Sequential recommendation models temporal patterns in user interaction sequences to capture dynamic preference changes. However, real-world user interaction data suffer from sparsity, hindering effective preference learning and limiting sequential recommendation model performance. Although existing studies employ time interval-aware data augmentation to address data sparsity, they inadequately mitigate popularity bias, leading to recommendations containing too many popular items. Consequently, this article proposes popularity bias mitigation based on time interval-aware data augmentation (TiPBMRec), which is described as a two-stage framework. In the first stage, TiPBMRec augments user interaction sequences using an item reshaper and a sequence refiner, dynamically generating augmented sequences. In the second stage, the augmented sequences are used to construct the time interval-aware dual-view graph and dual-channel conformity weight network, effectively capturing the changing patterns of user preferences. Experiments on real-world datasets demonstrate that TiPBMRec might be more optimal and achieve the popularity bias mitigation. This study provides a novel idea for exploring the combination of time interval-aware data augmentation and popularity bias mitigation. The related methods and conclusions might be valuable for enabling adaptive sequential recommendations.

To improve the particle swarm optimization algorithm's population diversity and global search ability, this study proposes a novel particle swarm optimization algorithm for meta-heuristic analysis mechanism based on population learning strategies and adaptive selection of leadership particles (ASLPSO) to achieve the fusion of population learning strategy and adaptive selection method of leadership particles. The particle swarm is adaptively separated into several populations through density peak clustering. Meanwhile, a new learning strategy is designed for analyzing the local optimal particles of each subgroup, that might enable the ordinary particles to learn effectively. The global optimum is relatively ensured by comparing the fitness value of each local optimal particle, which might obtain the best performances of multi-peak reference functions. After comparing with the approximate algorithms on multiple benchmark functions, it is found that the standard deviation and error mean value are improved. Therefore, ASLPSO has enhanced the population diversity and global search ability, prevented the algorithm from falling into premature too early.

More and more Web services raise the demands of personalized service recommendation; there exist some recommendation technologies, which improve the qualities of service recommendation by using service ranking and collaborative filtering. However, privacy and security are also important issues in service scheduling process; social relationships have been the key factors of interpersonal communication; service selection based on user preferences has become an inevitable trend. Starting from user demand preferences, this paper analyzes social topology and service demand information and obtains trusted social relationships; then we construct the fusion model of service historical preferences and potential ones; according to social service recommendation demands, TSRSR algorithm has completed designing. Through experiments, TSRSR algorithm is much better than the others, which can effectively improve potential preferences’ learning. Furthermore, the research results of this paper have more significance to study the security and privacy of service recommendation.

The QoS (quality of service) of a cloud service is not always trusted as advertised, due to the variable network environment or fake advertisement reasons. Therefore, to promote the trusted service selection, we should first evaluate the trust of each cloud service, based on its historical QoS records from past invocations. However, different from the traditional web service whose historical QoS record is a fixed value (e.g., a historical latency record of a web service is 2s), the historical QoS record of a cloud service is usually not fixed, but fluctuant during the long-running period of a single service invocation. For example, an virtual organization O rents cloud service WeatherInquiry between 8:00 am and 8:00 pm so that the employees of O can access WeatherInquiry. In this scenario, the latency of WeatherInquiry is fluctuant from the perspective of organization O, during the served 12 hours. In this situation, it is a challenge to evaluate the trust of service WeatherInquiry, based on WeatherInquiry's multiple historical QoS records that fluctuate continuously. In view of this challenge, we introduce a novel concept of flexible SLA, to better accommodate the fluctuant QoS of cloud service, and further put forward a trust evaluation method based on fluctuant QoS and flexible SLA, i.e., FL-FL (FLuctuant QoS-FLexible SLA-based trust evaluation method, FL-FL). Finally, a set of experiments are designed and deployed to validate the feasibility of our proposal, in terms of effectiveness and efficiency.

Due to the unstable network environment or fake advertisement reasons, the QoS (quality of service) data published by web service providers is not always trusted. Therefore, it is a promising way to evaluate the service quality, based on the historical QoS records generated from the past invocations of web services. However, for some web services with large numbers of historical QoS records, the evaluation efficiency is usually low and cannot meet the quick response requirements of subsequent service selection or service composition. Moreover, it will lead to the 'Lagging Effect' (i.e., The evaluation result cannot reflect the up-to-date quality change trend of a web service), if all the historical QoS records are treated equally in service evaluation. In view of these challenges, a novel service evaluation method named Partial-HR (Partial Historical Records-based service evaluation method) is put forward in this paper, by which only partial important historical QoS records are employed to evaluate the service quality. Finally, a set of experiments are designed and deployed to validate the feasibility of our proposal, in terms of evaluation accuracy and efficiency.