# Re-Enrichment Learning: Metadata Saliency for the Evolutive Personalization of a Recommender System
## 1 Introduction  
Online contents and services have been rapidly growing in recent decades. To help users make decisions when faced with overwhelming options and to achieve commercial success in making profit, both academic and industrial research on finding specific sets of items that meet the personalized interests of users has been intensely conducted. For this purpose, recommender systems have been suggested and designed that are based on a user's history, including whether the user has bought an item, which ratings the user has given to items, etc. Previous studies have reported that recommendations account for 60% of the clicks on the main screen of Youtube [1], 75% of what people watch through Netflix [2], and 35% of the sales through Amazon [3]. Recommendation is obviously not an easy task. A recommender system needs to provide a personalized user experience with long-term satisfaction under the condition that users' preferences can change over time [4]. Behaviors based on the preferences of users could be either positively or negatively influenced by recommendation results. Traditionally, the recommendation problem has been considered as equivalent to the problem of precisely predicting the rating that a user would leave on an item. However, recent academic studies and industrial providers have both emphasized that one of the main measurement targets is improved user retention [5, 6]. In spite of the gains in theoretical accuracy, it was unclear that the winning strategy in accuracy would result always in increasing business value [7]. Still, recommender systems are mostly optimized toward the accuracy of predictions of item ratings, and are likely to gradually narrow down the interest area of a user--the so-called the diversity problem [8; 9; 10; 11]. That is to say, recommendations where the lesser interests get squeezed out by the main interest ironically help the system minimize prediction errors. This not only impedes the diversity of experiences of users, but also causes filter bubbles [12; 13], e.g., a reduced spectrum of user consumption and the political bias, which limit discovery or neglect the potential for promoting new items from the long tail [14; 15]. Moreover, as the training of a recommender system mostly relies on explicit feedback (typically the ratings of items), it is inevitable to face the fact that only a small portion of users leave their ratings--the so-called the sparsity problem [16; 17]. In this paper, we propose a novel data interpretation approach, which includes graph-based domain transfer and metadata saliency, for recommender systems, and show its effective application for a personalized recommender, namely re-enrichment learning. To address the problems of diversity and sparsity, the goal of re-enrichment learning is to find conspicuous features of the implicit logged feedback [18; 19] that tell the behavior of a user, e.g., how frequently the user clicks on a certain item or how long the user stays on the platform, and to promote immanently correlated recommendations. Unlike explicit feedback, implicit logged feedback is relatively easier to spontaneously collect from users. Metadata saliency describes the confidence of a recommendation in the transferred domain, which reflects the graph-based relationships among items. It is generally known that the visual saliency represents the position-based visual preference [20]. As such, methods for predicting eye fixation maps on websites or mobile interfaces [21; 22; 23] have been studied, and the relation between the probability of the movement of a mouse pointer and the eye fixation has been discussed [24]. Inspired by those works, metadata saliency is a novel approach to interest fixation for distinguishing a certain set of items from the others. Based on the establishment of links between one group of items and their closely related ones, re-enrichment learning is used to train a recommender system based on the metadata saliency, thus enriching the potential interest areas of a user rather than narrowing them down.