# Recommendation Framework Combining User Interests with Fashion Trends in Apparel Online Shopping
## 3 Proposed Method  
### System Overview  
As mentioned earlier, frequent item changes due to fashion trends and seasonality makes data sparsity severe, especially in online apparel shopping. In order to cope with the data sparsity problem, we utilize implicit ratings, i.e., ratings generated from user activity logs. However, the data of implicit ratings are still too sparse to directly apply a CF algorithm. We therefore adopt item clustering based on the implicit ratings, which results in a user-item cluster ratings matrix. Apparel products typically follow the hierarchy of gender-category-item, where the gender takes either female (women's wear) or male (men's wear) and the category can be t-shirts, pants, skirts, and so on. The construction of item-clusters implies that we adopt the four-level hierarchy of gender-category-cluster-item, as can be seen in Figure 1. Notice from the figure that items belonging to different categories are not grouped in a same cluster in this study.  
Although typical CF algorithms can make personalized recommendations, they are not designed to incorporate temporal dynamics of items. Moreover, we cannot recommend specific items from a CFalgorithm based on the user-item cluster ratings matrix. In order to address this problem, we construct directed graphs using the same user activity log and update those as time passes. Frequently visited items by random walks on the graphs would be good candidates for recommendation. This graph-based method, however, does not make personalized recommendations. We therefore combine the user-based CF based on the user-item cluster ratings matrix and the graph-based random walk to incorporate both personalization and temporal dynamics of items, namely fashion trends and seasonality, into an apparel recommender system.  
Apparel products are usually selected according to a customer's needs with respect to categories. For example, a customer needs a t-shirt on one day and a pair of pants on another day. It is also important to know for which product categories the user under recommendation searches. Because apparel products are grouped into women's and men's wear, we should consider gender as a key factor for recommendation. It is worth noting that the gender does not refer to a user's gender and we do not use users' personal information. We only attempt to determine which category the user searches for between women's and men's wear.  
Figure 2 represents an overview of our recommendation framework, including all of the abovementioned aspects. After generating implicit ratings from user log data, we perform item-clustering (Figure 2a) and construct a directed base graph (Figure 2b). The user's preference ratings with regard to item clusters are calculated by a user-based CF algorithm. We construct a personalized graph by projecting the user's cluster ratings onto the latest item flow graph, and then derive item preference scores through random walks (Figure 2b). Final recommendation items are listed by a combination of item preference scores, the user's needs for categories, and the gender, as shown in Figure 2c. Each of the stages in our method will be explained in detail as follows: Figure 2a in Section 3.2, Figure 2b in Section 3.3, and Figure 2c in Section 3.4. To aid readers in better understanding our method, we prepared an example dataset in Table 1. This dataset will be used for a stage-by-stage description of our proposed method.  
Figure 1: Hierarchy of apparel products.  
Figure 2: Overview of the proposed recommendation framework. (**a**) Clustering items and rating item clusters. (**b**) Scoring item preferences. (**c**) Reflecting user’s needs.