A Video-Based Automated Recommender (VAR) System for Garments

References

• Abboud B, Davoine F, Dang M (2004) Facial expression recognition and synthesis based on an appearance model. Signal Processing: Image Comm. 19(8):723–740.Crossref, Google Scholar
• Abraham-Murali L, Littrell MA (1995) Consumers’ conceptualization of apparel attributes. Clothing Textiles Res. J. 13(2):65–74.Crossref, Google Scholar
• Adomavicius G, Kwon Y (2007) New recommendation techniques for multicriteria rating systems. IEEE Intelligent Systems 22(3):48–55.Crossref, Google Scholar
• Adomavicius G, Manouselis N, Kwon Y (2011) Multi-criteria recommender systems. Ricci R, Rokach L, Shapira B, Kantor PB, eds. Recommender Systems Handbook (Springer, New York), 769–803.Crossref, Google Scholar
• Aljukhadar M, Senecal S, Daoust CE (2012) Using recommendation agents to cope with information overload. Internat. J. Electronic Commerce 17(2):41–70.Crossref, Google Scholar
• Ariely D, Lynch JG Jr, Aparicio M IV (2004) Learning by collaborative and individual-based recommendation agents. J. Consumer Psych. 14(1):81–95.Crossref, Google Scholar
• Basil M (2011) Use of photography and video in observational research. Qualitative Market Res. 14(3):246–257.Crossref, Google Scholar
• Belk R (2011) Examining markets, marketing, consumers, and society through documentary films. J. Macromarketing 31(4):403–409.Crossref, Google Scholar
• Belk RW, Kozinets RV (2005) Videography in marketing and consumer research. Qualitative Market Res. 8(2):128–141.Crossref, Google Scholar
• Bell RM, Koren Y (2007) Scalable collaborative filtering with jointly derived neighborhood interpolation weights. Proc. Seventh IEEE Internat. Conf. Data Mining (IEEE, Omaha, NE), 43–52.Crossref, Google Scholar
• Bloch PH (1995) Seeking the ideal form: Product design and consumer response. J. Marketing 59(3):16–29.Crossref, Google Scholar
• Bobadilla J, Ortega F, Hernando A, Bernal J (2012) A collaborative filtering approach to mitigate the new user cold start problem. Knowledge-Based Systems 26:225–238.Crossref, Google Scholar
• Chavan UB, Kulkarni DB (2013) Facial expression recognition—Review. Internat. J. Latest Trends Engrg. Tech. 3(1):237–243.Google Scholar
• Cohen I, Sebe N, Garg A, Chen LS, Huang TS (2003) Facial expression recognition from video sequences: Temporal and static modeling. Comput. Vision Image Understanding 91(1):160–187.Crossref, Google Scholar
• Cordier F, Seo H, Magnenat-Thalmann N (2003) Made-to-measure technologies for an online clothing store. IEEE Comput. Graphics Appl. 23(1):38–48.Crossref, Google Scholar
• DeSarbo WS, Ramaswamy V, Cohen SH (1995) Market segmentation with choice-based conjoint analysis. Marketing Lett. 6(2):137–147.Crossref, Google Scholar
• Desrosiers C, Karypis G (2011) A comprehensive survey of neighborhood-based recommendation methods. Ricci R, Rokach L, Shapira B, Kantor PB, eds. Recommender Systems Handbook (Springer, New York), 107–144.Crossref, Google Scholar
• Ding M, Hauser JR, Dong S, Dzyabura D, Yang Z, Su C, Gaskin S (2011) Unstructured direct elicitation of decision rules. J. Marketing Res. 48(1):116–127.Crossref, Google Scholar
• Dong S, Ding M, Huber J (2010) A simple mechanism to incentive-align conjoint experiments. Internat. J. Res. Marketing 27(1):25–32.Crossref, Google Scholar
• Dzyabura D, Hauser JR (2011) Active machine learning for consideration heuristics. Marketing Sci. 30(5):801–819.Link, Google Scholar
• Eckman M, Damhorst ML, Kadolph SJ (1990) Toward a model of the in-store purchase decision process: Consumer use of criteria for evaluating women’s apparel. Clothing Textiles Res. J. 8(2):13–22.Crossref, Google Scholar
• Ekman P (1994) Strong evidence for universals in facial expression: A reply to Russell’s mistaken critique. Psych. Bull. 115(2):268–287.Crossref, Google Scholar
• Ekman P, Friesen WV (1971) Constants across cultures in the face and emotion. J. Personality Soc. Psych. 17(2):124–129.Crossref, Google Scholar
• Fang F, Chen YE, Lu H (2013) Research on building the preference model of seamless badminton sportswear based on conjoint analysis. Advanced Materials Res. 796:462–467.Crossref, Google Scholar
• Fasel B, Luettin J (2003) Automatic facial expression analysis: A survey. Pattern Recognition 36(1):259–275.Crossref, Google Scholar
• Franke GR, Park JE (2006) Salesperson adaptive selling behavior and customer orientation: A meta-analysis. J. Marketing Res. 43(4):693–702.Crossref, Google Scholar
• Frijda NH (1986) The Emotions (Cambridge University Press, Cambridge, UK).Google Scholar
• Frijda NH, Zeelenberg M (2001) Appraisal: What is the dependent? Scherer KR, Schorr A, Johnstone T, eds. Appraisal Processes in Emotion Theory, Methods, Research (Oxford University Press, New York), 141–155.Google Scholar
• Georghiades AS, Belhumeur PN, Kriegman D (2001) From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Analysis Machine Intelligence 23(6):643–660.Crossref, Google Scholar
• Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Comm. ACM 35(12):61–70.Crossref, Google Scholar
• Goldberg K, Roeder T, Gupta D, Perkins C (2001) Eigentaste: A constant time collaborative filtering algorithm. Inform. Retrieval 4(2):133–151.Crossref, Google Scholar
• Green PE, Srinivasan V (1990) Conjoint analysis in marketing: New developments with implications for research and practice. J. Marketing 54(4):3–19.Crossref, Google Scholar
• Grohmann B, Spangenberg ER, Sprott DE (2007) The influence of tactile input on the evaluation of retail product offerings. J. Retailing 83(2):237–245.Crossref, Google Scholar
• Gupta P, Goel A, Lin J, Sharma A, Wang D, Zadeh RB (2013) WTF: The who-to-follow system at Twitter. Proc. 22nd Internat. Conf. World Wide Web (ACM, New York), 505–514.Crossref, Google Scholar
• Hauser JR, Toubia O, Evgeniou T, Befurt R, Dzyabura D (2010) Disjunctions of conjunctions, cognitive simplicity, and consideration sets. J. Marketing Res. 47(3):485–496.Crossref, Google Scholar
• Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans. Inform. Systems 22(1):5–53.Crossref, Google Scholar
• Holbrook MB (1983) Using a structural model of halo effect to assess perceptual distortion due to affective overtones. J. Consumer Res. 10(2):247–252.Crossref, Google Scholar
• Hui SK, Fader PS, Bradlow ET (2009a) Path data in marketing: An integrative framework and prospectus for model-building. Marketing Sci. 28(2):320–335.Link, Google Scholar
• Hui SK, Fader PS, Bradlow ET (2009b) The traveling salesman goes shopping: The systematic deviations of grocery paths from TSP-optimality. Marketing Sci. 28(3):566–572.Link, Google Scholar
• Hui SK, Huang Y, Suher J, Inman JJ (2013) Deconstructing the “first moment of truth:” Understanding unplanned consideration and purchase conversion using in-store video tracking. J. Marketing Res. 50(4):445–462.Crossref, Google Scholar
• Jones MJ, Rehg JM (2002) Statistical color models with application to skin detection. Internat. J. Comput. Vision 46(1):81–96.Crossref, Google Scholar
• Karypis G (2001) Evaluation of item-based top-n recommendation algorithms. Proc. Tenth Internat. Conf. Inform. Knowledge Management (ACM, New York), 247–254.Crossref, Google Scholar
• Kim HJ, Park YH, Bradlow E, Ding M (2014) PIE: A holistic preference concept and measurement model. J. Marketing Res. 51(3):335–351.Crossref, Google Scholar
• Klatzky RL, Loomis JM, Lederman SJ, Wake H, Fujita N (1993) Haptic identification of objects and their depictions. Perception Psychophysics 54(2):170–178.Crossref, Google Scholar
• Klein J, Gorton I, Ernst N, Donohoe P, Pham K, Matser C (2015) Performance evaluation of NoSQL databases: A case study. Proc. 1st Workshop on Performance Anal. Big Data Systems (ACM, New York), 5–10.Crossref, Google Scholar
• Klems M, Bermbach D, Weinert R (2012) A runtime quality measurement framework for cloud database service systems. Proc. Eighth Internat. Conf. Quality Inform. Comm. Tech. (IEEE, Lisbon, Portugal), 38–46.Crossref, Google Scholar
• Kovač J, Peer P, Solina F (2003) Human skin color clustering for face detection. Zajc B, Tkalčič M, eds. EUROCON 2003, Comput. Tool, IEEE Region 8 (IEEE, Piscataway, NJ), 144–148.Crossref, Google Scholar
• Kozinets RV, Belk WR (2006) Videography. Jupp V, ed. The Sage Dictionary of Social Research Methods (Sage Publications, London), 318–320.Crossref, Google Scholar
• Krishna A, ed. (2009) Sensory Marketing: Research on the Sensuality of Products (Psychology Press, New York), 17–63.Google Scholar
• Kruppa H, Castrillon-Santana M, Schiele B (2003) Fast and robust face finding via local context. Proc. Joint IEEE Internat. Workshop Visual Surveillance Performance Evaluation Tracking Surveillance (IEEE, Nice, France), 157–164.Google Scholar
• Kuch J, Huang T (1995) Vision-based hand modeling and tracking for virtual teleconferencing and telecollaboration. Proc. Internat. Conf. Comput. Vision (IEEE, Cambridge, MA), 666–671.Crossref, Google Scholar
• Lee BK, Lee WN (2004) The effect of information overload on consumer choice quality in an online environment. Psych. Marketing 21(3):159–183.Crossref, Google Scholar
• Lee N, Broderick AJ (2007) The past, present and future of observational research in marketing. Qualitative Market Res. 10(2):121–129.Crossref, Google Scholar
• Li C, Kitani KM (2013) Pixel-level hand detection in ego-centric videos. Proc. 2013 IEEE Conf. Comput. Vision Pattern Recognition (IEEE, Portland, OR), 3570–3577.Crossref, Google Scholar
• Liu YJ, Zhang DL, Yuen MMF (2010) A survey on CAD methods in 3D garment design. Comput. Indust. 61(6):576–593.Crossref, Google Scholar
• Lyon D (2001) Surveillance Society: Monitoring Everyday Life (McGraw-Hill Education, Maidenhead, UK).Google Scholar
• Ma L, Khorasani K (2004) Facial expression recognition using constructive feed forward neural networks. IEEE Trans. Systems, Man, Cybernetics, Part B: Cybernetics 34(3):1588–1595.Crossref, Google Scholar
• Manouselis N, Costopoulou C (2007) Experimental analysis of design choices in multiattribute utility collaborative filtering. Internat. J. Pattern Recognition Artificial Intelligence 21(2):311–332.Crossref, Google Scholar
• McCabe DB, Nowlis SM (2003) The effect of examining actual products or product descriptions on consumer preference. J. Consumer Psych. 13(4):431–439.Crossref, Google Scholar
• Miller S (2013) GfK adds facial coding to ad testing system. ResearchLive (February 25), http://www.research-live.com/news/technology/gfk-adds-facial-coding-to-ad-testing-system/4009252.article.Google Scholar
• Mitra S, Acharya T (2007) Gesture recognition: A survey. IEEE Trans. Systems, Man, Cybernetics, Part C: Appl. Rev. 37(3):311–324.Crossref, Google Scholar
• Narayan V, Rao VR, Saunders C (2011) How peer influence affects attribute preferences: A Bayesian updating mechanism. Marketing Sci. 30(2):368–384.Link, Google Scholar
• Netzer O, Toubia O, Bradlow ET, Dahan E, Evgeniou T, Feinberg FM, Feit Eet al. (2008) Beyond conjoint analysis: Advances in preference measurement. Marketing Lett. 19(3/4):337–354.Crossref, Google Scholar
• Ojala T, Pietikinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distribution. Pattern Recognition 29(1):51–59.Crossref, Google Scholar
• Oliver N, Pentland A, Berard F (2000) LAFTER: A real-time face and lips tracker with facial expression recognition. Pattern Recognition 33(8):1369–1382.Crossref, Google Scholar
• Peck J, Childers TL (2003) To have and to hold: The influence of haptic information on product judgments. J. Marketing 67(2):35–48.Crossref, Google Scholar
• Peck J, Shu SB (2009) The effect of mere touch on perceived ownership. J. Consumer Res. 36(3):434–447.Crossref, Google Scholar
• Peck J, Wiggins J (2006) It just feels good: Customers’ affective response to touch and its influence on persuasion. J. Marketing 70(4):56–69.Crossref, Google Scholar
• Pham MT (1998) Representativeness, relevance, and the use of feelings in decision making. J. Consumer Res. 25(2):144–159.Crossref, Google Scholar
• Raouzaiou A, Tsapatsoulis N, Tzouvaras V, Stamou G, Kollias S (2002) A hybrid intelligence system for facial expression recognition. Proc. Eur. Sympos. Intelligent Technologies, Hybrid Systems Implementation Smart Adaptive Systems (Verlag Mainz, Albufeira, Portugal), 482–490.Google Scholar
• Rashid AM, Albert I, Cosley D, Lam SK, McNee SM, Konstan JA, Riedl J (2002) Getting to know you: Learning new user preferences in recommender systems. Proc. 7th Internat. Conf. Intelligent User Interfaces (ACM, New York), 127–134.Crossref, Google Scholar
• Resnick P, Varian HR (1997) Recommender systems. Comm. ACM 40(3):56–58.Crossref, Google Scholar
• Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) GroupLens: An open architecture for collaborative filtering of netnews. Proc. ACM Conf. Comput. Supported Cooperative Work (ACM, New York), 175–186.Crossref, Google Scholar
• Roseman IJ, Antoniou AA, Jose PE (1996) Appraisal determinants of emotions: Constructing a more accurate and comprehensive theory. Cognitive Emotion 10(2):41–77.Google Scholar
• Rowley HA, Baluja S, Kanade T (1998) Neural network-based face detection. IEEE Trans. Pattern Anal. Machine Intelligence 20(1):23–38.Crossref, Google Scholar
• Russell JA, Fernandez-Dols JM (1997) The Psychology of Facial Expression (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Sahoo N, Krishnan R, Duncan G, Callan JP (2006) Collaborative filtering with multi-component rating for recommender systems. Sinha A, Venkataraman R, eds. Proc. 16th Workshop Inform. Technologies Systems, Milwaukee.Google Scholar
• Saxe D, Foulds R (1996) Toward robust skin identification in video images. Proc. Internat. Conf. Automatic Face Gesture Recognition (IEEE, Los Alamitos, CA), 379–384.Crossref, Google Scholar
• Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. Brusilovsky P, Kobsa A, Nejdl W, eds. The Adaptive Web, Lecture Notes Comput. Sci., Vol. 4321 (Springer-Verlag, Berlin Heidelberg), 291–324.Crossref, Google Scholar
• Scheibehenne B, Greifeneder R, Todd PM (2010) Can there ever be too many options? A meta-analytic review of choice overload. J. Consumer Res. 37(3):409–425.Crossref, Google Scholar
• Schwarz N, Clore GL (1983) Mood, misattribution, and judgments of well-being: Informative and directive functions of affective states. J. Personality Soc. Psych. 45(3):513–523.Crossref, Google Scholar
• Schwarz N, Clore GL (1988) How do I feel about it? The informative function of affective states. Fiedler K, Forgas J, eds. Affect, Cognition, and Social Behavior (Hogrefe, Zurich), 44–62.Google Scholar
• Shan C, Gong S, McOwan PW (2009) Facial expression recognition based on local binary patterns: A comprehensive study. Image Vision Comput. 27(6):803–816.Crossref, Google Scholar
• Shan S, Gao W, Cao B, Zhao D (2003) Illumination normalization for robust face recognition against varying lighting conditions. Proc. IEEE Internat. Workshop Anal. Modeling Faces Gestures (IEEE, Nice, France), 157–164.Google Scholar
• Shergill GS, Sarrafzadeh A, Diegel O, Shekar A (2008) Computerized sales assistants: The application of computer technology to measure consumer interest—A conceptual framework. J. Electronic Commerce Res. 9(2):176–191.Google Scholar
• Srinivasan V, Park CS (1997) Surprising robustness of the self-explicated approach to customer preference structure measurement. J. Marketing Res. 34(5):286–291.Crossref, Google Scholar
• Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv. Artificial Intelligence 2009:Article 421425.Crossref, Google Scholar
• Sung KK, Poggio T (1998) Example-based learning for view-based human face detection. IEEE Trans. Pattern Anal. Machine Intelligence 20(1):39–51.Crossref, Google Scholar
• Swan JE, Bowers MR, Richardson LD (1999) Customer trust in the salesperson: An integrative review and meta-analysis of the empirical literature. J. Bus. Res. 44(2):93–107.Crossref, Google Scholar
• Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. Image Processing, IEEE Trans. Biometrics Compendium 19(6):1635–1650.Crossref, Google Scholar
• Teixeira T, Picard R, el Kaliouby R (2014) Why, when, and how much to entertain consumers in advertisements? A web-based facial tracking field study. Marketing Sci. 33(6):809–827.Link, Google Scholar
• Teixeira T, Wedel M, Pieters R (2012) Emotion-induced engagement in Internet video advertisements. J. Marketing Res. 49(2):144–159.Crossref, Google Scholar
• Tian Y, Kanade T, Cohn JF (2011) Facial expression recognition. Jain AK, Li SZ, eds. Handbook of Face Recognition (Springer, London), 487–519.Crossref, Google Scholar
• Turk M, Pentland A (1991) Eigenfaces for recognition. J. Cognitive Neurosci. 3(1):71–86.Crossref, Google Scholar
• Valizade S, Heil O, Jedidi K (2014) The impact of retailer promotional activities on store traffic—A video-based technology. Working paper, Columbia University, New York.Google Scholar
• Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. Proc. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recognition, Vol. 1 (IEEE, Kauai, HI), 511–518.Crossref, Google Scholar
• Viola P, Jones M (2004) Robust real-time face detection. Internat. J. Comput. Vision 57(2):137–154.Crossref, Google Scholar
• Wang H, Ahuja N (2003) Facial expression decomposition. Proc, Ninth IEEE Internat. Conf., Comput. Vision (IEEE, Nice, France), 958–965.Crossref, Google Scholar
• Weitz BA, Sujan H, Sujan M (1986) Knowledge, motivation, and adaptive behavior: A framework for improving selling effectiveness. J. Marketing 50(4):174–191.Crossref, Google Scholar
• Wyer RS, Carlston DE (1979) Social Cognition, Inference and Attribution (Psychology Press, Hillsdale, NJ).Google Scholar
• Xiao L, Ding M (2014) Just the faces: Exploring the effects of facial features in print advertising. Marketing Sci. 33(3):338–352.Link, Google Scholar
• Xiao L, Kim H, Ding M (2013) An introduction to audio and visual research and applications in marketing. Rev. Marketing Res. 10:213–253.Crossref, Google Scholar
• Yazdanparast A, Spears N (2012) Need for touch and information processing strategies: An empirical examination. J. Consumer Behav. 11(5):415–421.Crossref, Google Scholar
• Yoruk E, Konukoglu E, Sankur B, Darbon J (2006) Shape-based hand recognition. IEEE Trans. Image Processing 15(7):1803–1815.Crossref, Google Scholar
• Zeelenberg M, Pieters R (2004) Beyond valence in customer dissatisfaction: A review and new findings on behavioral responses to regret and disappointment in failed services. J. Bus. Res. 57(4):445–455.Crossref, Google Scholar
• Zhang X, Li S, Burke R (2014) An examination of social influence on shopper behavior using video tracking data. J. Marketing 78(5):24–41.Crossref, Google Scholar
• Zhang Z, Li Y, Gong C, Wu H (2002) Casual wear product attributes: A Chinese consumers’ perspective. J. Fashion Marketing Management 6(1):53–62.Crossref, Google Scholar
• Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: A literature survey. ACM Comput. Surveys 35(4):399–458.Crossref, Google Scholar
• Zhu X, Yang J, Waibel A (2000) Segmenting hands of arbitrary color. Proc. IEEE Internat. Conf. Automatic Face Gesture Recognition (IEEE, Grenoble, France), 446–453.Google Scholar