Personalized news recommendations have become a standard feature of large news aggregation services, optimizing user engagement through automated content selection. In contrast, legacy news media often approach personalization cautiously, striving to balance technological innovation with core editorial values. As a result, online platforms of traditional news outlets typically combine editorially curated content with algorithmically selected articles - a strategy we term controlled personalization. In this industry paper, we evaluate the effectiveness of controlled personalization through an A/B test conducted on the website of a major Norwegian legacy news organization. Our findings indicate that even a modest level of personalization yields substantial benefits. Specifically, we observe that users exposed to personalized content demonstrate higher click-through rates and reduced navigation effort, suggesting improved discovery of relevant content. Moreover, our analysis reveals that controlled personalization contributes to greater content diversity and catalog coverage and in addition reduces popularity bias. Overall, our results suggest that controlled personalization can successfully align user needs with editorial goals, offering a viable path for legacy media to adopt personalization technologies while upholding journalistic values.

Recently, a growing body of research has focused on either optimizing CTR model architectures to better model feature interactions or refining training objectives to aid parameter learning, thereby achieving better predictive performance. However, previous efforts have primarily focused on the training phase, largely neglecting opportunities for optimization during the inference phase. Infrequently occurring feature combinations, in particular, can degrade prediction performance, leading to unreliable or low-confidence outputs. To unlock the predictive potential of trained CTR models, we propose a Model-Agnostic Test-Time paradigm (MATT), which leverages the confidence scores of feature combinations to guide the generation of multiple inference paths, thereby mitigating the influence of low-confidence features on the final prediction. Specifically, to quantify the confidence of feature combinations, we introduce a hierarchical probabilistic hashing method to estimate the occurrence frequencies of feature combinations at various orders, which serve as their corresponding confidence scores. Then, using the confidence scores as sampling probabilities, we generate multiple instance-specific inference paths through iterative sampling and subsequently aggregate the prediction scores from multiple paths to conduct robust predictions. Finally, extensive offline experiments and online A/B tests strongly validate the compatibility and effectiveness of MATT across existing CTR models.

'I realised I'd been ChatGPT-ed into bed': how'Chatfishing' made finding love on dating apps even weirder Where once people were duped by soft-focus photos and borrowed chat-up lines, now they have to watch out for computer-generated charm. But it's one thing to use a witty phrase - another thing entirely to build a whole fake persona S tanding outside the pub, 36-year-old business owner Rachel took a final tug on her vape and steeled herself to meet the man she'd spent the last three weeks opening up to. They'd matched on the dating app Hinge and built a rapport that quickly became something deeper. "From the beginning he was asking very open-ended questions, and that felt refreshing," says Rachel. One early message from her match read: "I've been reading a bit about attachment styles lately, it's helped me to understand myself better - and the type of partner I should be looking for. Have you ever looked at yours? Do you know your attachment style?" "It was like he was genuinely trying to get to know me on a deeper level. The questions felt a lot more thoughtful than the usual, 'How's your day going?'"

The dual nature of these systems naturally influences both users and creators: users' preferences are

Fed-SVT is nearly optimal up to logarithmic factors.

Softmax Loss (SL) is widely applied in recommender systems (RS) and has demonstrated effectiveness.

CF with the same training epochs in both clean and poisoned data contexts.

Recently, multi-modal entity alignment has emerged as a pivotal endeavor for the integration of Multi-Modal Knowledge Graphs (MMKGs) originating from diverse data sources.