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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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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