B, Xie K, Zheng K, Kalnis P. User oriented trajectory search for trip recommendation. In Proc. the 15th International Conference on Extending Database Technology, March 2012, pp.156-167.

[94] Yin H, Cui B, Sun Y, Hu Z, Chen L. LCARS:A spatial item recommender system. ACM Transactions on Information Systems, 2014, 32(3):Article No. 11.

[95] Yin H, Cui B, Chen L, Hu Z, Zhang C. Modeling locationbased user rating profiles for personalized recommendation. ACM Transactions on Knowledge Discovery from Data, 2015, 9(3):Article No. 19.

[96] Yin H, Wang W, Wang H, Chen L, Zhou X. Spatial-aware hierarchical collaborative deep learning for POI recommendation. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(11):2537-2551.

[97] Yin H, Cui B, Zhou X, Wang W, Huang Z, Sadiq S. Joint modeling of user check-in behaviors for real-time point-ofinterest recommendation. ACM Transactions on Information Systems, 2016, 35(2):Article No. 11.

[98] Wang W, Yin H, Chen L, Sun Y, Sadiq S, Zhou X. STSAGE:A spatial-temporal sparse additive generative model for spatial item recommendation. ACM Transactions on Intelligent Systems and Technology, 2017, 8(3):Article No. 48.

[99] Yin H, Zhou X, Cui B, Wang H, Zheng K, Nguyen Q V H. Adapting to user interest drift for POI recommendation. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(10):2566-2581.

[100] Chen S, Moore J L, Turnbull D, Joachims T. Playlist prediction via metric embedding. In Proc. the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2012, pp.714-722.

[101] Ji K, Sun R, Shu W, Li X. Next-song recommendation with temporal dynamics. Knowledge-Based Systems, 2015, 88:134-143.

[102] Cheng Z, Shen J, Zhu L, Kankanhalli M, Nie L. Exploiting music play sequence for music recommendation. In Proc. the 26th International Joint Conference on Artificial Intelligence, January 2017, pp.3654-3660.

[103] Wang D, Deng S, Xu G. Sequence-based context-aware music recommendation. Information Retrieval Journal, 2017, 21(2/3):230-252.

[104] Lan A S, Studer C, Baraniuk R G. Time-varying learning and content analytics via sparse factor analysis. In Proc. the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2014, pp.452-461.

[105] Chen Y, Liu Q, Huang Z, Wu L, Chen E, Wu R, Su Y, Hu G. Tracking knowledge proficiency of students with educational priors. In Proc. the 26th ACM International Conference on Information and Knowledge Management, November 2017, pp.989-998.

[106] Chen W, Niu Z, Zhao X, Li Y. A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web, 2014, 17(2):271-284.

[107] Reddy S, Labutov I, Joachims T. Latent skill embedding for personalized lesson sequence recommendation. arXiv:1602.07029, 2016. https://arxiv.org/abs/1602.07029, May 2018.

[108] Benouaret I, Lenne D. A package recommendation framework for trip planning activities. In Proc. the 10th ACM Conference on Recommender Systems, September 2016, pp.203-206.

[109] Shi F, Ghedira C, Marini J L. Context adaptation for smart recommender systems. IT Professional, 2015, 17(6):18-26.

[110] Anava O, Golan S, Golbandi N, Karnin Z, Lempel R, Rokhlenko O, Somekh O. Budget-constrained item coldstart handling in collaborative filtering recommenders via optimal design. In Proc. the 24th International Conference on World Wide Web, May 2015, pp.45-54.

[111] Quadrana M, Cremonesi P, Jannach D. Sequenceaware recommender systems. arXiv:1802.08452, 2018. https://arxiv.org/abs/1802.08452, May 2018.

[112] Sidana S, Laclau C, Amini M R, Vandelle G, Bois-Crettez A. KASANDR:A large-scale dataset with implicit feedback for recommendation. In Proc. the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, August 2017, pp.1245-1248.

[113] He R, McAuley J. VBPR:Visual Bayesian personalized ranking from implicit feedback. In Proc. the 30th AAAI Conference on Artificial Intelligence, February 2016, pp.144-150.

[114] Zhang F, Yuan N J, Lian D, Xie X, Ma W Y. Collaborative knowledge base embedding for recommender systems. In Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016, pp.353-362.
No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 周笛;. A Recovery Technique for Distributed Communicating Process Systems[J]. , 1986, 1(2): 34 -43 .
[2] 刘明业; 洪恩宇;. Some Covering Problems and Their Solutions in Automatic Logic Synthesis Systems[J]. , 1986, 1(2): 83 -92 .
[3] 金兰; 杨元元;. A Modified Version of Chordal Ring[J]. , 1986, 1(3): 15 -32 .
[4] 沈理; Stephen Y.H.Su;. Generalized Parallel Signature Analyzers with External Exclusive-OR Gates[J]. , 1986, 1(4): 49 -61 .
[5] 孟力明; 徐晓飞; 常会友; 陈光熙; 胡铭曾; 李生;. A Tree-Structured Database Machine for Large Relational Database Systems[J]. , 1987, 2(4): 265 -275 .
[6] 王建潮; 魏道政;. Reconvergent-Fanout-Oriented Testability Measure[J]. , 1988, 3(1): 16 -28 .
[7] 王能斌; 刘小青; 刘光富;. A Software Tool for Constructing Traditional Chinese Medical Expert Systems[J]. , 1988, 3(3): 214 -220 .
[8] 练林; 张一立; 唐常杰;. A Non-Recursive Algorithm Computing Set Expressions[J]. , 1988, 3(4): 310 -316 .
[9] 薛行; 孙钟秀; 周建强; 徐希豪;. A Message-Based Distributed Kernel for a Full Heterogeneous Environment[J]. , 1990, 5(1): 47 -56 .
[10] 闵应骅;. Guest Editor s Introduction:Fault-Tolerant Computing[J]. , 1990, 5(2): 3 -4 .
版权所有 © 《计算机科学技术学报》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn
总访问量: