俗語有"相由心生"，是指一個人内心的精神狀態會影響自身的容貌，這種精神狀態具體體現為對事物的理解、解釋和感受。換言之，一個人的容貌或臉孔特徵在一定程度上會揭示他們内心對事物的態度。近年來，有學者證實了1)感知者在辨識人臉和汽車前臉時採用類似的處理機制，並且證實了2)車主臉孔與汽車前臉之間具有相似性。因此，本研究旨在建構人類臉孔特徵與汽車“前臉”形態之間非綫性的關係模型，最終提出一個汽車造形推薦與設計系統。推薦系統的建構使用混合推薦演算法，包括模糊K-means聚類法和捲積神經網絡。首先，使用適合描述汽車“前臉”形態的意象形容詞對汽車“前臉”樣本進行分類。其次，邀請潛在的消費者作爲受測者進行感性偏好問卷調查，同時採集每位受測者的正面頭像。之後，基於偏好問卷的結果使用模糊K-means聚類法對受測者進行分類，進而確定出每類受測者的首要推薦形態。最後，以正面頭像和聚類結果分別作爲捲積神經網絡的輸入層數據和輸出層的標簽數據訓練獲得一個分類模型。設計系統的建構主要使用了兩種理論與方法，分別是形態摻合和二次曲率熵。首先，採集一位有意願購買某個汽車品牌的消費者的正面臉孔，並將其輸入已建構的推薦系統，從而獲得一組系統推薦的形態。另外，爲了驗證系統所推薦形態的準確性，進一步使用協同過濾演算法對推薦結果進行分析。然後，進一步使用臉孔相似性理論從推薦結果中篩選出一個最佳推薦形態。隨後，使用形態摻合演算法計算系統推薦的形態和選定汽車品牌固有的形態，從而生成一系列備選形態。最後，以平均二次曲率熵值最小的形態作爲最佳形態。研究結果顯示，基於人類正面臉孔信息成功地構建了一個汽車形態的推薦系統，並依據此推薦系統構建了一個汽車形態的設計系統。對於汽車銷售行業而言，推薦系統可以實現精準銷售的目標，進而提高銷售業勣。對於汽車造形設計而言，設計系統可以在保留原始汽車形態特徵的前提下設計出符合消費者偏好的形態。

As the saying goes, " State outside is based on mind inside" means that a person's inner mental state will affect one's appearance. This mental state is embodied in the understanding, interpretation, and feeling of things. In other words, a person's appearance or facial features will reveal/highlight/expose their inner attitude towards stuff to a certain extent. In recent years, some scholars have confirmed that 1) the perceiver adopts a similar processing mechanism when recognizing the human face and the front face of the automobile, and confirmed 2) the similarity between the face of the automobile owner and the front face of the car. Therefore, this research aims to construct a model of the non-linear relationship between the human face and the automobile's "front face" and then propose an automobile shape recommendation and design system. The recommendation system (System Ⅰ) is constructed using hybrid recommendation algorithms, including fuzzy K-means clustering and convolutional neural network (CNN). First, use a set of suitable adjectives to classify the automobile sample. Secondly, invite potential consumers as subjects to conduct a questionnaire survey of perceptual preference. Meanwhile, collect the front portrait of each subject. Subsequently, based on the results of the preference questionnaire, the fuzzy K-means clustering method was used to classify the subjects, and then the primary recommendation shape of each category of subjects was determined. Finally, the front avatar and clustering results are used as the convolutional neural network's input layer and output layer label data to obtain a classification model. The construction of the design system (System Ⅱ) mainly uses two theories and methods, namely shape blending and quadratic curvature entropy. First, collect the front face of a consumer willing to buy an automobile and input it into the constructed recommendation system to obtain a set of recommended shapes. In addition, to verify the accuracy of the recommended form of the system, the Collaborative Filtering algorithm is further used to analyze the recommended results. Then, the face similarity theory is used to select the best recommendation shape from the recommendation results. Subsequently, the shape blending algorithm calculates the shape recommended by the system and the inherent shape of the automobile brand chosen, thereby generating a series of alternative shapes. Finally, the shape with the smallest average quadratic curvature entropy is taken as the best shape. The research results show that an automobile shape recommendation system is constructed based on the human front face information. An automobile form design system is built based on this recommendation system. For the automobile sales industry, the recommendation system can achieve the goal of actual sales, thereby increasing the sales volume. The design system can design a shape that meets consumer preferences while retaining the original automobile shape characteristics for car shape design.

Aggarwal P, McGill A L. (2007). Is that car smiling at me? Schema congruity as a basis for evaluating anthropomorphized products. Journal of consumer research, 34(4): 468-479.
Alpers G.W., Gerdes A.B.M. (2006). Another look at “look-alikes” can judges match belongings with their owners?. Journal of Individual Differences, 27(1): 38-41.
Alvarez L, Jaffe K. (2004). Narcissism guides mate selection: Humans mate assortatively, as revealed by facial resemblance, following an algorithm of “self seeking like”. Evolutionary Psychology, 2(1): 177-194.
Beck J. (1966a). Effect of orientation and of shape similarity on perceptual grouping. Perception & Psychophysics, 1(5): 300-302.
Beck J. (1966b). Perceptual grouping produced by changes in orientation and shape. Science, 154(3748): 538-540.
Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35(8), 1798-1828.
Biederman, I., & Ju, G. (1988). SurFaceversus edge-based determinants of visual recognition. Cognitive psychology, 20(1), 38-64.
Blahut, R.E. (1987). Principles and practice of information theory, Addison-Wesley, Boston, United States.
Bloch P H. (1995). Seeking the ideal form: Product design and consumer response. Journal of marketing, 59(3): 16-29.
Bobadilla, J., Alonso, S., & Hernando, A. (2020). Deep learning architecture for collaborative filtering recommender systems. Applied Sciences, 10(7), 2441.
Bogdanova, G., & Georgieva, T. (2008). Using error-correcting dependencies for collaborative filtering. Data & Knowledge Engineering, 66(3), 402-413.
Burke, R. (2000). Knowledge-based recommender systems. Encyclopedia of library and information systems, 69(32), 175-186.
Celebi, M. E., & Aydin, K. (Eds.). (2016). Unsupervised learning algorithms. Berlin: Springer International Publishing.
Chandler J, Schwarz N. (2010). Use does not wear ragged the fabric of friendship: Thinking of objects as alive makes people less willing to replace them. Journal of Consumer Psychology, 20(2): 138-145.
Chen, S. E., & Parent, R. E. (1989). Shape averaging and its applications to industrial design. IEEE Computer Graphics and Applications, 9(1), 47-54.
Chen, Y. L., Yeh, Y. H., & Ma, M. R. (2021). A movie recommendation method based on users' positive and negative profiles. Information Processing & Management, 58(3), 102531.
Chen, Y.P. (2011). Visual Psychology, Futaba Book Gallery, Taiwan.
Cheng, C., Peng, C., & Zhang, T. (2020). Fuzzy K-Means Cluster Based Generalized Predictive Control of Ultra Supercritical Power Plant. IEEE Transactions on Industrial Informatics, 17(7), 4575-4583.
Cheutet V, Léon J C, Catalano C E, et al. (2008). Preserving car stylists’ design intent through an ontology. International Journal on Interactive Design and Manufacturing, 2(1): 9-16.
Chiu, M. C., Huang, J. H., Gupta, S., & Akman, G. (2021). Developing a personalized recommendation system in a smart product service system based on unsupervised learning model. Computers in Industry, 128, 103421.
Chowdhury, G. G. (2010). Introduction to modern information retrieval. Facet publishing.
Ciregan, D., Meier, U., & Schmidhuber, J. (2012). Multi-column deep neural networks for image classification. In 2012 IEEE conference on computer vision and pattern recognition (pp. 3642-3649). IEEE.
Coates D. (2002). Watches tell more than time. New York: McGraw-Hill.
Coss R G. (2003). The role of evolved perceptual biases in art and design. Springer, Berlin, Heidelberg.
Covington, P., Adams, J., & Sargin, E. (2016). Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems (pp. 191-198).
De Heering A, Rossion B. (2015). Rapid categorization of natural Faceimages in the infant right hemisphere. Elife, 4: 1-14.
De, S., Britton, J., Reynolds, M., Skinner, R., Jansen, K., & Doostan, A. (2020). On transfer learning of neural networks using bi-fidelity data for uncertainty propagation. International Journal for Uncertainty Quantification, 10(6): 1-29.
DeBruine L M. (2002). Facial resemblance enhances trust. Proceedings of the Royal Society of London. Series B: Biological Sciences, 269(1498): 1307-1312.
Deshpande, M., & Karypis, G. (2004). Item-based top-n recommendation algorithms. ACM Transactions on Information Systems, 22(1), 143-177.
DiSalvo, C., & Gemperle, F. (2003). From seduction to fulfillment: the use of anthropomorphic form in design. In Proceedings of the 2003 international conference on Designing pleasurable products and interfaces (pp. 67-72).
Du, X., He, X., Yuan, F., Tang, J., Qin, Z., & Chua, T. S. (2019). Modeling embedding dimension correlations via convolutional neural collaborative filtering. ACM Transactions on Information Systems (TOIS), 37(4), 1-22.
Ekman P. (1999). Basic emotions. Handbook of cognition and emotion, 98: 45-46.
Enlow D.H, Moyers R.E. (1982). Handbook of facial growth. WB Saunders Company.
Epley N, Waytz A, Cacioppo J T. (2007). On seeing human: a three-factor theory of anthropomorphism. Psychological review, 114(4): 864-886.
Farah M.J., Wilson K.D., Drain M., Tanaka J.N. (1998). What is "special" about Faceperception? Psychological review, 105, 482–498.
Farley, B.W.A.C., & Clark, W. (1954). Simulation of self-organizing systems by digital computer. Transactions of the IRE Professional Group on Information Theory, 4(4), 76-84.
Fathollahi, M. S., & Razzazi, F. (2021). Music similarity measurement and recommendation system using convolutional neural networks. International Journal of Multimedia Information Retrieval, 10(1), 43-53.
Feldman, J. A., & Ballard, D. H. (1982). Connectionist models and their properties. Cognitive science, 6(3), 205-254.
Gauthier I, Curran T, Curby K M, et al. (2003). Perceptual interference supports a non-modular account of Faceprocessing. Nature neuroscience, 6(4): 428-432.
Goode M R, Dahl D W, Moreau C P. (2013). Innovation aesthetics: The relationship between category cues, categorization certainty, and newness perceptions. Journal of Product Innovation Management, 30(2): 192-208.
Guthrie S E, Guthrie S. (1995). Faces in the clouds: A new theory of religion. Oxford University Press on Demand.
Hadjikhani N, Kveraga K, Naik P, et al. (2009). Early (N170) activation of face-specific cortex by face-like objects. Neuroreport, 20(4): 403.
Han, K. (2020). Personalized News Recommendation and Simulation Based on Improved Collaborative Filtering Algorithm. Complexity, 2020.
Harada, T.; Yoshimoto, F.; Moriyama, M. (1999). An aesthetic curve in the field of industrial design. In Proceedings 1999 IEEE Symposium on Visual Languages, Tokyo, Japan, pp. 38-47.
Haxby J V, Horwitz B, Ungerleider L G, et al. (1994). The functional organization of human extrastriate cortex: a PET-rCBF study of selective attention to faces and locations. Journal of Neuroscience, 14(11): 6336-6353.
Hebb, D. O. (1049). The organization of behavior: A neuropsychological theory. Psychology Press.
Hecht-Nielsen, R. (1992). Theory of the backpropagation neural network. In Neural networks for perception (pp. 65-93). Academic Press.
Herlocker, J., Konstan, J. A., & Riedl, J. (2002). An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Information retrieval, 5(4), 287-310.
Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504-507.
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8), 2554-2558.
Hsiao S.W, Chuang J.C. (2003). A reverse engineering based approach for product form design. Design Studies, 24(2): 155-171.
Hsiao, S.W., & Tsai, H.C. (2005). Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design. International journal of industrial ergonomics, 35(5), 411-428.
Hsiao, S.W., Lee, C.H., Chen, R.Q., & Lin, C.Y. (2018). A methodology for brand feature establishment based on the decomposition and reconstruction of a feature curve. Advanced Engineering Informatics, 38, 14-26.
Hu, J., Niu, H., Carrasco, J., Lennox, B., & Arvin, F. (2020). Voronoi-based multi-robot autonomous exploration in unknown environments via deep reinforcement learning. IEEE Transactions on Vehicular Technology, 69(12), 14413-14423.
Hui, K.C., & Li,Y. (1998). A feature-based shape blending technique for industrial design. Computer-Aided Design, 30(10), 823-834.
Ichikawa H, Kanazawa S, Yamaguchi M.K. (2011). Finding a Facein a face-like object. Perception, 40(4): 500-502.
Jarrett, K., Kavukcuoglu, K., Ranzato, M.A., & LeCun, Y. (2009). What is the best multi-stage architecture for object recognition?. In 2009 IEEE 12th international conference on computer vision (pp. 2146-2153). IEEE.
Jordan, M.I., & Rumelhart, D.E. (1992). Forward models: Supervised learning with a distal teacher. Cognitive science, 16(3), 307-354.
Kai, H. (2021). Automatic Recommendation Algorithm for Video Background Music Based on Deep Learning. Complexity, 2021.
Kamps F.S., Morris E.J., Dilks D.D. (2019). A Faceis more than just the eyes, nose, and mouth: fMRI evidence that face-selective cortex represents external features. NeuroImage, 184: 90-100.
Kanizsa G. (1976). Subjective contours. Scientific American, 234(4): 48-53.
Kanwisher N, McDermott J, Chun M.M. (1997). The fusiform Facearea: a module in human extrastriate cortex specialized for Faceperception. Journal of neuroscience, 17(11): 4302-4311.
Kanwisher N, Yovel G. (2006). The fusiform Facearea: a cortical region specialized for the perception of faces. Philosophical Transactions of the Royal Society B: Biological Sciences, 361(1476): 2109-2128.
Kanwisher N. (2000). Domain specificity in Faceperception. Nature neuroscience, 3(8): 759-763.
Karjalainen T.M. (2007). It looks like a Toyota: Educational approaches to designing for visual brand recognition. International Journal of design, 1(1): 67-81.
Karjalainen T.M. (2010). Snelders D. Designing visual recognition for the brand. Journal of Product Innovation Management, 27(1): 6-22.
Keaveney S.M, Herrmann A, Befurt R, et al. (2012). The eyes have it: How a car's Faceinfluences consumer categorization and evaluation of product line extensions. Psychology & Marketing, 29(1): 36-51.
Keaveney, S.M., Herrmann, A., Befurt, R., & Landwehr, J.R. (2012). The eyes have it: How a car's Faceinfluences consumer categorization and evaluation of product line extensions. Psychology & Marketing, 29(1): 36-51.
Khan, S., Rahmani, H., Shah, S.A.A., & Bennamoun, M. (2018). A guide to convolutional neural networks for computer vision. Synthesis Lectures on Computer Vision, 8(1): 1-207.
Klatt W.K, Chesham A, Lobmaier J.S. (2016). Putting up a big front: car design and size affect road-crossing behaviour. PLoS one, 11(7): 1-12.
Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., & Riedl, J. (1997). Grouplens: Applying collaborative filtering to usenet news. Communications of the ACM, 40(3): 77-87.
Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25: 1097-1105.
Kühn S, Brick T.R, Müller B.C.N, et al. (2014). Is this car looking at you? How anthropomorphism predicts fusiform Facearea activation when seeing cars. PloS one, 9(12): e113885.
Kuyama, H. (1998). Measurements of feature-parameters of signs. Science of Design, 45(2), 35-44.
Kuzelewska, U. (2014). Clustering algorithms in hybrid recommender system on movielens data. Studies in logic, grammar and rhetoric, 37(1): 125-139.
Landwehr J.R., McGill A.L., Herrmann A. (2011). It's got the look: The effect of friendly and aggressive “facial” expressions on product liking and sales. Journal of marketing, 75(3): 132-146.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553): 436-444.
LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., & Jackel, L.D. (1989). Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4): 541-551.
Lee, H., Grosse, R., Ranganath, R., & Ng, A.Y. (2009). Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th annual international conference on machine learning (pp. 609-616).
Leung, C.W.K., Chan, S.C.F., & Chung, F.L. (2006). A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. Knowledge and Information Systems, 10(3): 357-381.
Li, G., Gomez, R., Nakamura, K., & He, B. (2019). Human-centered reinforcement learning: A survey. IEEE Transactions on Human-Machine Systems, 49(4): 337-349.
Li, J., & Ye, Z. (2020). Course Recommendations in Online Education Based on Collaborative Filtering Recommendation Algorithm. Complexity, 2020.
Lops, P., De Gemmis, M., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. Recommender systems handbook, 73-105.
Mae L, McMorris L.E, Hendry J.L. (2004) Spontaneous trait transference from dogs to owners. Anthrozoös, 17(3): 225-243.
Martínez-Horta S., Horta-Barba A., Perez-Perez J, et al. (2020). Impaired face-like object recognition in premanifest Huntington's disease. Cortex, 123: 162-172.
McCormack J.P, Cagan J., Vogel C.M. (2004). Speaking the Buick language: capturing, understanding, and exploring brand identity with shape grammars. Design studies, 25(1): 1-29.
McCrae R.R, Costa Jr P.T. (1997). Personality trait structure as a human universal. American psychologist, 52(5): 509.
McCulloch, W.S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4): 115-133.
Melville, P., Mooney, R.J., & Nagarajan, R. (2002). Content-boosted collaborative filtering for improved recommendations. Aaai, 23: 187-192.
Miesler, L., Landwehr, J.R., Herrmann, A. & McGill, A. (2010). Consumer and Product Face-To-Face: Antecedents and Consequences of Spontaneous Face-Schema Activation, In C. C. Margaret, I. Jeff & R. Pieters (Eds.), Association for Consumer Research (pp. 536-537).
Mitchell, R., Michalski, J., & Carbonell, T. (2013). An artificial intelligence approach. Berlin: Springer.
Mitchell, T. M. (1997). Artificial neural networks. Machine learning, 45: 81-127.
Miyahara, K., & Pazzani, M.J. (2000). Collaborative filtering with the simple bayesian classifier. In Pacific Rim International conference on artificial intelligence (pp. 679-689). Springer, Berlin, Heidelberg.
Morewedge C.K, Preston J, Wegner D.M. (2007). Timescale bias in the attribution of mind. Journal of personality and social psychology, 93(1): 1-11.
Nihei Y, Minami T, Nakauchi S. (2018). Brain Activity Related to the Judgment of Face-Likeness: Correlation between EEG and Face-Like Evaluation. Frontiers in human neuroscience, 12: 56.
Ning, H., Li, Q. (2020). Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm. Complexity, 2020.
Norman D.A. (1988). The psychology of everyday things. Basic books.
Norman D.A. (1992). Turn signals are the facial expressions of automobiles. Cambridge, MA: Perseus Publishing.
Norman D.A. (2004). Emotional design: Why we love (or hate) everyday things. Basic Civitas Books.
Omer Y, Sapir R, Hatuka Y, et al. (2019). What Is a Face? Critical Features for Face Detection. Perception, 48(5): 437-446.
Pittenger J.B, Shaw R E, Mark L S. (1979). Perceptual information for the age level of faces as a higher order invariant of growth. Journal of Experimental Psychology: Human Perception and Performance, 5(3): 478.
Pittenger J.B, Shaw R E. (1975). Aging faces as viscal-elastic events: Implications for a theory of nonrigid shape perception. Journal of Experimental Psychology: Human perception and performance, 1(4): 374.
Pratama, B.Y., Budi, I., & Yuliawati, A. (2020). Product Recommendation in Offline Retail Industry by using Collaborative Filtering. International Journal of Advanced Computer Science and Applications, 11(9), 635-643.
Pugliese, M.J., & Cagan, J. (2002). Capturing a rebel: modeling the Harley-Davidson brand through a motorcycle shape grammar. Research in Engineering Design, 13(3), 139-156.
Purucker C, Sprott D.E, Herrmann A. (2014). Consumer response to car fronts: eliciting biological preparedness with product design. Review of Managerial Science, 8(4): 523-540.
Ranzato, M.A., Huang, F.J., Boureau, Y.L., & LeCun, Y. (2007). Unsupervised learning of invariant feature hierarchies with applications to object recognition. In 2007 IEEE conference on computer vision and pattern recognition (pp. 1-8). IEEE.
Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender systems handbook (pp. 1-35). Springer, Boston, MA.
Rochester, N., Holland, J., Haibt, L., & Duda, W. (1956). Tests on a cell assembly theory of the action of the brain, using a large digital computer. IRE Transactions on information Theory, 2(3), 80-93.
Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 386.
Roy M.M, Nicholas J.S.C. (2004). Do dogs resemble their owners?. Psychological Science, 15(5): 361-363.
Rumelhart, D.E., Hinton, G.E., & Williams, R. J. (1986). Learning internal representations by error propagation. In D. E. Rumelhart & J. L. McClelland et al. (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition (pp. 318-362). MIT Press, Cambridge.
Scheirer W.J., de Rezende Rocha A., Sapkota A., Boult T.E. (2014). Perceptual annotation: measuring human vision to improve computer vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36:1679–1686.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61: 85-117.
Schneiderman H, Kanade T. (2000). A histogram-based method for detection of faces and cars. IEEE, 3: 504-507.
Schreiner T.F., Fandrich T., Heitmann M., et al. (2017). A Novel Approach for Predicting and Understanding Consumers' Sense of Design Similarity. Journal of Product Innovation Management, 34(6): 790-820.
Schuldt J.P, Konrath S.H, Schwarz N. (2012). The right angle: Visual portrayal of products affects observers’ impressions of owners. Psychology & Marketing, 29(10): 705-711.
Schuster, M., & Paliwal, K.K. (1997). Bidirectional recurrent neural networks. IEEE transactions on Signal Processing, 45(11): 2673-2681.
Shafqat, W., & Byun, Y.C. (2021). Incorporating Similarity Measures to Optimize Graph Convolutional Neural Networks for Product Recommendation. Applied Sciences, 11(4): 1366.
Shamshoddin, S., Khader, J., & Gani, S. (2019). Predicting consumer preferences in electronic market based on IoT and Social Networks using deep learning based collaborative filtering techniques. Electronic Commerce Research, 2019: 1-18.
Shannon, C.E. (1948). A mathematical theory of communication. The Bell system technical journal, 27(3): 379-423.
Simon, H.A. (1965). Administrative decision making. Public Administration Review, 1965: 31-37.
Stieger S, Voracek M. (2014). Not only dogs resemble their owners, cars do, too. Swiss Journal of Psychology, 73(2): 111.
Su, X., Khoshgoftaar, T.M., & Greiner, R. (2008). A Mixture Imputation-Boosted Collaborative Filter. In FLAIRS conference (pp. 312-316).
Suresh, A., & Belinda, M.C.M. (2021). Online product recommendation system using gated recurrent unit with Broyden Fletcher Goldfarb Shanno algorithm. Evolutionary Intelligence, 2021: 1-14.
Tanaka J.W, Sengco J.A. (1997). Features and their configuration in Facerecognition. Memory & cognition, 25(5): 583-592.
Terveen, L., & Hill, W. (2001). Beyond recommender systems: Helping people help each other. HCI in the New Millennium, 2001: 487-509.
Thompson, P. (1980). Thatcher,Margaret - a new illusion. Perception, 9: 483–484.
Ujiie, Y., Kato, T., Sato, K., & Matsuoka, Y. (2012). Curvature entropy for curved profile generation. Entropy, 14: 533-558.
Vallen B., Sridhar K., Rubin D., et al. (2019). Shape‐and Trait‐Congruency: Using Appearance‐based Cues as a Basis for Product Recommendations. Journal of Consumer Psychology, 29(2): 271-284.
Van Engelen, J.E., & Hoos, H.H. (2020). A survey on semi-supervised learning. Machine Learning, 109(2): 373-440.
Wang, L., You, Z.H., Chen, X., Xia, S.X., Liu, F., Yan, X., ... & Song, K.J. (2018). A computational-based method for predicting drug–target interactions by using stacked autoencoder deep neural network. Journal of Computational Biology, 25(3), 361-373.
Waytz A., Epley N., Cacioppo J.T. (2010). Social cognition unbound: Insights into anthropomorphism and dehumanization. Current Directions in Psychological Science, 19(1): 58-62.
Welsh J. (2006). Why cars got angry. Wall Street Journal, 3(10): W1.
Werbos, P. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. PhD thesis, Harvard Univ.
Willis J., Todorov A. (2006). First impressions: Making up your mind after a 100-ms exposure to a face. Psychological science, 17(7): 592-598.
Windhager S., Bookstein F.L, Grammer K., et al. (2012). Cars have their own faces: cross-cultural ratings of car shapes in biological terms. Evolution and Human Behavior, 33(2): 109-120.
Windhager S., Hutzler F,. Carbon C.C, et al. (2010). Laying eyes on headlights: Eye movements suggest facial features in cars. Collegium antropologicum, 34(3): 1075-1080.
Windhager S., Slice D..E, Schaefer K, et al. (2008). Faceto face. Human Nature, 19(4): 331-346.
Windhager, S., Bookstein, F. L., Grammer, K., Oberzaucher, E., Said, H., Slice, D. E., ... & Schaefer, K. (2012). “Cars have their own faces”: cross-cultural ratings of car shapes in biological (stereotypical) terms. Evolution and Human Behavior, 33(2), 109-120.
Wu, B., & Ye, Y. (2020). BSPR: Basket-sensitive personalized ranking for product recommendation. Information Sciences, 541, 185-206.
Wu, S., Ren, W., Yu, C., Chen, G., Zhang, D., & Zhu, J. (2016). Personal recommendation using deep recurrent neural networks in NetEase. In 2016 IEEE 32nd international conference on data engineering (ICDE) (pp. 1218-1229). IEEE.
Xia P. Y. (2011). Research on collaborative filtering algorithm in personalized recommendation technology. Doctoral dissertation, Ocean University of China.
Xu, P. Y. (2017). Collaborative filtering algorithms based on multi-factors research framework. Doctoral dissertation, Dalian University of technology.
Yang, X., Zhou, S., & Cao, M. (2019). An approach to alleviate the sparsity problem of hybrid collaborative filtering based recommendations: the product-attribute perspective from user reviews. Mobile networks and applications, 2019: 1-15.
Zhang, Z. (2016). Derivation of backpropagation in convolutional neural network. University of Tennessee, Knoxville, TN.