Yifan Chen
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Abstract
Top-N recommendations have been studied extensively. Promising results have been achieved by recent item-based collaborative filtering (ICF) methods. The key to ICF lies in the estimation of item similarities. Observing the block-diagonal structure of the item similarities in practice, we propose a block-diagonal regularization (BDR) over item similarities for ICF. The intuitions behind BDR are as follows: (1) with BDR, item clustering is embedded into the learning of ICF methods; (2) BDR induces sparsity of item similarities, which guarantees recommendation efficiency; and (3) BDR captures in-block transitivity to overcome rating sparsity. By regularizing the item similarity matrix of item similarity models with BDR, we obtain a block-aware item similarity model. Our experimental evaluations on a large number of datasets show that the block-diagonal structure is crucial to the performance of top-N recommendation.
- Fabio Aiolli. 2013. A preliminary study on a recommender system for the Million Songs Dataset Challenge. In Proceedings of the 4th Italian Information Retrieval Workshop (IIR’13). 73--83. http://ceur-ws.org/Vol-964/paper12.pdf.Google Scholar
- Marie Al-Ghossein, Talel Abdessalem, and Anthony Barré. 2018. Dynamic local models for online recommendation. In Companion of the 27th World Wide Web Conference (WWW’18). 1419--1423. DOI:https://doi.org/10.1145/3184558.3191586Google ScholarDigital Library
- Immanuel Bayer, Xiangnan He, Bhargav Kanagal, and Steffen Rendle. 2017. A generic coordinate descent framework for learning from implicit feedback. In Proceedings of the 26th International Conference on World Wide Web (WWW’17). 1341--1350. DOI:https://doi.org/10.1145/3038912.3052694Google ScholarDigital Library
- Alex Beutel, Ed Huai-Hsin Chi, Zhiyuan Cheng, Hubert Pham, and John R. Anderson. 2017. Beyond globally optimal: Focused learning for improved recommendations. In Proceedings of the 26th International Conference on World Wide Web (WWW’17). 203--212. DOI:https://doi.org/10.1145/3038912.3052713Google Scholar
- Deng Cai, Xiaofei He, Xiaoyun Wu, and Jiawei Han. 2008. Non-negative matrix factorization on manifold. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM’08). IEEE, 63--72. DOI:https://doi.org/10.1109/ICDM.2008.57Google ScholarDigital Library
- Yifan Chen, Pengjie Ren, Yang Wang, and Maarten de Rijke. 2019. Bayesian personalized feature interaction selection for factorization machines. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19). ACM, New York, NY, 665--674. DOI:https://doi.org/10.1145/3331184.3331196Google ScholarDigital Library
- Yifan Chen, Yang Wang, Xiang Zhao, Hongzhi Yin, Ilya Markov, and Maarten de Rijke. 2020. Local variational feature-based similarity models for recommending top-N new items. ACM Transactions on Information Systems 38, 2 (2020), Article 12, 33 pages. DOI:https://doi.org/10.1145/3372154Google ScholarDigital Library
- Yifan Chen, Xiang Zhao, and Maarten de Rijke. 2017. Top-N recommendation with high-dimensional side information via locality preserving projection. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’17). ACM, New York, NY, 985--988. DOI:https://doi.org/10.1145/3077136.3080697Google ScholarDigital Library
- Yifan Chen, Xiang Zhao, Xuemin Lin, Yang Wang, and Deke Guo. 2019. Efficient mining of frequent patterns on uncertain graphs. IEEE Transactions on Knowledge and Data Engineering 31, 2 (2019), 287--300. DOI:https://doi.org/10.1109/TKDE.2018.2830336Google ScholarDigital Library
- Yao Cheng, Li’ang Yin, and Yong Yu. 2014. LorSLIM: Low rank sparse linear methods for top-N recommendations. In Proceedings of the 14th IEEE International Conference on Data Mining (ICDM’14). IEEE, Los Alamitos, CA, 90--99. DOI:https://doi.org/10.1109/ICDM.2014.112Google ScholarDigital Library
- Evangelia Christakopoulou and George Karypis. 2016. Local item-item models for top-N recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys’16). ACM, New York, NY, USA, 67--74. DOI:https://doi.org/10.1145/2959100.2959185Google ScholarDigital Library
- Evangelia Christakopoulou and George Karypis. 2018. Local latent space models for top-N recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD’18). ACM, New York, NY, 1235--1243. DOI:https://doi.org/10.1145/3219819.3220112Google ScholarDigital Library
- Fan R. K. Chung and Fan Chung Graham. 1997. Spectral Graph Theory. Number 92. American Mathematical Society.Google Scholar
- Andrzej Cichocki, Rafal Zdunek, Anh Huy Phan, and Shun-Ichi Amari. 2009. Nonnegative Matrix and Tensor Factorizations—Applications to Exploratory Multi-Way Data Analysis and Blind Source Separation. Wiley.Google ScholarDigital Library
- Paolo Cremonesi, Yehuda Koren, and Roberto Turrin. 2010. Performance of recommender algorithms on top-N recommendation tasks. In Proceedings of the 4th ACM Conference on Recommender Systems (RecSys’10). ACM, New York, NY, 39--46. DOI:https://doi.org/10.1145/1864708.1864721Google ScholarDigital Library
- Maurizio Ferrari Dacrema, Paolo Cremonesi, and Dietmar Jannach. 2019. Are we really making much progress? A worrying analysis of recent neural recommendation approaches. In Proceedings of the 13th ACM Conference on Recommender Systems (RecSys’17). 101--109. DOI:https://doi.org/10.1145/3298689.3347058Google ScholarDigital Library
- Mukund Deshpande and George Karypis. 2004. Item-based top-N recommendation algorithms. ACM Transactions on Information Systems 22, 1 (Jan. 2004), 143--177. DOI:https://doi.org/10.1145/963770.963776Google ScholarDigital Library
- Travis Ebesu, Bin Shen, and Yi Fang. 2018. Collaborative memory network for recommendation systems. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’18). 515--524. DOI:https://doi.org/10.1145/3209978.3209991Google ScholarDigital Library
- Ky Fan. 1949. On a theorem of Weyl concerning eigenvalues of linear transformations I. Proceedings of the National Academy of Sciences 35, 11 (1949), 652--655.Google ScholarCross Ref
- Jiashi Feng, Zhouchen Lin, Huan Xu, and Shuicheng Yan. 2014. Robust subspace segmentation with block-diagonal prior. In Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR’14). IEEE, Los Alamitos, CA, 3818--3825. DOI:https://doi.org/10.1109/CVPR.2014.482Google ScholarDigital Library
- Xue Geng, Hanwang Zhang, Jingwen Bian, and Tat-Seng Chua. 2015. Learning image and user features for recommendation in social networks. In Proceedings of the 2015 International Conference on Computer Vision (ICCV’15). IEEE, Los Alamitos, CA, 4274--4282.Google ScholarDigital Library
- Thomas George and Srujana Merugu. 2005. A scalable collaborative filtering framework based on co-clustering. In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM’05). IEEE, Los Alamitos, CA, 625--628. DOI:https://doi.org/10.1109/ICDM.2005.14Google ScholarDigital Library
- Luigi Grippo and Marco Sciandrone. 2000. On the convergence of the block nonlinear Gauss–Seidel method under convex constraints. Operations Research Letters 26, 3 (2000), 127--136.Google ScholarDigital Library
- Guibing Guo, Jie Zhang, Zhu Sun, and Neil Yorke-Smith. 2015. LibRec: A Java library for recommender systems. In Proceedings of the 23rd Conference on User Modelling, Adaptation, and Personalization (UMAP’15), Vol. 1388.Google Scholar
- Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, and Tat-Seng Chua. 2018. NAIS: Neural attentive item similarity model for recommendation. IEEE Transactions on Knowledge and Data Engineering 30, 12 (2018), 2354--2366.Google ScholarDigital Library
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW’17). 173--182. DOI:https://doi.org/10.1145/3038912.3052569Google ScholarDigital Library
- Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. 2016. Fast matrix factorization for online recommendation with implicit feedback. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’16). ACM, New York, NY, 549--558. DOI:https://doi.org/10.1145/2911451.2911489Google ScholarDigital Library
- Binbin Hu, Chuan Shi, Wayne Xin Zhao, and Philip S. Yu. 2018. Leveraging meta-path based context for top-N recommendation with a neural co-attention model. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD’18). ACM, New York, NY, 1531--1540. DOI:https://doi.org/10.1145/3219819.3219965Google Scholar
- Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM’08). IEEE, Los Alamitos, CA, 263--272. DOI:https://doi.org/10.1109/ICDM.2008.22Google ScholarDigital Library
- Santosh Kabbur, Xia Ning, and George Karypis. 2013. FISM: Factored item similarity models for top-N recommender systems. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD’13). ACM, New York, NY, 659--667. DOI:https://doi.org/10.1145/2487575.2487589Google ScholarDigital Library
- Zhao Kang and Qiang Cheng. 2016. Top-N recommendation with novel rank approximation. In Proceedings of the 2016 SIAM International Conference on Data Mining (SDM’16). 126--134. DOI:https://doi.org/10.1137/1.9781611974348.15Google ScholarCross Ref
- Zhao Kang, Chong Peng, Ming Yang, and Qiang Cheng. 2016. Top-N recommendation on graphs. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM’16). ACM, New York, NY, 2101--2106. DOI:https://doi.org/10.1145/2983323.2983649Google ScholarDigital Library
- Yehuda Koren. 2008. Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD’08). ACM, New York, NY, 426--434. DOI:https://doi.org/10.1145/1401890.1401944Google ScholarDigital Library
- Daniel D. Lee and H. Sebastian Seung. 2001. Algorithms for non-negative matrix factorization. In Advances in Neural Information Processing Systems 13, T. K. Leen, T. G. Dietterich, and V. Tresp (Eds.). MIT Press, Cambridge, MA, 556–562. http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.Google Scholar
- Joonseok Lee, Samy Bengio, Seungyeon Kim, Guy Lebanon, and Yoram Singer. 2014. Local collaborative ranking. In Proceedings of the 23rd International World Wide Web Conference (WWW’14). 85--96. DOI:https://doi.org/10.1145/2566486.2567970Google ScholarDigital Library
- Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer, and Samy Bengio. 2016. LLORMA: Local low-rank matrix approximation. Journal of Machine Learning Research 17 (2016), Article 15, 24 pages.Google Scholar
- Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In Proceedings of the 27th World Wide Web Conference (WWW’18). 689--698. DOI:https://doi.org/10.1145/3178876.3186150Google ScholarDigital Library
- C. Lu, J. Feng, Z. Lin, T. Mei, and S. Yan. 2019. Subspace clustering by block diagonal representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 41, 2 (2019), 487--501. DOI:https://doi.org/10.1109/TPAMI.2018.2794348Google ScholarDigital Library
- Can-Yi Lu, Hai Min, Zhong-Qiu Zhao, Lin Zhu, De-Shuang Huang, and Shuicheng Yan. 2012. Robust and efficient subspace segmentation via least squares regression. In Proceeding of the 12th European Conference on Computer Vision (ECCV’12). 347--360. DOI:https://doi.org/10.1007/978-3-642-33786-4_26Google ScholarDigital Library
- Julian J. McAuley and Jure Leskovec. 2013. Hidden factors and hidden topics: Understanding rating dimensions with review text. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys’13). ACM, New York, NY, 165--172. DOI:https://doi.org/10.1145/2507157.2507163Google Scholar
- Bojan Mohar. 1991. The Laplacian spectrum of graphs. In Graph Theory, Combinatorics, and Applications. Vol. 2. Wiley, 871--898.Google Scholar
- Xia Ning and George Karypis. 2011. SLIM: Sparse linear methods for top-N recommender systems. In Proceedings of the 11th IEEE International Conference on Data Mining (ICDM’11). IEEE, Los Alamitos, CA, 497--506. DOI:https://doi.org/10.1109/ICDM.2011.134Google ScholarDigital Library
- Xia Ning and George Karypis. 2012. Sparse linear methods with side information for top-N recommendations. In Proceedings of the 6th ACM Conference on Recommender Systems (RecSys’12). ACM, New York, NY, 155--162. DOI:https://doi.org/10.1145/2365952.2365983Google ScholarDigital Library
- Mark O’Connor and Jon Herlocker. 1999. Clustering items for collaborative filtering. In Proceedings of the SIGIR Workshop on Recommender Systems. ACM, New York, NY.Google Scholar
- Steffen Rendle. 2019. Evaluation metrics for item recommendation under sampling. arXiv:1912.02263Google Scholar
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI’09). 452--461.Google ScholarDigital Library
- Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.). 2015. Recommender Systems Handbook. Springer.Google Scholar
- Badrul Munir Sarwar, George Karypis, Joseph A. Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International World Wide Web Conference (WWW’10). 285--295. DOI:https://doi.org/10.1145/371920.372071Google ScholarDigital Library
- Harald Steck. 2019. Embarrassingly shallow autoencoders for sparse data. In Proceedings of the 28th World Wide Web Conference (WWW’19). 3251--3257. DOI:https://doi.org/10.1145/3308558.3313710Google ScholarDigital Library
- Keqiang Wang, Wayne Xin Zhao, Hongwei Peng, and Xiaoling Wang. 2016. Bayesian probabilistic multi-topic matrix factorization for rating prediction. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI’16). 3910--3916. MISSINGGoogle ScholarDigital Library
- Yang Wang, Xuemin Lin, Lin Wu, and Wenjie Zhang. 2017. Effective multi-query expansions: Collaborative deep networks for robust landmark retrieval. IEEE Transactions on Image Processing 26, 3 (2017), 1393--1404. DOI:https://doi.org/10.1109/TIP.2017.2655449Google ScholarDigital Library
- Zengmao Wang, Yuhong Guo, and Bo Du. 2018. Matrix completion with preference ranking for top-N recommendation. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18). 3585--3591. DOI:https://doi.org/10.24963/ijcai.2018/498Google ScholarDigital Library
- Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-N recommender systems. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining (WSDM’16). ACM, New York, NY, 153--162. DOI:https://doi.org/10.1145/2835776.2835837Google ScholarDigital Library
- Yao Wu, Xudong Liu, Min Xie, Martin Ester, and Qing Yang. 2016. CCCF: Improving collaborative filtering via scalable user-item co-clustering. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining (WSDM’16). ACM, New York, NY, 73--82. DOI:https://doi.org/10.1145/2835776.2835836Google ScholarDigital Library
- Xingyu Xie, Xianglin Guo, Guangcan Liu, and Jun Wang. 2018. Implicit block diagonal low-rank representation. IEEE Transactions on Image Processing 27, 1 (2018), 477--489. DOI:https://doi.org/10.1109/TIP.2017.2764262Google ScholarCross Ref
- Bin Xu, Jiajun Bu, Chun Chen, and Deng Cai. 2012. An exploration of improving collaborative recommender systems via user-item subgroups. In Proceedings of the 21st World Wide Web Conference (WWW’12). 21--30. DOI:https://doi.org/10.1145/2187836.2187840Google ScholarDigital Library
- Feng Xue, Xiangnan He, Xiang Wang, Jiandong Xu, Kai Liu, and Richang Hong. 2019. Deep item-based collaborative filtering for top-N recommendation. ACM Transactions on Information Systems 37, 3 (2019), Article 33, 25 pages. DOI:https://doi.org/10.1145/3314578Google ScholarDigital Library
- Gui-Rong Xue, Chenxi Lin, Qiang Yang, Wensi Xi, Hua-Jun Zeng, Yong Yu, and Zheng Chen. 2005. Scalable collaborative filtering using cluster-based smoothing. In Proceedings of the 28th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’05). ACM, New York, NY, 114--121. DOI:https://doi.org/10.1145/1076034.1076056Google ScholarDigital Library
- Hilmi Yildirim and Mukkai S. Krishnamoorthy. 2008. A random walk method for alleviating the sparsity problem in collaborative filtering. In Proceedings of the 2nd ACM Conference on Recommender Systems (RecSys’08). ACM, New York, NY, 131--138. DOI:https://doi.org/10.1145/1454008.1454031Google Scholar
- Yongfeng Zhang, Min Zhang, Yiqun Liu, Shaoping Ma, and Shi Feng. 2013. Localized matrix factorization for recommendation based on matrix block diagonal forms. In Proceedings of the 22nd International World Wide Web Conference (WWW’13). 1511--1520. DOI:https://doi.org/10.1145/2488388.2488520Google ScholarDigital Library
- Feipeng Zhao and Yuhong Guo. 2016. Improving top-N recommendation with heterogeneous loss. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI’16). 2378--2384. http://www.ijcai.org/Abstract/16/339.Google Scholar
- Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, and Georg Lausen. 2005. Improving recommendation lists through topic diversification. In Proceedings of the 14th International Conference on World Wide Web (WWW’05). 22--32. DOI:https://doi.org/10.1145/1060745.1060754Google ScholarDigital Library
Index Terms
- Block-Aware Item Similarity Models for Top-N Recommendation
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