Felix von Reischach
Florian Michahelles
Albrecht Schmidt
ABSTRACT
Customer reviews and recommendations for products are provided by almost all e-business platforms, supporting consumers when shopping on the web. Mobile and ubiquitous computing provide extended means to sense input data for recommendations and to make recommendations available for consumers when shopping in traditional stores. This work contributes a comprehensive design space that outlines design options for product recommendation systems using mobile and ubiquitous technologies. A visual notation for the design space is proposed, based on which existing systems are categorized. Blank spaces are identified and concrete possible extensions are proposed by the example of an existing mobile product recommendation system. Finally, general options for future research on product recommendation systems using UbiComp technologies are discussed.
- G. D. Abowd, C. G. Atkeson, J. Hong, S. Long, R. Kooper, and M. Pinkerton. Cyberguide: A mobile context-aware tourguide. Wireless Networks, 3(5):421--433, 1997. Google Scholar
Digital Library
- G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6):734--749, 2005. Google ScholarDigital Library
- A. Asthana, M. Cravatts, and P. Krzyzanowski. An indoor wireless system for personalized shopping assistance. In Proceedings of the Workshop on Mobile Computing Systems and Applications, 1994. Google ScholarDigital Library
- F. Bellotti, R. Berta, A. D. Gloria, and M. Margarone. User testing a hypermedia tour guide. IEEE Pervasive Computing, 1(2):33--41, 2002. Google ScholarDigital Library
- V. Bellotti, B. Begole, E. H. Chi, N. Ducheneaut, J. Fang, E. Isaacs, T. King, M. W. Newman, K. Partridge, B. Price, P. Rasmussen, M. Roberts, D. J. Schiano, and A. Walendowski. Activity-based serendipitous recommendations with the magitti mobile leisure guide. In Proceedings of the SIGCHI conference on Human factors in computing systems (CHI '08), 2008. Google ScholarDigital Library
- A. B. Brody and E. J. Gottsman. Pocket bargainfinder: A handheld device for augmented commerce. In First International Symposium on Handheld and Ubiquitous Computing (HUC99), 1999. Google ScholarDigital Library
- M. Brunato and R. Battini. PILGRIM: A location broker and mobility-aware recommendation system. In Proceedings of the 1st International Conference on Pervasive Computing and Communications (Per-Com'03), 2003. Google ScholarDigital Library
- E. Brynjolfsson and M. D. Smith. Frictionless commerce? a comparison of internet and conventional retailers. Management Science, 46(4):563--585, 2000.Google ScholarCross Ref
- A. Chen. Context-aware collaborative filtering system: Predicting the users preference in the ubiquitous computing environment. In Proceedings of the International Symposium on Location and Context Awareness (LoCA '05), 2005. Google ScholarDigital Library
- K. Cheverst, N. Davies, K. Mitchell, A. Friday, and C. Efstratiou. Developing a context-aware electronic tourist guide: some issues and experiences. In Proceedings of the SIGCHI conference on Human factors in computing systems (CHI'00), 2000. Google ScholarDigital Library
- A. M. Degeratu, A. Rangaswamy, and J. Wu. Consumer choice behavior in online and traditional supermarkets: The effects of brand name, price, and other search attributes. International Journal of Research in Marketing, 17(1):55--78, 2000.Google ScholarCross Ref
- C. Dellarocas. The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science, 49(10):1407--1424, 2003. Google ScholarDigital Library
- L. Deng and L. Cox. Livecompare: Grocery bargain hunting through participatory sensing. In Proceedings of the 10th International Workshop on Mobile Computing Systems and Applications (HotMobile '09), 2009. Google ScholarDigital Library
- N. Eagle and A. Pentland. Social serendipity: mobilizing social software. IEEE Pervasive Computing, 4(2):28--34, 2005. Google ScholarDigital Library
- D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12):61--70, 1992. Google ScholarDigital Library
- P. Gratz, A. Andronache, and S. Rothkugel. Ad hoc collaborative filtering for mobile networks. International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, 0:355--360, 2008. Google ScholarDigital Library
- S. Guven, O. Oda, M. Podlaseck, H. Stavropoulos, S. Kolluri, and G. Pingali. Social mobile augmented reality for retail. In Adjunct Proceedings of the Conference of Pervasive Computing and Communications (PerCom '09), 2009. Google ScholarDigital Library
- J. A. Howard and J. N. Sheth. Theory of Buyer Behavior. Wiley, 1969.Google Scholar
- M. Jacobsson, M. R. Mattias, and L. E. Holmquist. When media gets wise: collaborative filtering with mobile media agents. In Proceedings of the 11th international conference on Intelligent user interfaces (IUI '06), 2006. Google ScholarDigital Library
- N. Kern, S. Antifakos, B. Schiele, and A. Schwaninger. A model for human interruptability: Experimental evaluation and automatic estimation from wearable sensors. In Proceedings of the Eighth International Symposium on Wearable Computers (ISWC '04), 2004. Google ScholarDigital Library
- M. F. King and S. K. Balasubramanian. The effects of expertise, end goal, and product type on adoption of preference formation strategy. Journal of the Academy of Marketing Science, 22(2):146--159, 1994.Google ScholarCross Ref
- L. R. Klein. Evaluating the potential of interactive media through a new lens: Search versus experience goods. Journal of Business Research, 41:195--203, 1998.Google ScholarCross Ref
- P. Kourouthanassis, D. Spinellis, G. Roussos, and G. M. Giaglis. Intelligent cokes and diapers: Mygrocer ubiquitous computing environment. In Proceedings of the 1st International Mobile Business Conference, 2002.Google Scholar
- E. Lee and J. Jin. Software Engineering Research and Applications, chapter A Next Generation Intelligent Mobile Commerce System, pages 320--331. Springer, 2004.Google Scholar
- S. Levy. Do real friends share ads? Newsweek, December 2007.Google Scholar
- J. Mankoff, G. Hsieh, H. C. Hung, S. Lee, and E. Nitao. Using low-cost sensing to support nutritional awareness. In Proceedings of the 4th International Conference on Ubiquitous Computing (UbiComp '02), 2002. Google ScholarDigital Library
- C. Metzger, E. Fleisch, J. Meyer, M. Dansachmüller, I. Graz, M. Kaltenbrunner, C. Keplinger, R. Schwödiauer, and S. Bauer. Flexible-foam-based capacitive sensor arrays for object detection at low cost. Applied Physics Letters, 92(1):13--16, 2008.Google ScholarCross Ref
- B. N. Miller, I. Albert, S. K. Lam, J. A. Konstan, and J. Riedl. MovieLens unplugged: Experiences with a recommender system on four mobile devices. In Proceedings of the 8th International Conference on Intelligent User Interfaces, 2003. Google ScholarDigital Library
- B. N. Miller, J. A. Konstan, and J. Riedl. PocketLens: Toward a personal recommender system. ACM Transactions on Information Systems, 22(3):437--476, 2004. Google ScholarDigital Library
- P. Mistry, P. Maes, and L. Chang. Wuw - wear ur world - a wearable gestural interface. In Extended Abstract at the 27th Conference on Human Factors in Computing Systems, 2009. Google ScholarDigital Library
- D. M. Nichols. Implicit rating and filtering. In 5th DELOS Workshop on Filtering and Collaborative Filtering, 1997.Google Scholar
- M. Ota, H. Ishizuka, M. Hirafuji, and Y. Tobe. On measuring the degree of real-world attention. In Adjunct Proceedings of the Conference of Ubiquitous Computing (UbiComp '06), 2006.Google Scholar
- L. L. Price and L. F. Feick. The role of recommendation sources in external search: An informational perspective. Advances in Consumer Research, 11:250--255, 1984.Google Scholar
- F. Resatsch, S. Karpischek, U. Sandner, and S. Hamacher. Mobile sales assistant: NFC for retailers. In Proceedings of the 9th International Conference on Human Computer Interaction with Mobile Devices and Services (Mobile HCI '07), 2007. Google ScholarDigital Library
- F. Resatsch, U. Sandner, J. M. Leimeister, and H. Krcmar. Do point of sale RFID-based information services make a difference? Analyzing consumer perceptions for designing smart product information services in retail business. Electronic Markets, 18(3):216--231, 2008. Google ScholarDigital Library
- A. Sahami, P. Holleis, A. Schmidt, and J. Häkkilä. Rich tactile output on mobile devices. In Proceedings of the European Conference on Ambient Intelligence (AmI '08), 2008. Google ScholarDigital Library
- A. Schmidt. Implicit human computer interaction through context. Personal and Ubiquitous Computing, 4(2--3):191--199, 2000.Google Scholar
- S. Senecal and J. Nantel. Online influence of relevant others: A framework. In 6th International Conference on Electronic Commerce Research, 2003.Google Scholar
- S. Senecal and J. Nantel. The influence of online product recommendations on consumers' choices. Journal of Retailing, 80(2):159--169, 2004.Google ScholarCross Ref
- R. Shannon, M. Stabeler, A. Quigley, and P. Nixon. Profiling and targeting opportunities in pervasive advertising. In 1st Workshop on Pervasive Advertising at Pervasive Conference 2009, 2009.Google Scholar
- U. Shardanand and P. Maes. Social information filtering: algorithms for automating "word of mouth". In Proceedings of the SIGCHI conference on Human factors in computing systems (CHI '95), 1995. Google ScholarDigital Library
- L. Terveen, W. Hill, B. Amento, D. McDonald, and J. Creter. PHOAKS: a system for sharing recommendations. Communications of the ACM, 40(3):59--62, 1997. Google ScholarDigital Library
- M. van Setten, S. Pokraev, and J. Koolwaaij. Adaptive Hypermedia and Adaptive Web-Based Systems, chapter Context-Aware Recommendations in the Mobile Tourist Application COMPASS, pages 235--244. Springer, 2004.Google Scholar
- F. von Reischach, D. Guinard, F. Michahelles, and E. Fleisch. A mobile product recommendation system interacting with tagged products. In Proceedings of the 7th International Conference on Pervasive Computing and Communications (PerCom 2009), 2009. Google ScholarDigital Library
- F. von Reischach and F. Michahelles. Apriori: A ubiquitous product rating system. In Workshop on Pervasive Mobile Interaction Devices (PERMID '08) at Pervasive Conference, 2008.Google Scholar
- F. von Reischach, F. Michahelles, D. Guinard, R. Adelmann, E. Fleisch, and A. Schmidt. An evaluation of product identification techniques for mobile phones. In Proceedings of the 12th Conference in Human-Computer Interaction (Interact '09), 2009. Google ScholarDigital Library
- A. Zenebe, A. Ozok, and A. F. Norcio. Personalized recommender systems in e-commerce and m-commerce: A comparative study. In Proceedings of the 11th International Conference on Human-Computer Interaction (HCI International), 2005.Google Scholar
Index Terms
- The design space of ubiquitous product recommendation systems
Recommendations
Product recommendation approaches: Collaborative filtering via customer lifetime value and customer demands
Recommender systems are techniques that allow companies to develop one-to-one marketing strategies and provide support in connecting with customers for e-commerce. There exist various recommendation techniques, including collaborative filtering (CF), ...
Product recommendation with temporal dynamics
In many E-commerce recommender systems, a special class of recommendation involves recommending items to users in a life cycle. For example, customers who have babies will shop on Diapers.com within a relatively long period, and purchase different ...
Ubiquitous recommender systems
Ubiquitous recommender systems combine characteristics from ubiquitous systems and recommender systems in order to provide personalized recommendations to users in ubiquitous environments. Although not a new research area, ubiquitous recommender systems ...
Comments