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• A fast weighted CP decomposition model for context-aware recommendation. • Unifying explicit and implicit feedback by assigning non-uniform weights. • Reducing computation time by element-wise alternating least squares. • A simple and effective multi-threaded computing scheme for above model. As an effective method for high-dimensional information processing, tensor factorization has been widely used in context-aware recommendation. However, the multi-dimensional processing of tensor factorization usually leads to more serious data sparsity. Making full use of the data in recommendation systems, including a small amount of explicit rating data and a large amount of implicit feedback data such as clicks, plays, and purchases, can effectively alleviate this problem. Meanwhile, the processing of sparse explicit data and huge implicit feedback data makes tensor factorization face a severe challenge of computational efficiency. Therefore, this study proposes a fast tensor factorization model named FWCP that unifies explicit and implicit feedback information, which mainly studies as follows: (1) Assigning non-uniform weights to observed and unobserved values to unify explicit and implicit feedback information; (2) Memorizing intermediate data which generated by element-wise alternating least squares(ALS), and implementing parallel optimization of FWCP, which can reduce the training time of tensor factorization based on whole-data. The evaluation results on two real datasets showed that FWCP proposed in this study is superior to other context-aware recommendation algorithms. [ABSTRACT FROM AUTHOR]

The CO2 emissions from the cement industry and the production of waste wood chips are increasing with the rapid growth of the construction industry. In order to develop a green environmental protection building material with low thermal conductivity and up to standard mechanical properties, in this study, pine waste wood chips were mixed into cement-based materials as fine aggregate, and three different kinds of cementitious binders were used, including sulfur aluminate cement (SAC), ordinary Portland cement (OPC), and granulated blast furnace slag (GBFS), to prepare a recycled light cementitious composite material. The mechanical, thermal conductivity, shrinkage, water absorption, and pore structure of a wood chip light cementitious composite material were studied by changing the Ch/B (the mass ratio of wood chip to binder). The results showed that the strength, dry density, and thermal conductivity of the specimens decreased significantly with the increase in the Ch/B, while the shrinkage, water absorption, and pore size increased with the increase in the Ch/B. By comparing three different kinds of cementitious binders, the dry density of the material prepared with OPC was 942 kg/m3, the compressive strength of the material prepared with SAC was 13.5 MPa, and the thermal conductivity of the material prepared with slag was the lowest at 0.15 W/m/K. From the perspective of low-cost and low-carbon emissions, it was determined that the best way to prepare a light cementitious composite with waste wood chips is to use granulated blast furnace slag (GBFS) as the cementitious binder. [ABSTRACT FROM AUTHOR]

Poor mechanical properties and durability of recycled aggregate concrete (RAC) hinder its application in the construction field. In this study, pre-wetted recycled coarse aggregate was used as the internal curing material for prepared RAC with low water-to-binder ratio (W/B), aiming to improve the mechanical properties and durability. The results show that the workability decreases with increasing contents of pre-wetted recycled coarse aggregate. The variation in compressive strength of RAC with different contents of pre-wetted recycled coarse aggregate is obvious within 28 d. After 28 d, the effect of internal curing of pre-wetted recycled coarse aggregate starts to occur, causing a sustained increase in compressive strength. The sealed concrete with 50% and 75% pre-wetted recycled coarse aggregate contents presents the highest compressive strength and better internal curing effect. The pre-wetted recycled coarse aggregate decreases the relative humidity inside the concrete and effectively inhibits the development of shrinkage in the early stages. The RAC with pre-wetted recycled coarse aggregate presents little effect on the drying shrinkage. Additionally, the electric flux of RAC cured for 28 d increases from 561C to 1001C, which presents good resistance to chloride permeation. Microscopic tests indicate that the incorporation of pre-wetted recycled coarse aggregate is beneficial to the improvements of internal structure of RAC. [ABSTRACT FROM AUTHOR]

In recent years, context information has been widely used in recommender systems. Tensor factorization is an effective method to process high-dimensional information. However, data sparsity is more serious in tensor factorization, and it is difficult to build a more accurate recommender system only based on user–item–context interaction information. Making full use of user's social information and implicit feedback can alleviate this problem. In this paper, we propose a new tensor factorization model named TrustTF, which mainly works as follows: (1) Using user's social trust information and implicit feedback to extend the bias tensor factorization (BiasTF), effectively alleviate data sparsity problem and improve the recommendation accuracy; (2) Dividing user's trust relationship into unilateral trust and mutual trust, which makes better use of user's social information. To our knowledge, this is the first work to consider the effects of both user trust and implicit feedback on the basis of the BiasTF model. The experimental results in two real-world data sets demonstrate that the TrustTF proposed in this paper can achieve higher accuracy than BiasTF and other social recommendation methods. [ABSTRACT FROM AUTHOR]