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Abstr act. Social influence plays an important role in product marketing. However, it has rarely been considered in traditional recommender systems. In this paper we present a new paradigm of recommender systems which can utilize information in social networks, including user preferences, item's general acceptance, and influence from social friends. A probabilistic model is developed to make personalized recommendations from such information. We extract data from a real online social network, and our analysis of this large dataset reveals that friends have a tendency to select the same items and give similar ratings. Experimental results on this dataset show that our proposed system not only improves the prediction accuracy of recommender systems but also remedies the data sparsity and coldstart issues inherent in collaborative filtering. Furthermore, we propose to improve the performance of our system by applying semantic filtering of social networks, and validate its improvement via a class project experiment. In this experiment we demonstrate how relevant friends can be selected for inference based on the semantics of friend relationships and finer-grained user ratings. Such technologies can be deployed by most content providers. Intr oductionIn order to overcome information overload, recommender systems have become a key tool for providing users with personalized recommendations on items such as movies, music, books, news, and web pages. Intrigued by many practical applications, researchers have developed algorithms and systems over the last decade. Some of them have been commercialized by online venders such as Amazon.com, Netflix.com, and IMDb.com. These systems predict user preferences (often represented as numeric ratings) for new items based on the user's past ratings on other items. There are typically two types of algorithms for recommender systems --content-based methods and collaborative filtering. Content-based methods measure the similarity of the recommended item (target item) to the ones that a target user (i.e., user who receives recommendations) likes or dislikes [25,22,30] based on item attributes. On the other hand, collaborative filtering finds users with tastes that are similar to the target user's based on their past ratings. Collaborative filtering will then make recommendations to the target user based on the opinions of those similar users [3,5,27].Despite all of these efforts, recommender systems still face many challenging problems. First, there are demands for further improvements on the prediction ac-

The evolution of a solitary wave under the action of rotation is considered within the framework of the rotation‐modified Korteweg–de Vries equation. Using an asymptotic procedure, the solitary wave is shown to be damped due to radiation of a dispersive wave train propagating with the same phase velocity as the solitary wave. Such a synchronism is possible because of the presence of rotational dispersion. The law of damping is found to be “terminal” in the sense that the solitary wave disappears in a finite time. The radiated wave amplitude and the structure of the radiated “tail” in space–time are also found. Some numerical results, which confirm the approximate theory developed here, are given.

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Hydraulic fracturing and well drilling bring water into shale reservoirs. The water interacts with the shale, which can destabilize the wellbore and impact the rate of gas production from the reservoir. Although wellbore instability has been extensively studied, the effect on gas production requires further work. In this work, the interactions of water with shale from China's Sichuan Basin were studied from macroscopic and microscopic perspectives. The visual information provided by studying shale at the microscopic scale helps in understanding the effects of water−shale interactions on gas production. We first studied the shale's wettability and water adsorption capacity and then investigated its water adsorption characteristics, swelling strain, and Young's modulus with different water contents. Field-emission scanning electron microscopy was also used to observe the storage of water in the shale matrix and the interaction of shale minerals with water. Our experimental data show that the adsorption capacity of the shale is low, although it is overall hydrophilic. The adsorption data indicated that diffusion may be the main mechanism for water adsorption by the sample under our experimental conditions. Capillary pressure may also help transport water into the matrix. The shale sample exhibited free swelling and its Young's modulus decreased after uptake of water, which may be attributed to hydration of clay minerals in the shale sample. Microscopic observations showed that some water remained in the sample's pores even when the water vapor pressure was much lower than the saturation pressure. This suggests that the residual water may be difficult to remove from the reservoir and that hydraulic fluid would have a greater influence on smaller pores than on larger pores.

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