The recent years have witnessed a surge in the development of traffic flow prediction methods, often deployed on cloud platforms to offer predictive services for entire transportation networks. However, the processes of training and executing a model for the entire traffic network are both time-consuming and computationally expensive. As a result, the utilization of edge servers for local sub-network prediction services has gained prominence. Nevertheless, training prediction models for numerous sub-networks within the extensive traffic network remains a time-intensive and computing resource-consuming task. To tackle this challenge, this article introduces the Pre-trained model REcommendation Framework for Edge computing enabled tRaffic flow prediction (PREFER). PREFER trains a set of traffic flow prediction models on selected sub-networks, then recommends optimal pre-trained models for edge servers. The recommendation is specifically based on performance prediction, integrating neural collaborative filtering and traffic flow characteristics. Experiments conducted on real datasets reveal that the pre-trained models recommended by PREFER perform close to the actual optimal ones and significantly outperform existing recommendation algorithms.

With the development of cloud computing and the advent of the Web 2.0 era, composing a set of Web services as a service process is becoming a common practice to provide more functional services. However, a service process involves multiple service invocations over the network, which incurs a huge time cost and could become a bottleneck to performance. To accelerate its execution, we propose an engine-side cache-block-based service process caching strategy (CBPCS). It is based on, and derives its advantages from, three key ideas. First, the invocation of Web services embodies semantics, which enables the application of semantic-based caching. Second, cache blocks are identified from a service process, and each block is equipped with a separate cache so that the time overhead of service invocation and caching can be minimized. Third, a replacement strategy is introduced taking into account time and space factors to manage the space allocation for a process with multiple caches. The algorithms and methods used in CBPCS are introduced in detail. Moreover, how CBPCS can be applied to multiple service process models is also investigated. Finally, CBPCS is validated via comparison experiments, which shows the considerable improvements of CBPCS over other strategies.

To improve the performance of recommender systems in a practical manner, several hybrid approaches have been developed by considering item ratings and content information simultaneously. However, most of these hybrid approaches make recommendations based on aggregating different recommendation techniques using various strategies, rather than considering joint modeling of the item’s ratings and content, and thus fail to detect many latent factors that could potentially improve the performance of the recommender systems. For this reason, these approaches continue to suffer from data sparsity and do not work well for recommending items to individual users. A few studies try to describe a user’s preference by detecting items’ latent features from content-description texts as compensation for the sparse ratings. Unfortunately, most of these methods are still generally unable to accomplish recommendation tasks well for two reasons: (1) they learn latent factors from text descriptions or user--item ratings independently, rather than combining them together; and (2) influences of latent factors hidden in texts and ratings are not fully explored. In this study, we propose a probabilistic approach that we denote as latent random walk (LRW) based on the combination of an integrated latent topic model and random walk (RW) with the restart method, which can be used to rank items according to expected user preferences by detecting both their explicit and implicit correlative information, in order to recommend top-ranked items to potentially interested users. As presented in this article, the goal of this work is to comprehensively discover latent factors hidden in items’ ratings and content in order to alleviate the data sparsity problem and to improve the performance of recommender systems. The proposed topic model provides a generative probabilistic framework that discovers users’ implicit preferences and items’ latent features simultaneously by exploiting both ratings and item content information. On the basis of this probabilistic framework, RW can predict a user’s preference for unrated items by discovering global latent relations. In order to show the efficiency of the proposed approach, we test LRW and other state-of-the-art methods on three real-world datasets, namely, CAMRa2011, Yahoo!, and APP. The experiments indicate that our approach outperforms all comparative methods and, in addition, that it is less sensitive to the data sparsity problem, thus demonstrating the robustness of LRW for recommendation tasks.

Short-head and long-tail distributed data are widely observed in the real world. The same is true of recommender systems (RSs), where a small number of popular items dominate the choices and feedback data while the rest only account for a small amount of feedback. As a result, most RS methods tend to learn user preferences from popular items since they account for most data. However, recent research in e-commerce and marketing has shown that future businesses will obtain greater profit from long-tail selling. Yet, although the number of long-tail items and users is much larger than that of short-head items and users, in reality, the amount of data associated with long-tail items and users is much less. As a result, user preferences tend to be popularity-biased. Furthermore, insufficient data makes long-tail items and users more vulnerable to shilling attack. To improve the quality of recommendations for items and users in the tail of distribution, we propose a coupled regularization approach that consists of two latent factor models: C-HMF, for enhancing credibility, and S-HMF, for emphasizing specialty on user choices. Specifically, the estimates learned from C-HMF and S-HMF recurrently serve as the empirical priors to regularize one another. Such coupled regularization leads to the comprehensive effects of final estimates, which produce more qualitative predictions for both tail users and tail items. To assess the effectiveness of our model, we conduct empirical evaluations on large real-world datasets with various metrics. The results prove that our approach significantly outperforms the compared methods.