# Recommendation Framework Combining User Interests with Fashion Trends in Apparel Online Shopping
## 1 Introduction  
Fashion-related products ranked the most popular of global online shopping categories (with 58% of online purchases), based on purchases up to November 2016 [1]. In addition, the increment of shoppers will accelerate with the rapid increase in smartphone users. However, as the number of fashion products available online dramatically increases, users spend considerable time looking for preferred products and sometimes fail to find products of interest. This is especially prominent in fashion shopping, because the attributes of fashion products are difficult to describe or classify. Therefore, an effective and efficient recommendation service of fashion items is becoming indispensable for both purchases and sales. A general approach for recommending fashion items is to explain each user's taste with item attributes such as color, material, and price of items, or/and user profile information such as age and gender [2]. One of the most recent studies [3] generated user clusters based on color, age range, and price range, and scored a user's preference for an item as a function of the preference of a cluster containing the user by reinforcement learning to adapt to the user's behavior. It is also suggested to calculate the similarity of apparel items [4] or brands [5] based on their attributes, and to recommend those with similar style to the previously preferred items/brands. However, to define users' tastes using several well-recognizable factors does not guarantee successful recommendation, owing to the emotional and subjective elements of apparel products, which are generally difficult to convert into a structured data form. Several studies [6; 7; 8] have been performed to further classify additional featuresof fashion items through image pattern recognition. More specific research topics related to fashion recommendation are referred to in [7]. A collaborative filtering (CF) algorithm, which effectively filters information or patterns by borrowing other users' preferences for items, remains valid. [PERSON] and [PERSON] [9] used two types of CF for item similarity and user similarity based on historical buying data, from which candidate items for recommendation are generated in a similar manner to the target user's or the nearest neighbors' buying items. However, periodic large-scale replacement of fashion products according to seasons causes extreme sparsity on the user-item ratings matrix, and thus, frequent cold starts make it difficult to obtain good recommendation performance. In addition to these, there are several characteristics when users select fashion products online, which are detrimental to the performance of recommender systems based on such existing approaches. Short-term factors, such as weather changes and fads, also affect the selection of fashion products. People are influenced by fashion trends, but also pursue their own personal tastes through fashion products. We should also consider shopping according to the needs of specific item categories, such as men's winter pants. In this paper, we propose a novel recommendation framework for apparel items by predicting individual taste and needs for items, and integrating these into fashion trends based only on users' access log data excluding attributes, profiles, and other external factors. Specifically, our proposed method includes three components:  
* We construct an item trend graph to sensitively capture the temporal dynamics of item search flow resulting from any external factors. * We score the user's preference for item clusters using user-based CF along with methods to overcome the sparsity problem and combine it with the item trend graph. * We predict the user's needs with respect to item category and gender based on recent access logs and reflect them in the final recommendation. Typically, there are two types of recommendation services in online fashion malls.