Abstract
With the widespread usage of smart phones, more and more mobile apps are developed every day, playing an increasingly important role in changing our lifestyles and business models. In this trend, it becomes a hot research topic for developing effective mobile app recommender systems in both industry and academia. Compared with existing studies about mobile app recommendations, our research aims to improve the recommendation effectiveness based on analyzing a psychological trait of human beings, exploratory behavior, which refers to a type of variety-seeking behavior in unfamiliar domains. To this end, we propose a novel probabilistic model named Goal-oriented Exploratory Model (GEM), integrating exploratory behavior identification with personalized item recommendation. An algorithm combining collapsed Gibbs sampling and Expectation Maximization is developed for model learning and inference. Through extensive experiments conducted on a real dataset, the proposed model demonstrates superior recommendation performances and good interpretability compared with state-of-art recommendation methods. Moreover, empirical analyses on exploratory behavior find that individuals with a strong exploratory tendency exhibit behavioral patterns of variety seeking, risk taking, and higher involvement. Besides, mobile apps that are less popular or in the long tail possess greater potential of arousing exploratory behavior in individuals.
- Hans Baumgartner and Jan Benedict E. M. Steenkamp. 1996. Exploratory consumer buying behavior: Conceptualization and measurement. International Journal of Research in Marketing 13, 121--137.Google ScholarCross Ref
- D. E. Berlyne. 1962. Motivational problems raised by exploratory and epistemic behavior. In Psychology: A Study of Science, 284--364.Google Scholar
- Christopher Bishop. 2006. Pattern recognition and machine learning. Journal of Electronic Imaging 16, 140--155.Google Scholar
- David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. Journal of Machine Learning Research 3, 993--1022. Google ScholarDigital Library
- Zhiyong Cheng and Jialie Shen. 2016. On effective location-aware music recommendation. ACM Transactions on Information Systems (TOIS) 34, 13. Google ScholarDigital Library
- Mark Claypool, Anuja Gokhale, Tim Miranda, Pavel Murnikov, Dmitry Netes, and Matthew Sartin. 1999. Combining content-based and collaborative filters in an online newspaper. In Proceedings of ACM SIGIR Workshop on Recommender Systems, Citeseer.Google Scholar
- A. Dempster. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society 39, 1--38.Google Scholar
- Mukund Deshpande and George Karypis. 2004. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS) 22, 143--177. Google ScholarDigital Library
- Xiao Han and Thomas Stibor. 2010. Efficient collapsed Gibbs sampling for latent Dirichlet allocation. Journal of Machine Learning Research 13, 63--78.Google Scholar
- Gregor Heinrich. 2005. Parameter estimation for text analysis. Technical Report. University of Leipzig, Germany.Google Scholar
- Roland Helm and Sebastian Landschulze. 2008. Optimal stimulation level theory, exploratory consumer behaviour and product adoption: An analysis of underlying structures across product categories. Review of Managerial Science 3, 41--73.Google ScholarCross Ref
- 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, 2008 (ICDM’08). IEEE, 263--272. 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 ACM International Conference on Information and Knowledge Management (CIKM’12). ACM, New York, 2527--2530. Google ScholarDigital Library
- K. Karplus, C. Barrett, and R. Hughey. 1998. Hidden Markov models for detecting remote protein homologies. Bioinformatics 14, 846--856.Google ScholarCross Ref
- Julian Kupiec. 1992. Robust part-of-speech tagging using a hidden markov model. Computer Speech 8 Language 6, 225--242.Google Scholar
- Takeshi Kurashima, Tomoharu Iwata, Takahide Hoshide, Noriko Takaya, and Ko Fujimura. 2013. Geo topic model: Joint modeling of user's activity area and interests for location recommendation. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining. ACM, 375--384. Google ScholarDigital Library
- 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 International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, NewYork, 283--292. Google ScholarDigital Library
- Bin Liu, Deguang Kong, Lei Cen, Neil Zhenqiang Gong, Hongxia Jin, and Hui Xiong. 2015. Personalized mobile app recommendation: Reconciling app functionality and user privacy preference. In Proceedings of the 8th ACM International Conference on Web Search 8 Data Mining. ACM, New York, 315--324. Google ScholarDigital Library
- Bin Liu, and Hui Xiong. 2013. Point-of-interest recommendation in location-based social networks with topic and location awareness. In SDM, 396--404.Google Scholar
- Qi Liu, Enhong Chen, Hui Xiong, Yong Ge, Zhongmou Li, and Xiang Wu. 2014. A cocktail approach for travel package recommendation. IEEE Transactions on Knowledge and Data Engineering 26, 278--293. Google ScholarDigital Library
- Qi Liu, Yong Ge, Zhongmou Li, Enhong Chen, and Hui Xiong. 2011. Personalized travel package recommendation. In Proceedings of the 2011 IEEE 11th International Conference on Data Mining (ICDM). IEEE, 407--416. Google ScholarDigital Library
- Warren K. Garlington and Helen E. Shimota. 1964. The change seeker index: A measure of the need for variable stimulus input. Psychological Reports 14, 919--924.Google ScholarCross Ref
- J. A. Mccart, B. Padmanabhan, and D. J. Berndt. 2009. Goal attainment on long tail web sites: An information foraging approach. Decision Support Systems 55, 235--246. Google ScholarDigital Library
- Albert Mehrabian and James A. Russell. 1973. A measure of arousal seeking tendency. Environment 8 Behavior 5, 3, 315--333.Google Scholar
- Ramesh M. Nallapati, Amr Ahmed, Eric P. Xing, and William W. Cohen. 2008. Joint latent topic models for text and citations. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Las Vegas, Nev., Aug.). 542--550. Google ScholarDigital Library
- H. Permuter, J. Francos, and H. Jermyn. 2010. Gaussian mixture models of texture and colour for image database. Proceedings of ICASSP 3, 25--88.Google Scholar
- Peter Pirolli. 2007. Information foraging theory. Journal of the American Society for Information Science 8 Technology 61, 2161--2164.Google Scholar
- Peter Pirolli and Stuart Card. 1999. Information foraging. Psychological Review 106, 643--675.Google ScholarCross Ref
- P. S. Raju. 1980. Optimum stimulation level: Its relationship to personality, demographics, and exploratory behavior. Journal of Consumer Research 7, 272--282.Google ScholarCross Ref
- Steffen Rendle and Christoph Freudenthaler. 2014. Improving pairwise learning for item recommendation from implicit feedback. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining. ACM, 273--282. Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, 452--461. Google ScholarDigital Library
- Douglas A. Reynolds. 1995. Speaker identification and verification using gaussian mixture speaker models ☆. Speech Communication 17, 91--108. Google ScholarDigital Library
- 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
- Benjamin Schuster Böckler and Alex Bateman. 2007. An introduction to hidden markov models. IEEE ASSP Magazine Appendix 3, 4--16.Google Scholar
- Kent Shi and Kamal Ali. 2012. GetJar mobile application recommendations with very sparse datasets. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 204--212. Google ScholarDigital Library
- Jan Benedict E. M. Steenkamp, and Hans Baumgartner. 1992. The role of optimum stimulation level in exploratory consumer behavior. Journal of Consumer Research 19, 434--448.Google ScholarCross Ref
- Jan Benedict E. M. Steenkamp and Steven M. Burgess. 2002. Optimum stimulation level and exploratory consumer behavior in an emerging consumer market. International Journal of Research in Marketing 19, 131--150.Google ScholarCross Ref
- Gábor Takács and Domonkos Tikk. 2012. Alternating least squares for personalized ranking. In Proceedings of the 6th ACM Conference on Recommender Systems. ACM, 83--90. Google ScholarDigital Library
- Hans-Georg Voss and Heidi Keller. 2013. Curiosity and exploration: Theories and results. Elsevier.Google Scholar
- Chong Wang and David M. Blei. 2011. Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 448--456. Google ScholarDigital Library
- Xiaolong Wang, Chengxiang Zhai, and Dan Roth. 2013. Understanding evolution of research themes: A probabilistic generative model for citations. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1115--1123. Google ScholarDigital Library
- Andrew D. Wilson and Aaron F. Bobick. 2010. Parametric hidden markov models for gesture recognition. IEEE Transactions on Pattern Analysis 8 Machine Intelligence 21, 884--900. Google ScholarDigital Library
- Hao Xu, Jingdong Wang, Xian Sheng Hua, and Shipeng Li. 2009. Tag refinement by regularized LDA. In Proceedings of the 17th International Conference on Multimedia 2009 (Vancouver, British Columbia, Canada, Oct. 19--24). 573--576. Google ScholarDigital Library
- Hongzhi Yin, Bin Cui, Yizhou Sun, Zhiting Hu, and Ling Chen. 2014. LCARS: A spatial item recommender system. ACM Transactions on Information Systems (TOIS) 32, 11. 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 ACM International Conference on Web Search 8 Data Mining. 395--404. Google ScholarDigital Library
- Quan Yuan, Gao Cong, and Chin Yew Lin. 2014. COM: A generative model for group recommendation. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery 8 Data Mining. 163--172. Google ScholarDigital Library
- Jing Zhang, Jie Tang, Honglei Zhuang, Wing Ki Leung, and Juanzi Li. 2014. Role-aware conformity influence modeling and analysis in social networks. In Proceedings of the 28th AAAI Conference on Artificial Intelligence. Google ScholarDigital Library
- Hengshu Zhu, Hui Xiong, Yong Ge, and Enhong Chen. 2014. Mobile app recommendations with security and privacy awareness. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 951--960. Google ScholarDigital Library
- Marvin Zuckerman. 1979. Sensation seeking: Beyond optimal level of arousal. (Hillsdale, N.J. and New York). Psychology Press.Google Scholar
Index Terms
- Mining Exploratory Behavior to Improve Mobile App Recommendations
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 ...
Bidirectional sensing of user preferences and application changes for dynamic mobile app recommendations
AbstractRecent years have witnessed the rapid adoption of mobile devices and significant growth in the use of mobile apps. However, the large number of mobile apps makes it difficult for users to determine which ones are of interest. Current app ...
Comments