Abstract
Fashion e-commerce is a fast growing area in online shopping. The fashion domain has several interesting properties, which make personalised recommendations more difficult than in more traditional domains. To avoid potential bias when using explicit user ratings, which are also expensive to obtain, this work approaches fashion recommendations by analysing implicit feedback from users in an app. A user’s actual behaviour, such as Clicks, Wants and Purchases, is used to infer her implicit preference score of an item she has interacted with. This score is then blended with information about the item’s price and popularity as well as the recentness of the user’s action wrt. the item. Based on these implicit preference scores, we infer the user’s ranking of other fashion items by applying different recommendation algorithms. Experimental results show that the proposed method outperforms the most popular baseline approach, thus demonstrating its effectiveness and viability.
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References
Hanf, C.H., Wersebe, B.: Price, quality, and consumers’ behaviour. Journal of Consumer Policy 17(3), 335–348 (1994)
Vignali, G., Vignali, C.: Fashion marketing & theory. Access Press (2009)
Li, Y., Hu, J., Zhai, C., Chen, Y.: Improving one-class collaborative filtering by incorporating rich user information. In: Proc. of CIKM 2010, pp. 959–968. ACM (2010)
Nichols, D.M.: Implicit rating and filtering. In: The DELOS Workshop on Filtering and Collaborative Filtering, pp. 31–36 (1997)
Oard, D., Kim, J.: Implicit feedback for recommender systems. In: Proc. of the AAAI Workshop on Recommender Systems, pp. 81–83 (1998)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proc. of IEEE ICDM 2008, pp. 263–272. IEEE CS (2008)
Ilievski, I., Roy, S.: Personalized news recommendation based on implicit feedback. In: Proc. of the 2013 International News Recommender Systems Workshop and Challenge, pp. 10–15. ACM (2013)
Iwata, T., Watanabe, S., Sawada, H.: Fashion coordinates recommender system using photographs from fashion magazines. In: Proc. of AAAI, pp. 2262–2267 (2011)
Shen, E., Lieberman, H., Lam, F.: What am i gonna wear?: Scenario-oriented recommendation. In: Proc. of the 12th International Conference on Intelligent User Interfaces, pp. 365–368. ACM (2007)
Yu-Chu, L., Kawakita, Y., Suzuki, E., Ichikawa, H.: Personalized clothing-recommendation system based on a modified bayesian network. In: Proc. of the 12th IEEE/IPSJ Inter. Symp. on Applications and the Internet, pp. 414–417 (2012)
Ying Zhao, K.A.: What to wear in different situations? a content-based recommendation system for fashion coordination. In: Proc. of the Japanese Forum on Information Technology (FIT2011) (2011)
Liu, S., Feng, J., Song, Z., Zhang, T., Lu, H., Xu, C., Yan, S.: Hi, magic closet, tell me what to wear! In: Proc. of ACM Multimedia 2012, pp. 619–628. ACM (2012)
Kao, K.: SuitUp! (2010) Social space centered around clothing items, http://bit.ly/YYyWoE (Online; accessed August 29, 2014)
Xu, S., Jiang, H., Lau, F.C.: Personalized online document, image and video recommendation via commodity eye-tracking. In: Proc. of RecSys, pp. 83–90 (2008)
Yang, D., Chen, T., Zhang, W., Lu, Q., Yu, Y.: Local implicit feedback mining for music recommendation. In: Proc. of ACM RecSys 2012, pp. 91–98. ACM (2012)
Parra, D., Amatriain, X.: Walk the talk: Analyzing the relation between implicit and explicit feedback for preference elicitation. In: Proc. of the 19th Inter. Conf. on User Modeling, Adaption, and Personalization, pp. 255–268 (2011)
Ghosh, P.: From explicit user engagement to implicit product rating (2014), http://bit.ly/1lidNQn (last accesed August 11, 2014)
Jahrer, M., Töscher, A., Legenstein, R.: Combining predictions for accurate recommender systems. In: Proc. of ACM SIGKDD 2010, pp. 693–702. ACM (2010)
Shani, G., Gunawardana, A.: Evaluating recommender systems. Technical Report MSR-TR-2009-159, Microsoft Research (2009)
Ellen Friedman, T.D.: Practical Machine Learning: Innovations in Recommendation. O’Reilly (2014)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proc. of the 25th Conference on Uncertainty in Artificial Intelligence, pp. 452–461. AUAI Press (2009)
Apache Software Foundation: Apache Mahout: Scalable machine-learning and data-mining library, http://mahout.apache.org
Gantner, Z., Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: MyMediaLite: A free recommender system library. In: Proc. of ACM RecSys 2011. (2011)
Bennett, J., Lanning, S.: The Netflix prize. In: KDD Cup and Workshop in conjunction with KDD (2007)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010, pp. 39–46 (2010)
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Nguyen, H.T. et al. (2014). Learning to Rank for Personalised Fashion Recommender Systems via Implicit Feedback. In: Prasath, R., O’Reilly, P., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8891. Springer, Cham. https://doi.org/10.1007/978-3-319-13817-6_6
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DOI: https://doi.org/10.1007/978-3-319-13817-6_6
Publisher Name: Springer, Cham
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