HyperCARS: Using Hyperbolic Embeddings for Generating Hierarchical Contextual Situations in Context-Aware Recommender Systems

Published Online:https://doi.org/10.1287/isre.2022.0202

References

  • Adjerid I, Adler-Milstein J, Angst C (2018) Reducing Medicare spending through electronic health information exchange: The role of incentives and exchange maturity. Inform. Systems Res. 29(2):341–361.LinkGoogle Scholar
  • Adomavicius G, Bauman K, Tuzhilin A, Unger M (2021) Context-aware recommender systems: From foundations to recent developments context-aware recommender systems. Recommender Systems Handbook (Springer, Berlin), 211–250.Google Scholar
  • Aggarwal CC (2018) Neural Networks and Deep Learning, vol. 10 (Springer, Berlin).CrossrefGoogle Scholar
  • Baltrunas L (2019) Keynote: Contextualization at Netflix. Proc. Workshop Context-Aware Recommender Systems 13th ACM Conf. Recommender Systems. Accessed June 21, 2024, https://cars-workshops.com/cars-2019-1.Google Scholar
  • Baltrunas L, Church K, Karatzoglou A, Oliver N (2015) Frappe: Understanding the usage and perception of mobile app recommendations in-the-wild. Preprint, submitted May 12, https://arxiv.org/abs/1505.03014.Google Scholar
  • Bauman K, Tuzhilin A (2018) Recommending remedial learning materials to students by filling their knowledge gaps. MIS Quart. 42(1):313–332.CrossrefGoogle Scholar
  • Bauman K, Tuzhilin A (2022) Know thy context: Parsing contextual information from user reviews for recommendation purposes. Inform. Systems Res. 33(1):179–202.LinkGoogle Scholar
  • Bella G, Bouquet P (2019) Modeling and using context. Proc. 11th Internat. Interdisciplinary Conf., Lecture Notes in Computer Science, vol. 11939 (Springer Nature, New York).Google Scholar
  • Bengio Y, Courville A, Vincent P (2013) Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Machine Intelligence 35(8):1798–1828.CrossrefGoogle Scholar
  • Beutel A, Covington P, Jain S, Xu C, Li J, Gatto V, Chi EH (2018) Latent cross: Making use of context in recurrent recommender systems. Proc. Eleventh ACM Internat. Conf. Web Search Data Mining (WSDM ’18) (Association for Computing Machinery, New York), 46–54.Google Scholar
  • Booch G, Jacobson I, Rumbaugh J (1996) The unified modeling language. Unix Rev. 14(13):5.Google Scholar
  • Bourbaki N (1954) Theory of sets. Accessed June 21, 2024, http://archives-bourbaki.ahp-numerique.fr/elements-mathematique.Google Scholar
  • Campello RJGB, Moulavi D, Sander J (2013) Density-based clustering based on hierarchical density estimates. Pei J, Tseng VS, Cao L, Motoda H, Xu G, eds. Advances in Knowledge Discovery and Data Mining (Springer, Berlin), 160–172.CrossrefGoogle Scholar
  • Chamberlain BP, Hardwick SR, Wardrope DR, Dzogang F, Daolio F, Vargas S (2019) Scalable hyperbolic recommender systems. Preprint, submitted February 22, https://arxiv.org/abs/1902.08648.Google Scholar
  • Chami I, Ying Z, Ré C, Leskovec J (2019) Hyperbolic graph convolutional neural networks. Adv. Neural Inform. Processing Systems 32:4868–4879.Google Scholar
  • Chung J, Kastner K, Dinh L, Goel K, Courville A, Bengio Y (2015) A recurrent latent variable model for sequential data. Proc. 28th Internat. Conf. Neural Inform. Processing Systems (NIPS’15), vol. 2 (MIT Press, Cambridge, MA), 2980–2988.Google Scholar
  • Davidson I, Ravi SS (2005) Agglomerative hierarchical clustering with constraints: Theoretical and empirical results. Proc. 9th Eur. Conf. Eur. Conf. Machine Learn. Principles Practice Knowledge Discovery Databases (ECMLPKDD’05) (Springer-Verlag, Berlin, Heidelberg), 59–70.Google Scholar
  • del Carmen Rodríguez-Hernández M, Ilarri S (2021) AI-based mobile context-aware recommender systems from an information management perspective: Progress and directions. Knowledge Base Systems 215:106740.CrossrefGoogle Scholar
  • Dhariwal P, Jun H, Payne C, Kim JW, Radford A, Sutskever I (2020) Jukebox: A generative model for music. Preprint, submitted April 30, https://arxiv.org/abs/2005.00341.Google Scholar
  • Dhingra B, Shallue CJ, Norouzi M, Dai AM, Dahl GE (2018) Embedding text in hyperbolic spaces. Preprint, submitted June 12, https://arxiv.org/abs/1806.04313.Google Scholar
  • Ding X, Tang J, Liu T, Xu C, Zhang Y, Shi F, Jiang Q, Shen D (2019) Infer implicit contexts in real-time online-to-offline recommendation. Proc. 25th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (KDD ’19) (Association for Computing Machinery, New York), 2336–2346.Google Scholar
  • Dong B, Jian S, Zuo K (2020) Cde++: Learning categorical data embedding by enhancing heterogeneous feature value coupling relationships. Entropy 22(4):391.CrossrefGoogle Scholar
  • Du X, Su M, Zhang X, Zheng X (2017) Bidding for multiple keywords in sponsored search advertising: Keyword categories and match types. Inform. Systems Res. 28(4):711–722.LinkGoogle Scholar
  • Dunn JC (1974) Well-separated clusters and optimal fuzzy partitions. J. Cybernetics 4(1):95–104.CrossrefGoogle Scholar
  • Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Proc. Second Internat. Conf. Knowledge Discovery Data Mining (KDD’96) (AAAI Press, Palo Alto, CA), 226–231.Google Scholar
  • Feng S, Tran LV, Cong G, Chen L, Li J, Li F (2020) HME: A hyperbolic metric embedding approach for next-POI recommendation. Proc. 43rd Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (SIGIR ’20) (Association for Computing Machinery, New York), 1429–1438.Google Scholar
  • Ganea O-E, Bécigneul G, Hofmann T (2018) Hyperbolic neural networks. Proc. 32nd Internat. Conf. Neural Inform. Processing Systems (NIPS’18) (Curran Associates Inc., Red Hook, NY), 5345–5355.Google Scholar
  • Gelfond M, Kahl Y (2014) Knowledge Representation, Reasoning, and the Design of Intelligent Agents: The Answer-Set Programming Approach (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Ghadimi Atigh M, Keller-Ressel M, Mettes P (2021) Hyperbolic Busemann Learning with ideal prototypes. Adv. Neural Inform. Processing Systems 34:103–115.Google Scholar
  • Goes PB, Guo C, Lin M (2016) Do incentive hierarchies induce user effort? Evidence from an online knowledge exchange. Inform. Systems Res. 27(3):497–516.LinkGoogle Scholar
  • Gong J, Abhishek V, Li B (2018) Examining the impact of keyword ambiguity on search advertising performance: A topic model approach. MIS Quart. 42(3):805–830.CrossrefGoogle Scholar
  • Greenberg MJ (1993) Euclidean and Non-Euclidean Geometries: Development and History (Macmillan, New York).Google Scholar
  • Hafner D, Lillicrap T, Ba J, Norouzi M (2019) Dream to control: Learning behaviors by latent imagination. Preprint, submitted December 3, https://arxiv.org/abs/1912.01603.Google Scholar
  • Hansen C, Hansen C, Maystre L, Mehrotra R, Brost B, Tomasi F, Lalmas M (2020) Contextual and sequential user embeddings for large-scale music recommendation. Proc. 14th ACM Conf. Recommender Systems (RecSys ’20) (Association for Computing Machinery, New York), 53–62.Google Scholar
  • Haruna K, Akmar Ismail M, Suhendroyono S, Damiasih D, Pierewan A, Chiroma H, Herawan T (2017) Context-aware recommender system: A review of recent developmental process and future research direction. Appl. Sci. 7(12):1211.CrossrefGoogle Scholar
  • Hastings R (2012) AWS re:Invent 2012, Day 1 Keynote. Accessed June 21, 2024, http://www.youtube.com/watch?v=8FJ5DBLSFe4.Google Scholar
  • He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. Proc. 26th Internat. Conf. World Wide Web (WWW ’17) (International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE), 173–182.Google Scholar
  • Ho YJ, Dewan S, Ho YC (2020) Distance and local competition in mobile geofencing. Inform. Systems Res. 31(4):1421–1442.LinkGoogle Scholar
  • Jagabathula S, Subramanian L, Venkataraman A (2018) A model-based embedding technique for segmenting customers. Oper. Res. 66(5):1247–1267.LinkGoogle Scholar
  • Jian S, Pang G, Cao L, Lu K, Gao H (2018) Cure: Flexible categorical data representation by hierarchical coupling learning. IEEE Trans. Knowledge Data Engrg. 31(5):853–866.CrossrefGoogle Scholar
  • Karanam SA, Agarwal A, Barua A (2023) Design for social sharing: The case of mobile apps. Inform. Systems Res. 34(2):721–743.LinkGoogle Scholar
  • Khrulkov V, Mirvakhabova L, Ustinova E, Oseledets I, Lempitsky V (2020) Hyperbolic image embeddings. Proc. IEEE/CVF Conf. Computer Vision Pattern Recognition (IEEE Computer Society, Washington, DC), 6418–6428.Google Scholar
  • Kingma DP, Welling M (2014) Auto-encoding variational bayes. Bengio Y, LeCun Y, eds., Proc. 2nd Internat. Conf. Learn. Representations, ICLR 2014, (Ban, AB).Google Scholar
  • Kohl SAA, Romera-Paredes B, Meyer C, De Fauw J, Ledsam JR, Maier-Hein KH, Ali Eslami SM, Rezende DJ, Ronneberger O (2018) A probabilistic U-Net for segmentation of ambiguous images. Proc. 32nd Internat. Conf. Neural Inform. Processing Systems (NIPS’18) (Curran Associates Inc., Red Hook, NY), 6965–6975.Google Scholar
  • Kokkodis M (2022) Adjusting skillset cohesion in online labor markets: Reputation gains and opportunity losses. Inform. Systems Res. 34(3):1245–1258.LinkGoogle Scholar
  • Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer (8):30–37.CrossrefGoogle Scholar
  • Lakkaraju H, Bach SH, Leskovec J (2016) Interpretable decision sets: A joint framework for description and prediction. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 1675–1684.Google Scholar
  • Langer N, Gopal RD, Bapna R (2020) Onward and upward? An empirical investigation of gender and promotions in information technology services. Inform. Systems Res. 31(2):383–398.LinkGoogle Scholar
  • Lee D, Hosanagar K (2019) How do recommender systems affect sales diversity? A cross-category investigation via randomized field experiment. Inform. Systems Res. 30(1):239–259.LinkGoogle Scholar
  • Li P, Que M, Jiang Z, Hu Y, Tuzhilin A (2020) PURS: Personalized unexpected recommender system for improving user satisfaction. Proc. 14th ACM Conf. Recommender Systems (RecSys ’20) (Association for Computing Machinery, New York), 279–288.Google Scholar
  • Lindberg A, Schecter A, Berente N, Hennel P, Lyytinen K (2024) The entrainment of task allocation and release cycles in open source software development. MIS Quart. 48(1):67–94.CrossrefGoogle Scholar
  • Liu Y, Wei W, Sun A, Miao C (2014) Exploiting geographical neighborhood characteristics for location recommendation. Proc. 23rd ACM Internat. Conf. Inform. Knowledge Management (CIKM ’14) (Association for Computing Machinery, New York), 739–748.Google Scholar
  • Liu D, He M, Luo J, Lin J, Wang M, Zhang X, Pan W, Ming Z (2022) User-event graph embedding learning for context-aware recommendation. Proc. 28th ACM SIGKDD Conf. Knowledge Discovery Data Mining (KDD ’22) (Association for Computing Machinery, New York), 1051–1059.Google Scholar
  • Loni B, Pagano R, Larson M, Hanjalic A (2016) Bayesian personalized ranking with multi-channel user feedback. Proc. 10th ACM Conf. Recommender Systems (RecSys ’16) (Association for Computing Machinery, New York), 361–364.Google Scholar
  • Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz R, et al. (2020) From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence 2(1):2522–5839.CrossrefGoogle Scholar
  • Mämmelä A, Riekki J, Kiviranta M (2023) Loose coupling: An invisible thread in the history of technology. IEEE Access 11:59456–59482.CrossrefGoogle Scholar
  • Mathieu E, Le Lan C, Maddison CJ, Tomioka R, Teh YW (2019) Continuous hierarchical representations with poincaré variational auto-encoders. Wallach H, Larochelle H, Beygelzimer A, d’Alché-Buc F, Fox E, Garnett R, eds. Proc. 33rd Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 12565–12576.Google Scholar
  • Miranda S, Berente N, Seidel S, Safadi H, Burton-Jones A (2022) Editor’s comments: Computationally intensive theory construction: A primer for authors and reviewers. MIS Quart. 46(2):iii–xviii.CrossrefGoogle Scholar
  • Mirvakhabova L, Frolov E, Khrulkov V, Oseledets I, Tuzhilin A (2020) Performance of hyperbolic geometry models on top-N recommendation tasks. Proc. 14th ACM Conf. Recommender Systems (ACM, New York), 527–532.Google Scholar
  • Nalepa GJ, Kutt K, Bobek S (2019) Mobile platform for affective context-aware systems. Future Generation Comput. Systems 92:490–503.CrossrefGoogle Scholar
  • Nickel M, Kiela D (2017) Poincaré embeddings for learning hierarchical representations, Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 30 (Curran Associates Inc., Red Hook, NY), 6341–6350.Google Scholar
  • Nie L, Li Y, Feng F, Song X, Wang M, Wang Y (2020) Large-scale question tagging via joint question-topic embedding learning. ACM Trans. Inform. Systems 38(2):1–23.CrossrefGoogle Scholar
  • Nweke HF, Teh YW, Al-Garadi MA, Alo UR (2018) Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Systems Appl. 105:233–261.CrossrefGoogle Scholar
  • Okawa M, Iwata T, Kurashima T, Tanaka Y, Toda H, Ueda N (2019) Deep mixture point processes: Spatio-temporal event prediction with rich contextual information. Proc. 25th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (KDD ’19) (Association for Computing Machinery, New York), 373–383.Google Scholar
  • Palmisano C, Tuzhilin A, Gorgoglione M (2008) Using context to improve predictive modeling of customers in personalization applications. IEEE Trans. Knowledge Data Engrg. 20(11):1535–1549.CrossrefGoogle Scholar
  • Panniello U, Gorgoglione M, Tuzhilin A (2016) In CARSs we trust: How context-aware recommendations affect customers’ trust and other business performance measures of recommender systems. Inform. Systems Res. 27(1):182–196.LinkGoogle Scholar
  • Pathak D, Krahenbuhl P, Donahue J, Darrell T, Efros AA (2016) Context encoders: Feature learning by inpainting. Proc. IEEE Conf. Comput. Vision Pattern Recognition, 2536–2544.Google Scholar
  • Petryk M, Rivera M, Bhattacharya S, Qiu L, Kumar S (2022) How network embeddedness affects real-time performance feedback: An empirical investigation. Inform. Systems Res. 33(4):1467–1489.LinkGoogle Scholar
  • Pu J, Liu Y, Chen Y, Qiu L, Cheng HK (2022) What questions are you inclined to answer? Effects of hierarchy in corporate Q&A communities. Inform. Systems Res. 33(1):244–264.LinkGoogle Scholar
  • Ricci F, Rokach L, Shapira B (2022) Recommender Systems Handbook (Springer Nature, Cham, Switzerland).CrossrefGoogle Scholar
  • Rousseeuw PJ (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20:53–65.CrossrefGoogle Scholar
  • Rudolph M, Ruiz F, Athey S, Blei D (2017) Structured embedding models for grouped data. Proc. 31st Internat. Conf. Neural Inform. Processing Systems (NIPS’17) (Curran Associates Inc., Red Hook, NY), 250–260.Google Scholar
  • Russell SJ (2010) Artificial Intelligence: A Modern Approach (Pearson Education, London).Google Scholar
  • Schmeier T, Chisari J, Garrett S, Vintch B (2019) Music recommendations in hyperbolic space: An application of empirical bayes and hierarchical poincaré embeddings. Proc. 13th ACM Conf. Recommender Systems (RecSys ’19) (Association for Computing Machinery, New York), 437–441.Google Scholar
  • Smirnova E, Vasile F (2017) Contextual sequence modeling for recommendation with recurrent neural networks. Proc. 2nd Workshop Deep Learn. Recommender Systems (Association for Computing Machinery, New York), 2–9.Google Scholar
  • Sun H, Zhang L, Ren J, Huang H (2022) Novel hyperbolic clustering-based band hierarchy (hcbh) for effective unsupervised band selection of hyperspectral images. Pattern Recognition 130:108788.CrossrefGoogle Scholar
  • Sun J, Cheng Z, Zuberi S, Pérez F, Volkovs M (2021) HGCF: Hyperbolic graph convolution networks for collaborative filtering. Proc. Web Conference (Association for Computing Machinery, New York), 593–601.Google Scholar
  • Tifrea A, Becigneul G, Ganea OE (2018) Poincare glove: Hyperbolic word embeddings. Preprint, submitted October 15, https://arxiv.org/abs/1810.06546.Google Scholar
  • Unger M, Tuzhilin A (2020) Hierarchical latent context representation for context-aware recommendations. IEEE Trans. Knowledge Data Engrg. 34(7):3322–3334.Google Scholar
  • Unger M, Tuzhilin A, Livne A (2020) Context-aware recommendations based on deep learning frameworks. ACM Trans. Management Inform. Systems 11(2):1–15.CrossrefGoogle Scholar
  • Unger M, Bar A, Shapira B, Rokach L (2016) Toward latent context-aware recommendation systems. Knowledge Based Systems 104:165–178.CrossrefGoogle Scholar
  • Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Proc. 31st Internat. Conf. Neural Inform. Processing Systems (NIPS’17) (Curran Associates Inc., Red Hook, NY), 6000–6010.Google Scholar
  • Vinh TDQ, Tay Y, Zhang S, Cong G, Li XL (2018) HyperML: A boosting metric learning approach in hyperbolic space for recommender systems. Preprint, submitted September 5, https://arxiv.org/abs/1809.01703.Google Scholar
  • Vinh Tran L, Tay Y, Zhang S, Cong G, Li X (2020) HyperML: A boosting metric learning approach in hyperbolic space for recommender systems. Proc. Internat. Conf. Web Search Data Mining (ACM, New York), 609–617.Google Scholar
  • Wang Q, Li B, Singh PV (2018) Copycats vs. original mobile apps: A machine learning copycat-detection method and empirical analysis. Inform. Systems Res. 29(2):273–291.LinkGoogle Scholar
  • Wang H, Lian D, Tong H, Liu Q, Huang Z, Chen E (2021) HyperSoRec: Exploiting hyperbolic user and item representations with multiple aspects for social-aware recommendation. ACM Trans. Inform. Systems 40(2):1–28.CrossrefGoogle Scholar
  • Wu W, Zhao J, Zhang C, Meng F, Zhang Z, Zhang Y, Sun Q (2017) Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge Based Systems 128:71–77.CrossrefGoogle Scholar
  • Xin X, Chen B, He X, Wang D, Ding Y, Jose JM (2019) CFM: Convolutional factorization machines for context-aware recommendation. Proc. 28th Internat. Joint Conf. Artificial Intelligence (IJCAI’19) (AAAI Press, Palo Alto, CA), 3926–3932.Google Scholar
  • Yan B, Mai F, Wu C, Chen R, Li X (2023) A computational framework for understanding firm communication during disasters. Inform. Systems Res., ePub ahead of print November 7, https://doi.org/10.1287/isre.2022.0128.Google Scholar
  • Yang Y, Zhang K, Fan Y (2022) Analyzing firm reports for volatility prediction: A knowledge-driven text-embedding approach. INFORMS J. Comput. 34(1):522–540.LinkGoogle Scholar
  • Yelp (2023) Yelp open data set. Accessed June 21, 2024, https://www.yelp.com/dataset/.Google Scholar
  • Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: A survey and new perspectives. ACM Comput. Survey 52(1):1–38.CrossrefGoogle Scholar
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