Bin Liu
Abstract
Advances in smartphone technology have promoted the rapid development of mobile apps. However, the availability of a huge number of mobile apps in application stores has imposed the challenge of finding the right apps to meet the user needs. Indeed, there is a critical demand for personalized app recommendations. Along this line, there are opportunities and challenges posed by two unique characteristics of mobile apps. First, app markets have organized apps in a hierarchical taxonomy. Second, apps with similar functionalities are competing with each other. Although there are a variety of approaches for mobile app recommendations, these approaches do not have a focus on dealing with these opportunities and challenges. To this end, in this article, we provide a systematic study for addressing these challenges. Specifically, we develop a structural user choice model (SUCM) to learn fine-grained user preferences by exploiting the hierarchical taxonomy of apps as well as the competitive relationships among apps. Moreover, we design an efficient learning algorithm to estimate the parameters for the SUCM model. Finally, we perform extensive experiments on a large app adoption dataset collected from Google Play. The results show that SUCM consistently outperforms state-of-the-art Top-N recommendation methods by a significant margin.
- Deepak Agarwal and Bee-Chung Chen. 2009. Regression-based latent factor models. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’09). 19--28. Google Scholar
Digital Library
- Amr Ahmed, Bhargav Kanagal, Sandeep Pandey, Vanja Josifovski, Lluis Garcia Pueyo, and Jeff Yuan. 2013. Latent factor models with additive and hierarchically-smoothed user preferences. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM’13). 385--394. Google ScholarDigital Library
- Natalie Aizenberg, Yehuda Koren, and Oren Somekh. 2012. Build your own music recommender by modeling internet radio streams. In Proceedings of the 21st International Conference on World Wide Web. 1--10. Google ScholarDigital Library
- Ricardo Baeza-Yates, Di Jiang, Fabrizio Silvestri, and Beverly Harrison. 2015. Predicting the next app that you are going to use. In Proceedings of the 8th ACM International Conference on Web Search and Data Mining (WSDM’15). 285--294. Google ScholarDigital Library
- Robert M. Bell and Yehuda Koren. 2007. Lessons from the Netflix prize challenge. SIGKDD Explor. Newsl. 9, 2 (Dec. 2007), 75--79. Google ScholarDigital Library
- Moshe E. Ben-Akiva and Steven R. Lerman. 1985. Discrete Choice Analysis: Theory and Application to Travel Demand. Vol. 9. MIT Press.Google Scholar
- Léon Bottou. 2010. Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT 2010. 177--186.Google ScholarCross Ref
- Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, and Yong Yu. 2012. SVDFeature: A toolkit for feature-based collaborative filtering. J. Mach. Learn. Res. 13, 1 (Dec. 2012), 3619--3622. Google ScholarDigital Library
- Xu Chen, Yongfeng Zhang, Tao Xu, and Zheng Qin. 2016. Learning to rank features for recommendation over multiple categories. In Proceedings of the 39th International Acm SIGIR Conference on Research 8 Development in Information Retrieval. ACM. Google ScholarDigital Library
- Christoffer Davidsson and Simon Moritz. 2011. Utilizing implicit feedback and context to recommend mobile applications from first use. In Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation (CaRR’11). 19--22. Google ScholarDigital Library
- Neil Zhenqiang Gong, Wenchang Xu, Ling Huang, Prateek Mittal, Emil Stefanov, Vyas Sekar, and Dawn Song. 2012. Evolution of social-attribute networks: Measurements, modeling, and implications using google+. In Proceedings of the 2012 ACM Conference on Internet Measurement Conference. ACM, 131--144. Google ScholarDigital Library
- Peter M. Guadagni and John D. C. Little. 1983. A logit model of brand choice calibrated on scanner data. Mark. Sci. 2, 3 (1983), 203--238. Google ScholarDigital Library
- Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In Proceedings of 8th IEEE International Conference on Data Mining, (ICDM’08). 263--272. Google ScholarDigital Library
- Bhargav Kanagal, Amr Ahmed, Sandeep Pandey, Vanja Josifovski, Jeff Yuan, and Lluis Garcia-Pueyo. 2012. Supercharging recommender systems using taxonomies for learning user purchase behavior. Proc. VLDB Endow. 5, 10 (June 2012), 956--967. Google ScholarDigital Library
- Alexandros Karatzoglou, Linas Baltrunas, Karen Church, and Matthias Böhmer. 2012. Climbing the app wall: Enabling mobile app discovery through context-aware recommendations. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM’12). 2527--2530. Google 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 (KDD’08). 426--434. Google ScholarDigital Library
- Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (Aug. 2009), 30--37. Google ScholarDigital Library
- Ravi Kumar, Mohammad Mahdian, Bo Pang, Andrew Tomkins, and Sergei Vassilvitskii. 2015. Driven by food: Modeling geographic choice. In Proceedings of the 8th ACM International Conference on Web Search and Data Mining (WSDM’15). 213--222. Google ScholarDigital Library
- Jovian Lin. 2014. Mobile App Recommendation. Ph.D. Dissertation. National University of Singapore.Google Scholar
- Jovian Lin, Kazunari Sugiyama, Min-Yen Kan, and Tat-Seng Chua. 2013. Addressing cold-start in app recommendation: Latent user models constructed from Twitter followers. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’13). 283--292. Google ScholarDigital Library
- Jovian Lin, Kazunari Sugiyama, Min-Yen Kan, and Tat-Seng Chua. 2014. New and improved: Modeling versions to improve app recommendation. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’14). 647--656. Google ScholarDigital Library
- Bin Liu, Yanjie Fu, Zijun Yao, and Hui Xiong. 2013. Learning geographical preferences for point-of-interest recommendation. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’13). 1043--1051. Google ScholarDigital Library
- Bin Liu, Deguang Kong, Lei Cen, Neil Zhenqiang Gong, Hongxia Jin, and Hui Xiong. 2015a. Personalized mobile app recommendation: Reconciling app functionality and user privacy preference. In Proceedings of the 8th International Conference on Web Search and Data Mining (WSDM’15). 315--324. Google ScholarDigital Library
- Bin Liu, Hui Xiong, Spiros Papadimitriou, Yanjie Fu, and Zijun Yao. 2015b. A general geographical probabilistic factor model for point of interest recommendation. IEEE Trans. Knowl. Data Eng. 27, 5 (2015), 1167--1179.Google ScholarDigital Library
- R. Duncan Luce. 1959. Individual Choice Behavior: A Theoretical Analysis. Wiley.Google Scholar
- Charles F. Manski. 1977. The structure of random utility models. Theory Decis. 8, 3 (1977), 229--254.Google ScholarCross Ref
- Daniel McFadden. 1973. Conditional logit analysis of qualitative choice behavior. In Frontiers in Econometrics, P. Zarembka (Ed.). Academic Press, New-York.Google Scholar
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 (2013).Google Scholar
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013b. Distributed representations of words and phrases and their compositionality. In Proceedings of Advances in Neural Information Processing Systems. 3111--3119. Google ScholarDigital Library
- Frederic Morin and Yoshua Bengio. 2005. Hierarchical probabilistic neural network language model. In Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics. 246--252.Google Scholar
- Rong Pan, Yunhong Zhou, Bin Cao, Nathan Nan Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. 2008. One-class collaborative filtering. In Proceedings of 8th IEEE International Conference on Data Mining (ICDM’08). 502--511. Google ScholarDigital Library
- Dae Hoon Park, Mengwen Liu, ChengXiang Zhai, and Haohong Wang. 2015. Leveraging user reviews to improve accuracy for mobile app retrieval. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’15). 533--542. Google ScholarDigital Library
- Steffen Rendle. 2010. Factorization machines. In Proceedings of IEEE 10th International Conference on Data Mining (ICDM). IEEE, 995--1000. Google ScholarDigital Library
- Steffen Rendle. 2012. Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. 3, 3, Article 57 (May 2012), 22 pages. DOI:http://dx.doi.org/10.1145/2168752.2168771 Google ScholarDigital Library
- 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
- Ruslan Salakhutdinov and Andriy Mnih. 2008. Probabilistic matrix factorization. In Proceedings of Neural Information Processing Systems (NIPS), Vol. 20. Google ScholarDigital Library
- Kenneth Train. 1978. A validation test of a disaggregate mode choice model. Transp. Res. 12, 3 (1978), 167--174.Google ScholarCross Ref
- Wolfgang Woerndl, Christian Schueller, and Rolf Wojtech. 2007. A hybrid recommender system for context-aware recommendations of mobile applications. In Proceedings of IEEE 23rd International Conference on Data Engineering Workshop, 2007. IEEE, 871--878. 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 (WSDM’16). 153--162. Google ScholarDigital Library
- Yao Wu and Martin Ester. 2015. FLAME: A probabilistic model combining aspect based opinion mining and collaborative filtering. In Proceedings of the 8th ACM International Conference on Web Search and Data Mining (WSDM’15). ACM, 199--208. Google ScholarDigital Library
- Shuang-Hong Yang, Bo Long, Alexander J. Smola, Hongyuan Zha, and Zhaohui Zheng. 2011. Collaborative competitive filtering: Learning recommender using context of user choice. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’11). 295--304. Google ScholarDigital Library
- Peifeng Yin, Ping Luo, Wang-Chien Lee, and Min Wang. 2013. App recommendation: A contest between satisfaction and temptation. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM’13). 395--404. Google ScholarDigital Library
- Yuchen Zhang, Amr Ahmed, Vanja Josifovski, and Alexander Smola. 2014a. Taxonomy discovery for personalized recommendation. In Proceedings of the 7th International Conference on Web Search and Data Mining (WSDM’14). 243--252. Google ScholarDigital Library
- Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma. 2014b. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In Proceedings of the 37th International ACM SIGIR Conference on Research 8 Development in Information Retrieval. ACM, 83--92. Google ScholarDigital Library
- Hengshu Zhu, Hui Xiong, Yong Ge, and Enhong Chen. 2014. Mobile app recommendation with security and privacy awareness. In Proceedings of the 20th ACM International Conference on Knowledge Discovery and Data Mining (KDD’14). Google ScholarDigital Library
- 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. Google ScholarDigital Library
Index Terms
- Structural Analysis of User Choices for Mobile App Recommendation
Recommendations
Mobile app recommendations with security and privacy awareness
KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data miningWith the rapid prevalence of smart mobile devices, the number of mobile Apps available has exploded over the past few years. To facilitate the choice of mobile Apps, existing mobile App recommender systems typically recommend popular mobile Apps to ...
Personalized Mobile App Recommendation: Reconciling App Functionality and User Privacy Preference
WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data MiningRecent years have witnessed a rapid adoption of mobile devices and a dramatic proliferation of mobile applications (Apps for brevity). However, the large number of mobile Apps makes it difficult for users to locate relevant Apps. Therefore, recommending ...
An Explorative Study of the Mobile App Ecosystem from App Developers' Perspective
WWW '17: Proceedings of the 26th International Conference on World Wide WebWith the prevalence of smartphones, app markets such as Apple App Store and Google Play has become the center stage in the mobile app ecosystem, with millions of apps developed by tens of thousands of app developers in each major market. This paper ...
Reviews
Amos O Olagunju
Mobile apps are rapidly evolving due to ongoing improvements in smartphone technology. However, the use of mobile devices introduces some obstacles for rookie users. How should novice users locate suitable apps from a hierarchy of apps to accomplish different tasks How should rival mobile apps with comparable operational services be effectively recommended to users In efforts to improve app recommendation based on the interests and proclivities of users, Liu and colleagues present a structural user choice model (SUCM) for ascertaining the well-organized classification and viable associations between apps. In the SUCM, a user first decides on the type of apps to use prior to selecting the suitable app category/subcategory. The model requires the user to choose from the root of mobile apps, and then proceed through levels of categories and subcategories of apps until the user's preferences are optimally satisfied. A probabilistic algorithm called the softmax function is used to examine the relationships among competing nodes of apps, in efforts to minimize the processing times for identifying the user's preferences from numerous alternative app categories. This reliable algorithm is predicated on the theoretical foundations axioms of user preferences and choices. The authors compellingly present the log likelihood algorithms for understanding the dynamic parameters that are applied to effectively select among competing apps for the users. The authors uniquely illustrate how SUCM works with a tree of alternative choices of apps from Google Play (music and audio, finance, sports, entertainment, games, and so on). In an effort to assess the effectiveness of the SUCM, experiments were performed with data derived from the marketplace-Google Play data-which contained several apps. Clearly, compared to the reputable currently available algorithms for ascertaining user-behavioral choices in the literature, the authors present more reliable results for distributing the apps to users based on their preferences. I encourage all statisticians and artificial intelligence advocates to read the exciting ideas in this paper. Online Computing Reviews Service
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