The Influence and Fusion of Online Films with Traditional Cinema: A Case Study of the Netflix Platform

The emergence and popularization of streaming movies have witnessed the change in acceptance mode and acceptance psychology of traditional movie and television, and broke the confinement of time and space. Taking Netflix, a streaming online platform, as a case study, this research endeavors to explore the impact of streaming movies on traditional cinema movies and their convergence utilizing literature analysis, classification and comparative analysis, case study research method, and data collection and analysis method

DeePOF: A hybrid approach of deep convolutional neural network and friendship to Point‐of‐Interest (POI) recommendation system in location‐based social networks

Today, millions of active users spend a percentage of their time on location-based social networks like Yelp and Gowalla and share their rich information. They can easily learn about their friends\u27 behaviors and where they are visiting and be influenced by their style. As a result, the existence of personalized recommendations and the investigation of meaningful features of users and Point of Interests (POIs), given the challenges of rich contents and data sparsity, is a substantial task to accurately recommend the POIs and interests of users in location-based social networks (LBSNs). This work proposes a novel pipeline of POI recommendations named DeePOF based on deep learning and the convolutional neural network. This approach only takes into consideration the influence of the most similar pattern of friendship instead of the friendship of all users. The mean-shift clustering technique is used to detect similarity. The most similar friends\u27 spatial and temporal features are fed into our deep CNN technique. The output of several proposed layers can predict latitude and longitude and the ID of subsequent appropriate places, and then using the friendship interval of a similar pattern, the lowest distance venues are chosen. This combination method is estimated on two popular datasets of LBSNs. Experimental results demonstrate that analyzing similar friendships could make recommendations more accurate and the suggested model for recommending a sequence of top-k POIs outperforms state-of-the-art approaches

A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data

Tourism is an important application domain for recommender systems. In this domain, recommender systems are for example tasked with providing personalized recommendations for transportation, accommodation, points-of-interest (POIs), or tourism services. Among these tasks, in particular the problem of recommending POIs that are of likely interest to individual tourists has gained growing attention in recent years. Providing POI recommendations to tourists \emph{during their trip} can however be especially challenging due to the variability of the users' context. With the rapid development of the Web and today's multitude of online services, vast amounts of data from various sources have become available, and these heterogeneous data sources represent a huge potential to better address the challenges of in-trip POI recommendation problems. In this work, we provide a comprehensive survey of published research on POI recommendation between 2017 and 2022 from the perspective of heterogeneous data sources. Specifically, we investigate which types of data are used in the literature and which technical approaches and evaluation methods are predominant. Among other aspects, we find that today's research works often focus on a narrow range of data sources, leaving great potential for future works that better utilize heterogeneous data sources and diverse data types for improved in-trip recommendations.Comment: 35 pages, 19 figure

Decentralized Collaborative Learning Framework for Next POI Recommendation

Next Point-of-Interest (POI) recommendation has become an indispensable functionality in Location-based Social Networks (LBSNs) due to its effectiveness in helping people decide the next POI to visit. However, accurate recommendation requires a vast amount of historical check-in data, thus threatening user privacy as the location-sensitive data needs to be handled by cloud servers. Although there have been several on-device frameworks for privacy-preserving POI recommendations, they are still resource-intensive when it comes to storage and computation, and show limited robustness to the high sparsity of user-POI interactions. On this basis, we propose a novel decentralized collaborative learning framework for POI recommendation (DCLR), which allows users to train their personalized models locally in a collaborative manner. DCLR significantly reduces the local models' dependence on the cloud for training, and can be used to expand arbitrary centralized recommendation models. To counteract the sparsity of on-device user data when learning each local model, we design two self-supervision signals to pretrain the POI representations on the server with geographical and categorical correlations of POIs. To facilitate collaborative learning, we innovatively propose to incorporate knowledge from either geographically or semantically similar users into each local model with attentive aggregation and mutual information maximization. The collaborative learning process makes use of communications between devices while requiring only minor engagement from the central server for identifying user groups, and is compatible with common privacy preservation mechanisms like differential privacy. We evaluate DCLR with two real-world datasets, where the results show that DCLR outperforms state-of-the-art on-device frameworks and yields competitive results compared with centralized counterparts.Comment: 21 Pages, 3 figures, 4 table

Semi-Automated Location Planning for Urban Bike-Sharing Systems

Bike-sharing has developed into an established part of many urban transportation systems. However, new bikesharing systems (BSS) are still built and existing ones are extended. Particularly for large BSS, location planning is complex since factors determining potential usage are manifold. We propose a semi-automatic approach for creating or extending real-world sized BSS during general planning. Our approach optimizes locations such that the number of trips is maximized for a given budget respecting construction as well as operation costs. The approach consists of four steps: (1) collecting and preprocessing required data, (2) estimating a demand model, (3) calculating optimized locations considering estimated redistribution costs, and (4) presenting the solution to the planner in a visualization and planning front end. The full approach was implemented and evaluated positively with BSS and planning experts

An overview of video recommender systems: state-of-the-art and research issues

Video platforms have become indispensable components within a diverse range of applications, serving various purposes in entertainment, e-learning, corporate training, online documentation, and news provision. As the volume and complexity of video content continue to grow, the need for personalized access features becomes an inevitable requirement to ensure efficient content consumption. To address this need, recommender systems have emerged as helpful tools providing personalized video access. By leveraging past user-specific video consumption data and the preferences of similar users, these systems excel in recommending videos that are highly relevant to individual users. This article presents a comprehensive overview of the current state of video recommender systems (VRS), exploring the algorithms used, their applications, and related aspects. In addition to an in-depth analysis of existing approaches, this review also addresses unresolved research challenges within this domain. These unexplored areas offer exciting opportunities for advancements and innovations, aiming to enhance the accuracy and effectiveness of personalized video recommendations. Overall, this article serves as a valuable resource for researchers, practitioners, and stakeholders in the video domain. It offers insights into cutting-edge algorithms, successful applications, and areas that merit further exploration to advance the field of video recommendation

Landscapes and Sublime Memories: Revisiting Liang Xiaosheng's "A Land of Wonder and Mystery"

This essay suggests memory studies, ecocriticism, and trauma studies as new avenues for the study of rusticated youth narratives. Towards reaching this goal, I first introduce a meditation on memory by Paul Ricoeur (1913-2005), especially his sketch of memory and imagination with classical Greek philosophy. His ideas on affective and practical memories are then telescoped into individual and communal memories. Onze Fleurs (Wo shiyi, 2011), directed by Wang Xiaoshuai (1966- ), and The River without Buoys (Meiyou hangbiao de heliu, 1984), directed by Wu Tianming (1939-2014) provide illustrative examples of each. Building upon these notions of personal memory I turn to the popular memory of rustication, especially that of the natural environment in Liang Xiaosheng's "A Land of Wonder and Mystery" ("Zhe shi yipian shenqi de tudi," 1985). More specifically I examine the evocation of the ghost marsh, narratives of departure, the family left in the city, and the menace of nature in Liang's short story to force not only a reconsideration of rustication, but also of nature in contemporary China. Moreover, in addition to noting the questioning of the sanitization of rusticated memories as a means of conforming to dominant state ideological discourses, I introduce a comparison of the story of doomed rusticated youth to the doomed youth in Sean Penn's Into the Wild, in order to force a comparison of youth and the environment often overlooked in rusticated youth studies. Finally, this essay concludes by suggesting that by more carefully considering the interplay between memory and place more nuanced and perhaps more ecologically and critically engaged assessments of rusticated youth fiction become possible

Enhancing Warnings

Warnings are part of our everyday life, whether traffic lights, food health warnings, the weather, advice from colleagues, or moralistic stories. Warnings serve to provide cautionary advice, give advance notice of something, and generate awareness to trigger consequent decisions and actions. Warnings are seldom considered beyond the issuance of a warning, yet warnings are far more complex, requiring a comprehensive tool and system to help implement preventative, mitigative, and disaster risk-reductive actions. Warnings are not just a siren or phone alert but should be a long-term social process that is a carefully crafted, integrated system of preparedness involving vulnerability analysis and reduction, hazard monitoring and forecasting, disaster risk assessment, and communication. Together, these activities enable a wide range of leaders and others – such as individuals, local groups, governments, and businesses – to take timely and effective action to reduce disaster risks in advance of hazards. Warnings are represented via different iconographies and communicated via different mediums that usually express some form of threshold or tipping point. These vary enormously contingent on the hazard, and social, political, and economic context of the warning. Warnings should provide actionable guidance that is integrated into everyday life and behaviour, providing transparency and credibility to help manage risk in emerging and ongoing situations. Warnings must operate beyond the silos frequently seen in institutions, for different vulnerabilities, different hazards, and different stakeholders to become a long-term social process that can serve to bring together these diverse issues. This report examines how this can be implemented providing key case-study examples of lessons learnt and guidance on how to build effective warning systems. To enhance a warning requires placing it as part of a warning system, a long-term social process that embodies the 3 I’s ( Imagination, Initiative, Integration) and 3 E’s (Education, Exchange, Engagement). The authors offer three recommendations and provide guidance on how to implement these recommendations: Develop effective warnings that consider multiple-hazards, cascading events, and integration across stakeholders. Adopt a public engagement and outreach programme that empowers people to identify and fulfil their own needs regarding warnings for enhancing preparedness and response behaviours and actions. Create and support mechanisms to overcome silos and territorialism and instead encourage idea and action exchange for building trust and connections that support action when a major situation arises