The Art of Gift-Giving with Limited Preference Data: How Fashion Recommender Systems Can Help
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Sharareh Alipour, Sina Elahimanesh, Aliakbar Ghayouri Sales, Iman Mohammadi, Parimehr Morassafar, Seyed Parsa NeshaeiAuthors Info & Claims
Article No.: 314, Pages 1 - 7
Abstract
Gift shopping can be challenging due to the limited prior knowledge of the recipient’s preferences, leading to after-purchase regret. The effectiveness of Fashion Recommender Systems (FRS) in the context of gift purchases with limited preference data remains under-explored. We considered a gift-buying scenario and conducted an experiment with 192 pairs of participants to compare FRS versus humans in recommending fashion gifts to buyers. We find both FRS and humans score >50% correctness in recommending the right gift, even without direct interaction with gift-givers or recipients. Although the buyers know the gift receivers directly, they lead to less accuracy. Additionally, we identify gender-based differences in the recommendations. We also embed our scenario into a smartphone application. Our findings investigate the potential of FRS in cold-start scenarios with limited data and unavailable human assistance while highlighting the risks of using FRS for in-store gift purchases.
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References
[1]
Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. on Knowl. and Data Eng. 17, 6 (jun 2005), 734–749. https://doi.org/10.1109/TKDE.2005.99
[2]
Joeran Beel, Stefan Langer, Andreas Nürnberger, and Marcel Genzmehr. 2013. The impact of demographics (age and gender) and other user-characteristics on evaluating recommender systems. In Research and Advanced Technology for Digital Libraries: International Conference on Theory and Practice of Digital Libraries, TPDL 2013, Valletta, Malta, September 22-26, 2013. Proceedings 3. Springer, 396–400.
[3]
Robin Burke. 2002. Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction 12 (2002), 331–370.
[4]
David J Cheal. 1986. The social dimensions of gift behaviour. Journal of social and personal relationships 3, 4 (1986), 423–439.
[5]
Li Chen and Pearl Pu. 2014. Experiments on user experiences with recommender interfaces. Behaviour & Information Technology 33, 4 (2014), 372–394.
[6]
Zefeng Chen, Wensheng Gan, Jiayang Wu, Kaixia Hu, and Hong Lin. 2024. Data Scarcity in Recommendation Systems: A Survey. ACM Transactions on Recommender Systems (2024).
[7]
Soo Eun Cho. 2007. Clothing gift expenditures: The influence of gender, ethnicity, and age. The University of North Carolina at Greensboro.
[8]
Jong-Myoung Choi and In-Sook Kim. 2006. Comparison of Clothing Gift Giving Behavior of University Students by Gender. The International Journal of Costume Culture 9, 1 (2006), 52–59.
[9]
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).
[10]
Junmei Feng, Zhaoqiang Xia, Xiaoyi Feng, and Jinye Peng. 2021. RBPR: A hybrid model for the new user cold start problem in recommender systems. Knowledge-Based Systems 214 (2021), 106732.
[11]
Cathy Goodwin, Kelly L Smith, and Susan Spiggle. 1990. Gift giving: Consumer motivation and the gift purchase process. ACR North American Advances (1990).
[12]
Lena Horne and Geitel Winakor. 1991. A conceptual framework for the gift-giving process: Implications for clothing. Clothing and Textiles Research Journal 9, 4 (1991), 23–33.
[13]
Rong Hu and Pearl Pu. 2011. Enhancing collaborative filtering systems with personality information. In Proceedings of the Fifth ACM Conference on Recommender Systems. 197–204.
[14]
P Jackson Vanessa and Hyun-Ju Kwon. 2006. Gift Giving: The Interaction between Gender, Gift Recipient, and Group Identity Importance by Product Category. Journal of the Korean Society of Clothing and Textiles 30, 12 (2006), 1759–1767.
[15]
Dietmar Jannach, Pearl Pu, Francesco Ricci, and Markus Zanker. 2022. Recommender Systems: Trends and Frontiers. AI Magazine 43, 2 (2022), 145–150.
[16]
Joseph Konstan and Loren Terveen. 2021. Human-centered recommender systems: Origins, advances, challenges, and opportunities. AI Magazine 42, 3 (2021), 31–42.
[17]
Vinod Krishnan, Pradeep Kumar Narayanashetty, Mukesh Nathan, Richard T. Davies, and Joseph A. Konstan. 2008. Who Predicts Better? Results from an Online Study Comparing Humans and an Online Recommender System. In Proceedings of the 2008 ACM Conference on Recommender Systems (Lausanne, Switzerland) (RecSys ’08). Association for Computing Machinery, New York, NY, USA, 211–218. https://doi.org/10.1145/1454008.1454042
[18]
Greg Linden, Brent Smith, and Jeremy York. 2003. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing 7, 1 (2003), 76–80.
[19]
Linda Manikowske and Geitel Winakor. 1994. Equity, attribution, and reactance in giving and receiving gifts of clothing. Clothing and Textiles research journal 12, 4 (1994), 22–30.
[20]
Masoud Mansoury, Himan Abdollahpouri, Jessie Smith, Arman Dehpanah, Mykola Pechenizkiy, and Bamshad Mobasher. 2020. Investigating potential factors associated with gender discrimination in collaborative recommender systems. arXiv preprint arXiv:2002.07786 (2020).
[21]
Deepak Kumar Panda and Sanjog Ray. 2022. Approaches and algorithms to mitigate cold start problems in recommender systems: a systematic literature review. Journal of Intelligent Information Systems 59, 2 (2022), 341–366.
[22]
Pearl Pu, Li Chen, and Rong Hu. 2011. A User-Centric Evaluation Framework for Recommender Systems. In Proceedings of the Fifth ACM Conference on Recommender Systems (Chicago, Illinois, USA) (RecSys ’11). Association for Computing Machinery, New York, NY, USA, 157–164. https://doi.org/10.1145/2043932.2043962
[23]
Pearl Pu, Li Chen, and Rong Hu. 2012. Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Modeling and User-Adapted Interaction 22, 4 (2012), 317–355.
[24]
M Sarumathi, S Singarani, S Thameemaa, V Umayal, S Archana, K Indira, and MK Kavitha Devi. 2016. Systematic approach for cold start issues in recommendations system. In 2016 International Conference on Recent Trends in Information Technology (ICRTIT). IEEE, 1–7.
[25]
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. 285–295.
[26]
Muhammad Arslan Sarwar, Zainudin Awang, and Muhammad Danish Habib. 2019. Consumer purchase regret: A systematic review. International Journal of Academic Research in Business and Social Sciences 9, 9 (2019), 403–425.
[27]
Shrikant Saxena and Shweta Jain. 2021. Exploring and mitigating gender bias in recommender systems with explicit feedback. arXiv preprint arXiv:2112.02530 (2021).
[28]
Jyoti Shokeen and Chhavi Rana. 2020. A study on features of social recommender systems. Artificial Intelligence Review 53 (2020), 965–988.
[29]
Brent Smith and Greg Linden. 2017. Two decades of recommender systems at Amazon.com. IEEE Internet Computing 21, 3 (2017), 12–18.
[30]
M Sridevi, N ManikyaArun, M Sheshikala, and E Sudarshan. 2020. Personalized fashion recommender system with image based neural networks. In IOP conference series: materials science and engineering, Vol. 981. IOP Publishing, 022073.
[31]
Alex S. Taylor and Richard Harper. 2002. Age-old practices in the ’new world’: a study of gift-giving between teenage mobile phone users. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Minneapolis, Minnesota, USA) (CHI ’02). Association for Computing Machinery, New York, NY, USA, 439–446. https://doi.org/10.1145/503376.503455
[32]
Usha Yadav, Neelam Duhan, and Komal Kumar Bhatia. 2020. Dealing with pure new user cold-start problem in recommendation system based on linked open data and social network features. Mobile Information Systems 2020 (2020), 1–20.
Index Terms
- The Art of Gift-Giving with Limited Preference Data: How Fashion Recommender Systems Can Help
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Published In
May 2024
4761 pages
ISBN:9798400703317
DOI:10.1145/3613905
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Published: 11 May 2024
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