Abstract
In this paper, we propose and investigate the effectiveness of fully decentralized, collaborative filtering techniques. These are particularly interesting for use in pervasive systems of small devices with limited communication and computational capabilities. In particular, we assume that items are tagged with smart tags (such as passive RFIDs), storing aggregate information about the visiting patterns of users that interacted with them in the past. Users access and modify information stored in smart tags transparently, by smart reader devices that are already available on commercial mobile phones. Smart readers use private information about previous behavior of the user and aggregate information retrieved from smart tags to recommend new items that are more likely to meet user expectations. Note that we do not assume any transmission capabilities between smart tags: Information exchange among them is mediated by users’ collective and unpredictable navigation patterns. Our algorithms do not require any explicit interaction among users and can be easily and efficiently implemented. We analyze their theoretical behavior and assess their performance in practice, by simulation on both synthetic and real, publicly available data sets. We also compare the performance of our fully decentralized solutions with that of state-of-the-art centralized strategies.
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Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6): 734–749
Airbus Signs Contract for High-Memory RFID Tags, RFID J (2010) http://www.rfidjournal.com/article/view/7323
Alon N, Awerbuch B, Azar Y, Patt-Shamir B (2006) Tell me who i am: an interactive recommendation system, SPAA ’06. Proceedings of the eighteenth annual ACM symposium on parallelism in algorithms and architectures, ACM, New York, NY, USA, pp 1–10
Amazon Web site (2008) http://www.amazon.com
Awerbuch B, Patt-Shamir B, Peleg D, Tuttle MR (2005) Improved recommendation systems, SODA, SIAM, pp 1174–1183
Azar Y, Fiat A, Karlin AR, McSherry F, Saia J (2001) Spectral analysis of data, STOC, pp 619–626
Babaoglu O, Canright G, Deutsch A, Caro GAD, Ducatelle F, Gambardella LM, Ganguly N, Jelasity M, Montemanni R, Montresor A, Urnes T (2006) Design patterns from biology for distributed computing. ACM Trans Auton Adapt Syst 1(1): 26–66
Badrul S, George K, Joseph K, John R (2001) Item-based collaborative filtering recommendation algorithms, WWW ’01. Proceedings of the 10th international conference on World Wide Web, ACM, pp 285–295
Baeza-Yates R, Ribeiro-Neto B (1999) Modern information retrieval, Addison Wesley. http://www.amazon.ca/exec/obidos/redirect?tag=citeulike04-20&path=ASIN/020139829X
Berkovsky S, Kuflik T, Ricci F (2007) Distributed collaborative filtering with domain specialization, RecSys ’07. Proceedings of the 2007 ACM conference on recommender systems, ACM, New York, NY, USA, pp 33–40
Bezerra1 BLD, de Assis Tenorio de Carvalho F (2010) Symbolic data analysis tools for recommendation systems. Knowl Inf Syst (on-line)
Cormode G, Muthukrishnan S (2005) What’s hot and what’s not: tracking most frequent items dynamically. ACM Trans Database Syst 30(1): 249–278
Cöster R, Svensson M (2005) Incremental collaborative filtering for mobile devices, SAC ’05. Proceedings of the 2005 ACM symposium on applied computing, ACM, New York, NY, USA, pp 1102–1106
Decker C, Kubach U, Beigl M (2003) Revealing the retail black box by interaction sensing, ICDCS Workshops, pp 328–333
Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans Inf Syst 22(1): 143–177
Drineas P, Kerenidis I, Raghavan P (2002) Competitive recommendation systems, STOC, pp 82–90
Fog A (2008) Sampling methods for wallenius’ and fisher’s noncentral hypergeometric distributions. Commun Stat Simul Comput 37(2): 241–257
Gori M, Pucci A (2007) Itemrank: a random-walk based scoring algorithm for recommender engines, IJCAI’07. Proceedings of the 20th international joint conference on artificial intelligence. Morgan Kaufmann Publishers Inc., pp 2766–2771
Huang Z, Zeng D, Chen H (2007) A comparison of collaborative-filtering recommendation algorithms for E-commerce. IEEE Intell Syst 22(5): 68–78
ki Leung CW, fai Chan SC, Chung F-L (2006) A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. Knowl Inf Syst 10(3): 357–381
Kleinberg JM, Sandler M (2003) Convergent algorithms for collaborative filtering. ACM Conference on Electronic Commerce, ACM, pp 1–10
Kourouthanasis P, Spinellis D, Roussos G, Giaglis G (2002) Intelligent cokes and diapers: MyGrocer ubiquitous computing environment. First international mobile business conference, pp 150–172. http://www.spinellis.gr/pubs/conf/2002-MBus-MyGrocer/html/paper.htm
Kumar R, Raghavan P, Rajagopalan S, Tomkins A (2001) Recommendation systems: a probabilistic analysis. J Comput Syst Sci 63(1): 42–61
Linden G, Smith B, York J (2003) Industry report: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Distrib Syst 4(1) (Online)
Mamei M, Zambonelli F (2007) Pervasive pheromone-based interaction with rfid tags. ACM Trans Auton Adapt Syst 2(2): 4
Meyer, CD (eds) (2000) Matrix analysis and applied linear algebra. Society for Industrial and Applied Mathematics, Philadelphia
Miller BN, Albert I, Lam SK, Konstan JA, Riedl J (2003) Movielens unplugged: experiences with an occasionally connected recommender system. Intelligent User Interfaces, ACM, pp 263–266
Mitzenmacher M, Upfal E (2005) Probability and computing : randomized algorithms and probabilistic analysis. Cambridge University Press. http://www.amazon.ca/exec/obidos/redirect?tag=citeulike04-20&path=ASIN/0521835402
Muthukrishnan (2005) Data streams: algorithms and applications, foundations and trends in theoretical computer science, vol 1. Now Publishers or World Scientific
Netflix Web site (2008) http://www.netflix.com
Rosset S, Perlich C, Zadrozny B (2007) Ranking-based evaluation of regression models. Knowl Inf Syst 12(3): 331–353
Roth M, Wicker S (2003) Termite: ad-hoc networking with stigmergy, Global Telecommunications Conference, 2003. GLOBECOM ’03, vol 5. IEEE 5:2937–2941
Tamine-Lechani L, Boughanem M, Daoudontact M (2009) Evaluation of contextual information retrieval effectiveness: overview of issues and research. Knowl Inf Syst (on-line)
Wade W (2003) A grocery cart that holds bread, butter, and preferences. New York Times, January 16
Wang J, Pouwelse J, Lagendijk R, Reinders MJT (2006) Distributed collaborative filtering for peer-to-peer file sharing systems. 21st annual ACM symposium on applied computing, pp 1026–1030
Xie B, Han P, Yang F, Shen R, Zeng H-J, Chen Z (2007) DCFLA: a distributed collaborative-filtering neighbor-locating algorithm. Inf Sci 177(6): 1349–1363
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Becchetti, L., Colesanti, U.M., Marchetti-Spaccamela, A. et al. Recommending items in pervasive scenarios: models and experimental analysis. Knowl Inf Syst 28, 555–578 (2011). https://doi.org/10.1007/s10115-010-0338-4
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DOI: https://doi.org/10.1007/s10115-010-0338-4