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In recent years, session-based recommendation (SBR), which seeks to predict the target user's next click based on anonymous interaction sequences, has drawn increasing interest for its practicality. The key to completing the SBR task is modeling user intent accurately. Due to the popularity of graph neural networks (GNNs), most state-of-the-art (SOTA) SBR approaches attempt to model user intent from the transitions among items in a session with GNNs. Despite their accomplishments, there are still two limitations. First, most existing SBR approaches utilize limited information from short user–item interaction sequences and suffer from the data sparsity problem of session data. Second, most GNN-based SBR approaches describe pairwise relations between items while neglecting complex and high-order data relations. Although some recent studies based on hypergraph neural networks have been proposed to model complex and high-order relations, they usually output unsatisfactory results due to insufficient relation modeling and information loss. To this end, we propose a category-aware lossless heterogeneous hypergraph neural network (CLHHN) in this article to recommend possible items to the target users by leveraging the category of items. More specifically, we convert each category-aware session sequence with repeated user clicks into a lossless heterogeneous hypergraph consisting of item and category nodes as well as three types of hyperedges, each of which can capture specific relations to reflect various user intents. Then, we design an attention-based lossless hypergraph convolutional network to generate sessionwise and multi-granularity intent-aware item representations. Experiments on three real-world datasets indicate that CLHHN can outperform the SOTA models in making a better tradeoff between prediction performance and training efficiency. An ablation study also demonstrates the necessity of CLHHN's key components. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the rapid development of service‐oriented computing technologies. The boom of Web services increases software developers' selection burden in developing new service‐based systems such as mashups. Timely recommending appropriate component services for developers to build new mashups has become a fundamental problem in service‐oriented software engineering. Existing service recommendation approaches are mainly designed for mashup development in the single‐round scenario. It is hard for them to effectively update recommendation results according to developers' requirements and behaviours (e.g. instant service selection). To address this issue, the authors propose a service bundle recommendation framework based on deep learning, DLISR, which aims to capture the interactions among the target mashup to build, selected (component) services, and the following service to recommend. Moreover, an attention mechanism is employed in DLISR to weigh selected services when recommending a candidate service. The authors also design two separate models for learning interactions from the perspectives of content and invocation history, respectively, and a hybrid model called HISR. Experiments on a real‐world dataset indicate that HISR can outperform several state‐of‐the‐art service recommendation methods to develop new mashups iteratively. [ABSTRACT FROM AUTHOR]

The structures of two one-dimensional coordination complexes, [[M.sup.II][(3-Bzpy).sub.2][(N[(CN).sub.2]).sub.2]] (M = Mn (1) and Co (2) and 3-Bzpy = 3-benzoylpyridine), have been determined in this work. The two structures are isomorphous, crystallize in a monoclinic P[2.sub.1]/c space group, and show almost the same crystal structure. The magnetic measurements of the two compounds (2-300 K) indicate that antiferromagnetic exchanges exist between the metal ions of the two compounds. Key words: dicyanamide, 1-D, structure, magnetic property. Dans ce travail, on a determine les structures de deux complexes de coordination unidimensionnels, [[M.sup.II][(3-Bzpy).sub.2][(N[(CN).sub.2]).sub.2]] (M = Mn (1) et Co (2) et 3-Bzpy = 3-benzoylpyridine). Les deux structures sont isomorphes, elles cristallisent dans le groupe d'espace monoclinique P[2.sub.1/c] et elles se presentent sous des formes cristallines pratiquement semblables. Des mesures magnetiques effectuees sur les deux composes (2-300 K) indiquent qu'il se produit des echanges antiferromagnetiques entre les ions metalliques des deux composes. Mots-cles : dicyanamide, 1-D, structure, propriete magnetique. [Traduit par la Redaction], Introduction In the last 10 years, 1-D coordination polymers have been frequently investigated because of their magnetic characteristics. (1-3) The dicyanamide ligand [[N[(CN).sub.2]].sup.-] is extremely popular as a building block [...]