Video Recommendation System for YouTube Considering Users Feedback

Youtube is the most video sharing and viewing platform in the world. As there are many people of different tastes, hundreds of categories of videos can be found on YouTube while thousands of videos of each. So, when the site recommends videos for a user it takes some issues which fill the needs of the user. Most of the time a user watches videos related to the previously watched video. But sometimes userFFFD;s mood changes with time or weather. A user may not hear a song in the whole year but can search the song on a rainy day. Another case a user may watch some types of videos at day but another type of videos at night or another at midnight. In this paper, we propose a recommendation system considering some attributes like weather, time, month to understand the dynamic mood of a user. Each attribute is assigned a weight calculated by performing a survey on some YouTube users. Most recently viewed videos is assigned heavy weight and weather is assigned lower. This recommendation system will make YouTube more user-friendly, dynamic and acceptable

Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks

Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings. Recently, Recurrent Neural Networks have been shown to perform very well in session-based settings. While in many session-based recommendation domains user identifiers are hard to come by, there are also domains in which user profiles are readily available. We propose a seamless way to personalize RNN models with cross-session information transfer and devise a Hierarchical RNN model that relays end evolves latent hidden states of the RNNs across user sessions. Results on two industry datasets show large improvements over the session-only RNNs

Unraveling the Relationship between Content Quality, Design, and User Engagement in Douyin's Adolescent Mode

In recent years, short video social platforms have garnered widespread attention, with Douyin (the Chinese version of TikTok) being a typical representative. The daily active users of the teen segment in Douyin are estimated to exceed 42 million. However, there still exists a certain blind spot in the research of Douyin's teen-specific content. This study conducts a content analysis of 360 short videos in Douyin's adolescent mode, and employs a Structural Equation Model (SEM) to analyze the relationships between the content quality, design features, learning objectives, age appropriateness, and user engagement. The results reveal that age appropriateness and learning objectives have a significant positive impact on content quality, and content quality and design features significantly positively impact user engagement. Further discussions point out that Douyin's adolescent mode faces challenges in dealing with issues like gender bias and algorithm bias, which may have adverse effects on the holistic development of teen users. The research findings provide empirical evidence for short video creators and platform optimization, and put forward targeted suggestions for the design of short video content in adolescent mode

Mind over machine? The clash of agency in social media environments

Includes bibliographical references.2022 Fall.Underlying many social media platforms are choice recommendation "nudging" architectures designed to give users instant content and social recommendations to keep them engaged. Powered by complex algorithms, these architectures flush people's feeds and an array of other features with fresh content and create a highly individualized experience tailored to their interests. In a critical realist qualitative study, this research examines how individual agency manifests when users encounter these tools and the suggestions they provide. In interviews and focus groups, 45 participants offered their experiences where they reflected on how they perceived the engines, e.g., their Facebook feed, influenced their actions and behaviors, as well as how the participants felt they controlled it to achieve personal aims. Based on these and other experiences, this study posits the Social Cognitive Machine Agency Dynamic (SCMAD) model, which provides an empirically supported explanatory framework to explain how individual agency can manifest and progress in response to these tools. The model integrates Albert Bandura's social cognitive theory concepts and emergent findings. It demonstrates how users react to the engines through agentic expressions not dissimilar to the real-world, including enacting self-regulatory, self-reflective and intentionality processes, as well as other acts not captured by Bandura's theory. Ultimately, the research and model propose a psycho-environmental explanation of the swerves of agency experienced by users in reaction to the unique conditions and affordances of these algorithmically driven environments. The study is the first known extension of social cognitive theory to this technology context. Implications of the findings are discussed and recommendations for future research provided. The study recommends that future research and media discourse aim for an individual-level psychological evaluation of these powerful technologies. This stance will afford a greater understanding of the technology's impacts and implications on individuals, particularly as it is anticipated to significantly evolve in the coming years