The N‐heterocyclic stannylenes [{Fe(η5‐C5H4‐NSitBuMe2)2}Sn] (1) and [o‐C6H4(NSitBuMe2)2]Sn (2) were prepared in one‐pot reactions from SnCl2, LiN(SiMe3)2, and the corresponding diamine, viz. [Fe(η5‐C5H4‐NHSitBuMe2)2] and o‐C6H4(NSiHtBuMe2)2, respectively. Oxidative addition reactions of 1 and 2 with S8 and Se8, respectively, afforded the corresponding 1,3,2,4‐dithiadistannetanes ((1S)2, (2S)2), and 1,3,2,4‐diselenadistannetanes [(1Se)2, (2Se)2]. The reactions of 1 and 2 with PhSeSePh respectively furnished 1(SePh)2 and 2(SePh)2. The crystal structures of o‐C6H4(NSiHtBuMe2)2, 1, 2, (1S)2, (2S)2, (1Se)2, (2Se)2, 1(SePh)2, and 2(SePh)2 were determined by single‐crystal X‐ray diffraction. Stannylene 2 shows a chain‐like aggregation in the solid state due to intermolecular Sn···arene interactions. A short intramolecular CH···Se contact compatible with a hydrogen bond was observed for (1Se)2.

Recommender systems have dramatically changed the way we consume content. Internet applications rely on these systems to help users navigate among the everincreasing number of choices available. However, most current systems ignore the fact that user preferences can change according to context, resulting in recommendations that do not fit user interests. This research addresses these issues by proposing the (CF) 2 architecture, which uses local learning techniques to embed contextual awareness into collaborative filtering models. The proposed architecture is demonstrated on two large-scale case studies involving over 130 million and over 7 million unique samples, respectively. Results show that contextual models trained with a small fraction of the data provided similar accuracy to collaborative filtering models trained with the complete dataset. Moreover, the impact of taking into account context in real-world datasets has been demonstrated by higher accuracy of context-based models in comparison to random selection models.