Personalized recommendation technology in Ecommerce is widespread to solve the problem of product information overload. However, with the further growth of the number of E-commerce users and products, the original recommendation algorithms and systems will face several new challenges: (1) to model user's interests more accurately; (2) to provide more diverse recommendation modes; and (3) to support large-scale expansion. To address these challenges, from the actual demands of E-commerce applications (as Made-inChina website), a personalized hybrid recommendation system, which can support massive data set, is designed and implemented in this paper by using Cloud technology. Hereinto, the recommendation algorithms are designed based on a novel user interesting model for different scenarios; and the massive data parallel processing techniques in Cloud computing is utilized to realize the effective execution of recommendation algorithms. Finally, several experiments are presented to highlight the system performance. (1) to model user's interests more accurately; (2) to provide more diverse recommendation modes; and (3) to support large-scale expansion. To address these challenges, from the actual demands of E-commerce applications (as Made-in-China website), a personalized hybrid recommendation system, which can support massive data set, is designed and implemented in this paper by using Cloud technology. There are three parts of this paper, the first part is to introduce the recommendation algorithms which are designed for different demands; In the second part, the massive data parallel processing techniques in Cloud computing is utilized to realize the effective execution of recommendation algorithms; At last, the real personalized hybrid recommendation system and relevant algorithms have been implemented and deployed upon SEUCloud Platform, then several experiments are presented to highlight the system performance.

Summary RDF knowledge graphs (KG) usually contain billions of labeled entities, and how to obtain the desired results efficiently on RDF KG for given SPARQL queries have attracted increasing attentions recently. However, it is difficult for users to write a complex SPARQL query without full knowledge of the underlying KG schema due to the “schema‐free” feature of RDF KG. In this paper, we study the problem of acquiring semantic approximate results for a simple SPARQL query instead of the complex expression. The basic idea behind is to use the real knowledge semantics to extend the query intention of given simplified SPARQL query, and then a semantic based greedy search over is designed to return top‐k similar results. To obtain the knowledge semantics efficiently, we define type similarity to reorganize the original KG as a corpus with semantic locality and then a context aware text embedding model is adopted to achieve the semantic vectors of existing knowledge. Afterwards, an approximate query method over RDF KG is designed to obtain top‐k similar results based on the knowledge semantics above. Extensive experiments over DBpedia dataset and QALD‐4 benchmark confirm our solution's superiority on both effectiveness and efficiency.

Global warming has led to changes in rainfall patterns in many regions and it has an increasing impact on the availability of water for plants, especially in the arid and semi-arid regions. Seed germination is the most critical stage in the plant life cycle, it determines whether or not the population can successfully establish. Here, we assessed the seed germination characteristics of Seriphidium transiliense under six water potentials and four temperature regimes. S. transiliense seeds could germinate from 5/15°C to 20/30°C, while the optimum temperature regime was 10/20°C. As water potential decreased, the germination percentage, germination index, germination energy, vigour index, plumule length and radicle length increased and then decreased, while mean time to germinate decreased and then increased. The optimum condition for S. transiliense seed germination was -0.2 MPa at 10/20°C. Some seeds that failed to germinate under drought conditions were transferred to distilled water and recovered germination ability.