結合身材屬性相似度與購物偏好的商品合適度推薦

Combining Body Attributes and Shopping Preferences for Product Fit Recommendation

盧台鳳(Tai-Feng Lu)

指導教授 ： 劉敦仁

國立交通大學/管理學院/管理學院資訊管理學程/碩士(2020年)

摘要

電子商務在運營方式與傳統零售有根本上的不同，而這樣的不同處正是業者在規劃電商策略時的致勝關鍵。為了替消費者打造良好的個人化銷售體驗，許多電商致力於發展推薦系統。
然而，在服飾類別而言，推薦的內容仍多以消費者過往的消費記錄來推薦消費者偏好的商品類別，其中鮮有針對消費者身材進行商品合適度的推薦。因此，本論文提出了結合身材屬性相似度與消費偏好的推薦方法。使用電商網站所收集的消費者身材資訊，根據相對應的服飾類別挑選相對應的身材屬性，並找出身材相似的其他消費者選擇的尺碼作為推薦依據。另外結合協同過濾，找出與目標消費者消費偏好相似的其他消費者所選擇的尺碼。並且在實驗中比較兩種因素對於準確度的影響權重為何。

關鍵字

合適度推薦 ； 協同過濾 ； 身材屬性相似度 ； 電子商務

並列摘要

The Business model of E- Commerce is different from traditional retail, while the difference is just the key to success in planning e-commerce strategies. In order to create good personalized sales experience for consumers, many e-commerce companies devoted to developing recommendation systems.
However, in terms of apparel categories, the recommended content is still focus on the consumers’ preferred products, few studies recommend the suitability of the product based on consumers’ body measurement. In our research, we proposed a recommendation method combining body attribute similarity and consumption preferences. We use the body measurement data collected from e-commerce websites and seek the similarity among different consumers. In addition, we find out the selected apparel size of other consumers with similar shopping preferences based on collaborative filtering. Then we compare these two elements in experiments to discover which weights most when recommending apparel size to users.

並列關鍵字

Suitability recommendation ； collaborative filtering ； e-commerce

參考文獻

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