# Recommendation Framework Combining User Interests with Fashion Trends in Apparel Online Shopping
## 2 Related Works  
To overcome the sparsity problem of using CF, many studies have attempted to increase the accuracy of the similarity between users to improve the recommendation performance. One commonly used approach is a hybrid method [10; 11; 12] that complements the similarity from the user-item ratings matrix with the similarity of item attributes. However, this method can be applied when the item features are sufficient to explain the item similarity. Instead of using item attributes, some researchers attempted to estimate more accurate similarities [13; 14; 15] or ratings [16; 17] using additional methods or algorithms on the user-item ratings matrix. For example, [PERSON] [13] proposed a method of estimating user similarity by establishing the relationship between user similarity and item similarity as a regression model. Further approaches include a proposal to supplement similarity by the addition of external data [18; 19], and to alleviate sparsity through user clustering [20; 21]. In this study, we also attempt to reduce sparsity by generating dense ratings from user log data for the most recent months and clustering items based on item similarity. To generate ratings from implicit feedback datasets, a general approach is to map the users' activities on items such as click, cart, and purchase into ratings. [PERSON] et al. [22] set the proportion of each activity to its score range to generate a personalized score in a functional form; this is not the best scheme using CF based on the common ground ratings. According to [23], using implicit feedback data resulted in a more diverse and accurate recommendation by providing a wealth of unbiased information compared to using explicit ratings. A major topic in addition to user interest in apparel recommendations is the incorporation of up-to-date fashion trends and seasonality into the recommendations, which requires a model that captures sequential and temporal dynamics from user behaviors. The most popular method for representing such dynamics is a graph, although some studies [24; 25; 26] suggested models that reflect item sequence without using graphs. Several studies [27; 28; 29] demonstrated that link analysis methods using Markov chains, such as personalized PageRank [30], can be effectively applied to personalized recommendation considering a sequential pattern based on implicit feedback data. According to [31], exploiting a graph-based model for recommendation makes it easy to not only combine various types of information, but also to represent indirect relationships. Recent research focuses on recommending items with diversity or serendipity, rather than items with only strong relevance or popularity, in order to broaden the user experience and avoid typicality [32; 33]. For example, in related entity recommendations for entity queries, diverse but related entities are ranked by link analysis methods on related entity networks based on a co-occurrence relation added by an entity importance vector [33]. Some recommendation systems attempted to reflect both personalization and temporal dynamics by considering user interest as long- and short-term preferences. [PERSON] and [PERSON] [34] suggested a sequential prediction model by combining a similarity-based method for long-term preference and a Markov chain for short-term dynamics. For top-N news recommendations, [PERSON] et al. [32] utilized long-term user profiles to model users' general topic interests and short-term profiles to capture the recent preferences with respect to new topics based on user profiling by a time-sensitive weighting scheme. In our proposed framework, we conceptually define the user's taste as a consistent preference for items and the user's needs as purchase intent for item categories, which differ conceptually and in terms of object level with the exception of the temporal division of long- and short-term preferences from existing studies.