Abstract
Item-based models are among the most popular collaborative filtering approaches for building recommender systems. Random walks can provide a powerful tool for harvesting the rich network of interactions captured within these models. They can exploit indirect relations between the items, mitigate the effects of sparsity, ensure wider itemspace coverage, as well as increase the diversity of recommendation lists. Their potential however, can be hindered by the tendency of the walks to rapidly concentrate towards the central nodes of the graph, thereby significantly restricting the range of K-step distributions that can be exploited for personalized recommendations. In this work, we introduce RecWalk; a novel random walk-based method that leverages the spectral properties of nearly uncoupled Markov chains to provably lift this limitation and prolong the influence of users’ past preferences on the successive steps of the walk—thereby allowing the walker to explore the underlying network more fruitfully. A comprehensive set of experiments on real-world datasets verify the theoretically predicted properties of the proposed approach and indicate that they are directly linked to significant improvements in top-n recommendation accuracy. They also highlight RecWalk’s potential in providing a framework for boosting the performance of item-based models. RecWalk achieves state-of-the-art top-n recommendation quality outperforming several competing approaches, including recently proposed methods that rely on deep neural networks.
- Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge 8 Data Engineering 17, 6 (2005), 734--749.Google ScholarDigital Library
- Albert Ando, Franklin M. Fisher, and Herbert Alexander Simon. 1963. Essays on the Structure of Social Science Models. Mit Press Cambridge.Google Scholar
- D. Berberidis, A. N. Nikolakopoulos, and G. B. Giannakis. 2019. Adaptive diffusions for scalable learning over graphs. IEEE Transactions on Signal Processing 67, 5 (March 2019), 1307--1321. DOI:https://doi.org/10.1109/TSP.2018.2889984Google ScholarDigital Library
- A. Cevahir, C. Aykanat, A. Turk, and B. B. Cambazoglu. 2011. Site-based partitioning and repartitioning techniques for parallel PageRank computation. IEEE Transactions on Parallel and Distributed Systems 22, 5 (2011), 786--802.Google ScholarDigital Library
- Fabian Christoffel, Bibek Paudel, Chris Newell, and Abraham Bernstein. 2015. Blockbusters and wallflowers: Accurate, diverse, and scalable recommendations with random walks. In Proceedings of the 9th ACM Conference on Recommender Systems. ACM, 163--170.Google ScholarDigital Library
- Colin Cooper, Sang Hyuk Lee, Tomasz Radzik, and Yiannis Siantos. 2014. Random walks in recommender systems: Exact computation and simulations. In Proceedings of the 23rd International Conference on World Wide Web (WWW’14). ACM, New York, NY, 811--816. DOI:https://doi.org/10.1145/2567948.2579244Google ScholarDigital Library
- Pierre-Jacque Courtois. 1977. Decomposability: Queueing and Computer System Applications. Academic Press. Retrieved from http://books.google.gr/books?id=jD5RAAAAMAAJ.Google Scholar
- 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, 39--46. DOI:https://doi.org/10.1145/1864708.1864721Google 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
- Chantat Eksombatchai, Pranav Jindal, Jerry Zitao Liu, Yuchen Liu, Rahul Sharma, Charles Sugnet, Mark Ulrich, and Jure Leskovec. 2018. Pixie: A system for recommending 3+ Billion items to 200+ Million users in real-time. In Proceedings of the 2018 World Wide Web Conference (WWW’18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 1775--1784. DOI:https://doi.org/10.1145/3178876.3186183Google ScholarDigital Library
- Guang Feng, Tie-Yan Liu, Ying Wang, Ying Bao, Zhiming Ma, Xu-Dong Zhang, and Wei-Ying Ma. 2006. AggregateRank: Bringing order to web sites. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’06). ACM, New York, NY, 75--82. DOI:https://doi.org/10.1145/1148170.1148187Google ScholarDigital Library
- François Fouss, Kevin Francoisse, Luh Yen, Alain Pirotte, and Marco Saerens. 2012. An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification. Neural Networks 31 (2012), 53--72. DOI:https://doi.org/10.1016/j.neunet.2012.03.001Google ScholarDigital Library
- Ashish Goel, Pankaj Gupta, John Sirois, Dong Wang, Aneesh Sharma, and Siva Gurumurthy. 2015. The who-to-follow system at Twitter: Strategy, algorithms, and revenue impact. Interfaces 45, 1 (Feb. 2015), 98--107. DOI:https://doi.org/10.1287/inte.2014.0784Google Scholar
- Geoffrey Grimmett and David Stirzaker. 2001. Probability and Random Processes. Oxford University Press.Google Scholar
- Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In Proceedings of the 25th International Conference on World Wide Web (WWW’16). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 507--517. DOI:https://doi.org/10.1145/2872427.2883037Google ScholarDigital Library
- Xiangnan He, Zhankui He, Xiaoyu Du, and Tat-Seng Chua. 2018. Adversarial personalized ranking for recommendation. In Proceedings of the 41st International ACM SIGIR Conference on Research 8 Development in Information Retrieval (SIGIR’18). ACM, New York, NY, 355--364. DOI:https://doi.org/10.1145/3209978.3209981Google ScholarDigital Library
- X. He, Z. He, J. Song, Z. Liu, Y. Jiang, and T. Chua. 2018. NAIS: Neural attentive item similarity model for recommendation. IEEE Transactions on Knowledge and Data Engineering 30, 12 (Dec 2018), 2354--2366. DOI:https://doi.org/10.1109/TKDE.2018.2831682Google 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). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 173--182. DOI:https://doi.org/10.1145/3038912.3052569Google ScholarDigital Library
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).Google 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. IEEE, 263--272.Google 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 (KDD’13). ACM, New York, NY, 659--667. DOI:https://doi.org/10.1145/2487575.2487589Google ScholarDigital Library
- Sepandar D. Kamvar, Taher H. Haveliwala, Christopher D. Manning, and Gene H. Golub. 2003. Exploiting the block structure of the web for computing PageRank. In Stanford University, Technical Report.Google Scholar
- Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM’18). IEEE, 197--206.Google ScholarCross Ref
- Zhao Kang, Chong Peng, Ming Yang, and Qiang Cheng. 2016. Top-n recommendation on graphs. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 2101--2106.Google ScholarDigital Library
- Yehuda Koren. 2010. Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Transactions on Knowledge Discovery from Data 4, 1 (2010), 1.Google ScholarDigital Library
- Amy N. Langville and Carl D. Meyer. 2011. Google’s PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press.Google ScholarDigital Library
- Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In Proceedings of the 2018 World Wide Web Conference. 689--698. DOI:https://doi.org/10.1145/3178876.3186150Google ScholarDigital Library
- David Meunier, Renaud Lambiotte, and Edward T. Bullmore. 2010. Modular and hierarchically modular organization of brain networks. Frontiers in Neuroscience 4 (2010), 200. DOI:10.3389/fnins.2010.00200Google ScholarCross Ref
- Carl D. Meyer. 2000. Matrix Analysis and Applied Linear Algebra. Vol. 2. Siam.Google ScholarDigital Library
- Carl D. Meyer and Charles D. Wessell. 2012. Stochastic data clustering. SIAM Journal on Matrix Analysis and Applications 33, 4 (2012), 1214--1236.Google ScholarCross Ref
- Athanasios N. Nikolakopoulos and John D. Garofalakis. 2013. NCDawareRank: A novel ranking method that exploits the decomposable structure of the web. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM’13). ACM, New York, NY, 143--152. DOI:https://doi.org/10.1145/2433396.2433415Google Scholar
- Athanasios N. Nikolakopoulos and John D. Garofalakis. 2015. Random surfing without teleportation. In Algorithms, Probability, Networks, and Games. Springer, 344--357.Google Scholar
- Athanasios N. Nikolakopoulos and John D. Garofalakis. 2015. Top-N recommendations in the presence of sparsity: An NCD-based approach. Web Intelligence 14, 1 (2015), 1--19. DOI:https://doi.org/10.3233/WEB-150324Google Scholar
- Athanasios N. Nikolakopoulos, Maria Kalantzi, and John D. Garofalakis. 2014. On the use of lanczos vectors for efficient latent factor-based top-n recommendation. In Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS’14). ACM, 28.Google Scholar
- Athanasios N Nikolakopoulos, Vassilis Kalantzis, Efstratios Gallopoulos, and John D Garofalakis. 2019. EigenRec: Generalizing PureSVD for effective and efficient top-N recommendations. Knowledge and Information Systems 58, 1 (2019), 59--81.Google ScholarDigital Library
- Athanasios N. Nikolakopoulos and George Karypis. 2019. RecWalk: Nearly uncoupled random walks for Top-N recommendation. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining (WSDM’19). ACM, New York, NY, 9. DOI:https://doi.org/10.1145/3289600.3291016Google Scholar
- A. N. Nikolakopoulos, A. Korba, and J. D. Garofalakis. 2016. Random surfing on multipartite graphs. In Proceedings of the 2016 IEEE International Conference on Big Data. 736--745. DOI:https://doi.org/10.1109/BigData.2016.7840666Google ScholarCross Ref
- Athanasios N. Nikolakopoulos, Marianna Kouneli, and John Garofalakis. 2013. A novel hierarchical approach to ranking-based collaborative filtering. In Proceedings of the International Conference on Engineering Applications of Neural Networks. Springer, 50--59.Google ScholarCross Ref
- Athanasios N. Nikolakopoulos, Marianna A. Kouneli, and John Garofalakis. 2015. Hierarchical Itemspace rank: Exploiting hierarchy to alleviate sparsity in ranking-based recommendation. Neurocomputing 163 (2015), 126--136. DOI:https://doi.org/10.1016/j.neucom.2014.09.082Google ScholarDigital Library
- Xia Ning, Christian Desrosiers, and George Karypis. 2015. A comprehensive survey of neighborhood-based recommendation methods. In Recommender Systems Handbook. Springer, 37--76.Google Scholar
- Xia Ning and George Karypis. 2011. Slim: Sparse linear methods for top-n recommender systems. In Proceedings of the 2011 11th IEEE International Conference on Data Mining. IEEE, 497--506.Google 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. ACM, 155--162.Google ScholarDigital Library
- Xia Ning, Athanasios N. Nikolakopoulos, Zeren Shui, Mohit Sharma, and George Karypis. 2019. SLIM Library for Recommender Systems. Retrieved from https://github.com/KarypisLab/SLIM.Google Scholar
- Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report 1999-66. Stanford InfoLab. Retrieved from http://ilpubs.stanford.edu:8090/422/.Google Scholar
- Andreas Reinstaller. 2007. The division of labor in the firm: Agency, near-decomposability and the Babbage principle. Journal of Institutional Economics 3, 3 (12 2007), 293--322. DOI:https://doi.org/10.1017/S1744137407000732Google ScholarCross Ref
- 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. AUAI Press, 452--461.Google ScholarDigital Library
- Saras D. Sarasvathy. 2003. Entrepreneurship as a science of the artificial. Journal of Economic Psychology 24, 2 (2003), 203--220. DOI:https://doi.org/10.1016/S0167-4870(02)00203-9Google ScholarCross Ref
- Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web. ACM, 285--295.Google ScholarDigital Library
- Max Shpak, Peter Stadler, Gunter P. Wagner, and Lee Altenberg. 2004. Simon-Ando decomposability and fitness landscapes. Theory in Biosciences 123, 2 (2004), 139--180.Google ScholarCross Ref
- Herbert A. Simon and Albert Ando. 1961. Aggregation of variables in dynamic systems. Econometrica: Journal of The Econometric Society 29, 2 (Apr. 1961), 111--138.Google ScholarCross Ref
- Harald Steck. 2019. Embarrassingly shallow autoencoders for sparse data. In The World Wide Web Conference (WWW’19). ACM, New York, NY, 3251--3257. DOI:https://doi.org/10.1145/3308558.3313710Google ScholarDigital Library
- G. W. Stewart. 1991. On the sensitivity of nearly uncoupled Markov chains. Numerical Solution of Markov Chains. Probability: Pure and Applied 8 (1991), 105--119.Google Scholar
- William J. Stewart. 1994. Introduction to the Numerical Solution of Markov Chains. Princeton University Press.Google Scholar
- Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. ACM, 565--573.Google ScholarDigital Library
- Jason Weston, Ron J. Weiss, and Hector Yee. 2013. Nonlinear latent factorization by embedding multiple user interests. In Proceedings of the 7th ACM Conference on Recommender Systems. ACM, 65--68.Google 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. ACM, 153--162.Google 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 Trans. Inf. Syst. 37, 3, Article 33 (July 2019), 25 pages. DOI:https://doi.org/10.1145/3314578Google ScholarDigital Library
- Ramsin Yakob and Fredrik Tell. 2007. Managing near decomposability in complex platform development projects. International Journal of Technology Intelligence and Planning 3, 4 (2007), 387--407.Google ScholarCross Ref
- Sai Yayavaram and Gautam Ahuja. 2008. Decomposability in knowledge structures and its impact on the usefulness of inventions and knowledge-base malleability. Administrative Science Quarterly 53, 2 (2008), 333--362.Google ScholarCross Ref
- Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys 52, 1 (2019), 5.Google ScholarDigital Library
- Yong Zheng, Bamshad Mobasher, and Robin Burke. 2014. CSLIM: Contextual SLIM recommendation algorithms. In Proceedings of the 8th ACM Conference on Recommender Systems. ACM, 301--304.Google ScholarDigital Library
- Tao Zhou, Zoltán Kuscsik, Jian-Guo Liu, Matúš Medo, Joseph Rushton Wakeling, and Yi-Cheng Zhang. 2010. Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences 107, 10 (2010), 4511--4515. DOI:https://doi.org/10.1073/pnas.1000488107Google ScholarCross Ref
- Yangbo Zhu, Shaozhi Ye, and Xing Li. 2005. Distributed PageRank computation based on iterative aggregation-disaggregation methods. In Proceedings of the 14th ACM International Conference on Information and Knowledge Management. ACM, 578--585.Google ScholarDigital Library
Index Terms
- Boosting Item-based Collaborative Filtering via Nearly Uncoupled Random Walks
Recommendations
RecWalk: Nearly Uncoupled Random Walks for Top-N Recommendation
WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data MiningRandom walks can provide a powerful tool for harvesting the rich network of interactions captured within item-based models for top-n recommendation. They can exploit indirect relations between the items, mitigate the effects of sparsity, ensure wider ...
Adaptive collaborative filtering based on user-genre-item relation
Collaborative filtering provides personalised recommendations based on individual user preferences as well as those of other users with similar interests. In collaborative filtering, memory-based approaches make predictions by measuring the whole ...
A Collaborative Filtering Recommendation Algorithm Based on Item Classification
PACCS '09: Proceedings of the 2009 Pacific-Asia Conference on Circuits, Communications and SystemsCollaborative filtering systems represent services of personalized that aim at predicting a user’s interest on some items available in the application systems. With the development of electronic commerce, the number of users and items grows rapidly, ...
Comments