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In this study, we focus on the problem of information expiration when using the traditional collaborative filtering algorithm and propose a new collaborative filtering algorithm by integrating the time factor (ITWCF). This algorithm considers information influence attenuation over time, introduces an information retention period based on the information half-value period, and proposes a time-weighted function, which is applied to the nearest neighbor selection and score prediction to assign different time weights to the scores. In addition, to further improve the quality of the nearest neighbor selection and alleviate the problem of data sparsity, a method of calculating users’ sentiment tendency by analysis of user review features is proposed to mine users’ attitudes about the reviewed items, which expands the score matrix. The time factor and sentiment tendency are then integrated into the K-means clustering algorithm to select the nearest neighbor. A hybrid collaborative filtering model (TWCHR) based on the improved K-means clustering algorithm is then proposed, by combining item-based and user-based collaborative filtering. Finally, the experimental results show that the proposed algorithm can address the time effect and sentiment analysis in recommendations and improve the predictive performance of the model.

In the collaborative filtering (CF) recommendation applications, the sparsity of user rating data, the effectiveness of cold start, the strategy of item information neglection, and user profiles construction are critical to both the efficiency and effectiveness of the recommendation algorithm. In order to solve the above problems, a personalized recommendation approach combining semisupervised support vector machine and active learning (AL) is proposed in this paper, which combines the benefits of both TSVM (Transductive Support Vector Machine) and AL. Firstly, a “maximum-minimum segmentation” of version space-based AL strategy is developed to choose the most informative unlabeled samples for human annotation; it aims to choose the least data which is enough to train a high-quality model. And then, an AL-based semisupervised TSVM algorithm is proposed to make full use of the distribution characteristics of unlabeled samples by adding a manifold regularization into objective function, which is helpful to make the proposed algorithm to overcome the traditional drawbacks of TSVM. Furthermore, during the procedure of recommendation model construction, not only user behavior information and item information, but also demographic information is utilized. Due to the benefits of the above design, the quality of unlabeled sample annotation can be improved; meanwhile, both the data sparsity and cold start problems are alleviated. Finally, the effectiveness of the proposed algorithm is verified based on UCI datasets, and then it is applied to personalized recommendation. The experimental results show the superiority of the proposed method in both effectiveness and efficiency.

Crystalline nickel-phosphide (c-Ni-P) plating is a newly developed mold material for precision glass molding (PGM) to fabricate microgrooves. In the ultraprecision cutting process of the c-Ni-P plating material, the neighboring microgrooves are required to adjoin with each other to ensure acute microgroove ridges and miniaturize the microgroove size. Generally, defects of burrs and fracture pits can easily occur on the ridges when the plating layer is grooved. Burrs appear when tears dominate in material removal with a large adjacent amount. With the change of the adjacent amount, the removed material is sheared out from the workpiece, and when the cutting depth of the groove ridge is over the brittle-ductile transition thickness, fracture pits arise. To restrict these defects, a small cross-angle microgrooving method is proposed to test the critical adjacent amount range efficiently. It is found that an acute ridge of the microgroove is formed with a small enough adjacent amount; when this amount is in the range of 570 nm~720 nm in the microgroove machining process, fracture pits begin to arise on the gradient edge. High-quality microgrooves can be obtained based on this methodology.

Due to the widespread use of high-accuracy miniature and micro features or components, it is required to predict the machined surface performance of the micro milling processes. In this paper, a new predictive model of the surface roughness is established by response surface method (RSM) according to the micro milling experiment of 6061-T6 aluminum alloy which is carried out based on the central composite circumscribed (CCC) design. Then the model is used to analyze the effects of parameters on the surface roughness, and it can be concluded that the surface roughness increases with the increasing of the feed rate and the decreasing of the spindle speed. At last, based on the model the contour map of the surface roughness and material removal rate is established for optimizing the process parameters to improve the cutting efficiency with good surface roughness. The prediction results from the model have good agreement with the experimental results.