## 3 Design Challenges for News Recommenders
In addition to the element of recency, the popularity of news items may differ dramatically, thereby rendering the traditional recommendation methods unsuccessful [32]. Figure 2 summarizes the main design challenges associated with the particularities of news as a media offering. Furthermore, news recommender systems must deal with a large and heterogenous corpus of news items generating scalability issues. A common strategy for solving scalability is clustering; effective clustering though requires a very thorough classification of news stories and detailed user profiling based on the reading behavior of users and their short-term and long-term profiles [54], which often proves a difficult task. The cold start problem stemming from insufficient user information to estimate user preferences is a common challenge in NRS [32; 56]. User registration, among others, comprises a standard method to overcome it. [PERSON] and her colleagues [57], however, found significant differences between declared and actual interests in news topics, especially in broad news categories containing many subtopics (for instance, politics). They concluded that users cannot accurately assess their interest in news topics and argue that news recommender systems should apply different filtering mechanisms for different news categories. In other words, the depth of personalization should be adjusted to cater for both declared interests and assumed interests of important events (see Figure 3, Challenges associated with profiling). Figure 2: Design challenges for NRS related to the nature of news. An important factor influencing how specific challenges will be treated pertains to the purpose of the recommender system as shaped by competing logics in the news organization. [PERSON] et al. [58] argue that there is a crucial stage in the design of a recommender system in which the organization decides on the business and strategic goals they want to reach with the personalization service. A purpose-driven evaluation of recommender systems brings along the question of which stakeholder(s) are defining the recommendation purpose and how the conflicts and trade-offs between stakeholders are resolved and embedded in the system. More sophisticated algorithms not only combine hybrid logics in their filtering techniques but also include an element of surprise: serendipity [33]. The serendipity principle posits that the algorithm recommends items that are not only novel but also positively surprising for the user and propose a generic metric based on the concepts of unexpectedness and usefulness [32]. A key challenge of serendipitous recommendations is setting a balance between novelty and users' expectations. Serendipity is considered a quality factor for improving algorithmic output; it helps users keep an open window to new discoveries, it broadens the recommendation spectrum to avoid cases of users losing interest because the choice set is too uniform and narrow and helps integrate new items in order to acquire information on their appeal [49]. In addition to serendipity, the principles of novelty and diversity are deemed quality factors that can broaden the news menu and improve user perceptions and engagement [59]. Again, the 'right' level of novelty and diversity works as a trade-off for accuracy and can depend on the user's current situation and context [32]. Figure 4 depicts the main parameters for evaluating the quality of news recommendations. Recent initiatives aiming to run 'diversity-enhancing' algorithms focus on nudge-like personalization features, for instance, visuals to increase item salience, or item re-ranking in an attempt to curb rigid algorithmic recommendations [60]. Although nudging involves the steering of people in news paths that can enhance their news diet and knowledge [61], initiatives to nudge people toward diversity in their information exposure raise questions of autonomy and freedom of choice [62].