Optimizing Factorization Machines for Top-N Context-Aware Recommendations
详细信息 查看全文
- 关键词：Context ; aware ; Learning to rank ; Factorization machines ; RankingFM ; LambdaFM
- 刊名：Lecture Notes in Computer Science
- 出版年：2016
- 出版时间：2016
- 年：2016
- 卷：10041
- 期：1
- 页码：278-293
- 全文大小：586 KB
- 参考文献：1.Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.: Learning to rank using gradient descent. In: ICML, pp. 89–96 (2005)
2.Burges, C.J.: From ranknet to lambdarank to lambdamart: An overview
3.Cao, Z., Qin, T., Liu, T., Tsai, M., Li, H.: Learning to rank: from pairwise approach to listwise approach. In: ICML, pp. 129–136 (2007)
4.Chen, W., Liu, T.-Y., Lan, Y., Ma, Z.-M., Li, H.: Ranking measures and loss functions in learning to rank. In: NIPS, pp. 315–323 (2009)
5.Christakopoulou, K., Banerjee, A.: Collaborative ranking with a push at the top. In: WWW, pp. 205–215 (2015)
6.Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: RecSys, pp. 39–46 (2010)
7.Donmez, P., Svore, K.M., Burges, C.J.: On the local optimality of lambdarank. In: SIGIR, pp. 460–467 (2009)
8.Herbrich, R., Graepel, T., Obermayer, K.: Support vector learning for ordinal regression (1999)
9.Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM, pp. 263–272 (2008)
10.Karatzoglou, A., Amatriain, X., Baltrunas, L., Oliver, N.: Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In: RecSys, pp. 79–86 (2010)
11.Koren, Y.: Collaborative filtering with temporal dynamics, pp. 89–97 (2010)
12.Li, X., Cong, G., Li, X.-L., Pham, T.-AN., Krishnaswamy, S.: Rank-GeoFM: a ranking based geographical factorization method for point of interest recommendation. In: SIGIR, pp. 433–442 (2015)
13.Tsai, M., Liu, T., Qin, T., Chen, H., Ma, W.: Frank: a ranking method with fidelity loss. In: SIGIR, pp. 383–390 (2007)
14.Pan, R., Zhou, Y., Cao, B., Liu, N.N., Lukose, R., Scholz, M., Yang, Q.: One-class collaborative filtering. In: ICDM, pp. 502–511 (2008)
15.Quoc, C., Le, V.: Learning to rank with nonsmooth cost functions. In: Advances in Neural Information Processing Systems 19, pp. 193–200 (2007)
16.Rendle, S.: Factorization machines. In: ICDM, pp. 995–1000 (2010)
17.Rendle, S.: Factorization machines with libFM. TIST 3, 57:1–57:22 (2012)CrossRef
18.Rendle, S., Balby Marinho, L., Nanopoulos, A., Schmidt-Thieme, L.: Learning optimal ranking with tensor factorization for tag recommendation. In: SIGKDD, pp. 727–736 (2009)
19.Rendle, S., Freudenthaler, C.: Improving pairwise learning for item recommendation from implicit feedback. In: WSDM, pp. 273–282 (2014)
20.Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: UAI, pp. 452–461 (2009)
21.Rendle, S., Gantner, Z., Freudenthaler, C., Schmidt-Thieme, L.: Fast context-aware recommendations with factorization machines. In: SIGIR, pp. 635–644 (2011)
22.Rendle, S., Schmidt-Thieme, L.: Pairwise interaction tensor factorization for personalized tag recommendation. In: WSDM, pp. 81–90 (2010)
23.Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS 20, pp. 1257–1264 (2008)
24.Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Hanjalic, A., Oliver, N.: TFMAP: optimizing map for top-n context-aware recommendation. In: SIGIR, pp. 155–164 (2012)
25.Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Oliver, N., Hanjalic, A.: CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering. In: RecSys, pp. 139–146 (2012)
26.Yuan, F., Guo, G., Jose, J., Chen, L., Yu, H., Zhang, W.: Lambdafm: learning optimal ranking with factorization machines using lambda surrogates. In: CIKM (2016)
27.Zhang, W., Chen, T., Wang, J., Yu, Y.: Optimizing top-n collaborative filtering via dynamic negative item sampling. In: SIGIR, pp. 785–788 (2013)
28.Zhong, H., Pan, W., Xu, C., Yin, Z., Ming, Z.: Adaptive pairwise preference learning for collaborative recommendation with implicit feedbacks. In: CIKM, pp. 1999–2002 (2014) - 作者单位：Fajie Yuan (19)
Guibing Guo (20)
Joemon M. Jose (19)
Long Chen (19)
Haitao Yu (21)
Weinan Zhang (22)
19. University of Glasgow, Glasgow, UK
20. Northeastern University, Shenyang, China
21. University of Tsukuba, Tsukuba, Japan
22. Shanghai Jiao Tong University, Shanghai, China - 丛书名：Web Information Systems Engineering ¨C WISE 2016
- ISBN：978-3-319-48740-3
- 刊物类别：Computer Science
- 刊物主题：Artificial Intelligence and Robotics
Computer Communication Networks
Software Engineering
Data Encryption
Database Management
Computation by Abstract Devices
Algorithm Analysis and Problem Complexity - 出版者：Springer Berlin / Heidelberg
- ISSN：1611-3349
- 卷排序：10041
文摘
Context-aware Collaborative Filtering (CF) techniques such as Factorization Machines (FM) have been proven to yield high precision for rating prediction. However, the goal of recommender systems is often referred to as a top-N item recommendation task, and item ranking is a better formulation for the recommendation problem. In this paper, we present two collaborative rankers, namely, Ranking Factorization Machines (RankingFM) and Lambda Factorization Machines (LambdaFM), which optimize the FM model for the item recommendation task. Specifically, instead of fitting the preference of individual items, we first propose a RankingFM algorithm that applies the cross-entropy loss function to the FM model to estimate the pairwise preference between individual item pairs. Second, by considering the ranking bias in the item recommendation task, we design two effective lambda-motivated learning schemes for RankingFM to optimize desired ranking metrics, referred to as LambdaFM. The two models we propose can work with any types of context, and are capable of estimating latent interactions between the context features under sparsity. Experimental results show its superiority over several state-of-the-art methods on three public CF datasets in terms of two standard ranking metrics.