Learning and adapting user criteria for recommending followees in social networks
提出一种根据用户行为和偏好自适应调整推荐关注对象标准的方法，能随时间演化并提升推荐多样性，实验证明比静态权重策略更精准。
The accurate suggestion of interesting friends arises as a crucial issue in recommendation systems. The selection of friends or followees responds to several reasons whose importance might differ according to the characteristics and preferences of each user. Furthermore, those preferences might also change over time. Consequently, understanding how friends or followees are selected emerges as a key design factor of strategies for personalized recommendations. In this work, we argue that the criteria for recommending followees needs to be adapted and combined according to each user's behavior, preferences, and characteristics. A method is proposed for adapting such criteria to the characteristics of the previously selected followees. Moreover, the criteria can evolve over time to adapt to changes in user behavior, and broaden the diversity of the recommendation of potential followees based on novelty. Experimental evaluation showed that the proposed method improved precision results regarding static criteria weighting strategies and traditional rank aggregation techniques.