A Link Prediction Strategy for Personalized Tweet Recommendation through Doc2Vec Approach

Nowadays with growth of using Internet as a principle way of communication, likes different social medias channels (Twitter, Facebook, etc.) and also access to huge amount of information like News, there appear a main research subject to help users to find his/her interests among vast amount of relevant and irrelevant information. Recommender systems are helped to handle information overload problem and in this paper we introduce our Tweet Recommendation System that implement user’s Twitter information (Tweets, Retweet, Like,...) as a source of user’s information. In this work the semantic of tweets that regard as a User’s Explicit Interests (e.g., person, events, product mentioned in user’s tweets) are identified with the Doc2vec approach and recommend similar tweets through link-prediction strategy. The experiment results show that Doc2Vec approach is a better approach than the other previous approaches

Une Approche de recommandation proactive dans un environnement mobile

Les systèmes de recommandation contextuelle visent à combiner un ensemble de technologies et de connaissances sur le contexte de l’utilisateur pour lui fournir une information pertinente au moment où il en a le plus besoin, c’est ce qu’on appelle la recommandation proactive. Dans cet article nous proposons une approche de recommandation contextuelle et proactive dans un environnement mobile qui apprend implicitement les préférences de l’utilisateur. Nous avons évalué notre approche dans le cadre de la tâche “Contextual Suggestion Track” de TREC 2014. Les résultats que nous avons obtenus sont prometteurs

Inferring user interests in microblogging social networks: a survey

With the growing popularity of microblogging services such as Twitter in recent years, an increasing number of users are using these services in their daily lives. The huge volume of information generated by users raises new opportunities in various applications and areas. Inferring user interests plays a significant role in providing personalized recommendations on microblogging services, and also on third-party applications providing social logins via these services, especially in cold-start situations. In this survey, we review user modeling strategies with respect to inferring user interests from previous studies. To this end, we focus on four dimensions of inferring user interest profiles: (1) data collection, (2) representation of user interest profiles, (3) construction and enhancement of user interest profiles, and (4) the evaluation of the constructed profiles. Through this survey, we aim to provide an overview of state-of-the-art user modeling strategies for inferring user interest profiles on microblogging social networks with respect to the four dimensions. For each dimension, we review and summarize previous studies based on specified criteria. Finally, we discuss some challenges and opportunities for future work in this research domain

알고리즘에 기반한 개인화되고 상호작용적인 뉴스 생성에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 : 언론정보학과, 2017. 2. 이준환.Algorithms are increasingly playing an important role in the production of news content with growing computational capacity. Moreover, the use of the algorithm is taking up traditional human roles as increasing number of journalistic activities are mediated by software. For instance, the Los Angele Times runs software called Quakebot, which makes automated decisions on publishing news articles on abnormal seismic events. The Associated Press and Forbes have long been publishing algorithm-generated news content in collaboration with narrative-generation algorithm developers since 2014. The Washington Post also joined the trend by developing news reporting software for 2016 Rio Olympics. We were motivated by the advent of various algorithm-generated news products. We reviewed current practices of algorithm-generated news and classified common algorithmic attributes to derive insights on how to maximize the capacity of the algorithm for more engaging and appealing news content generation. The key opportunity areas we found were 1) broadening depth and breadth of input data enriches algorithmic computation, 2) personalizing the narrative in the context of news readers raises interest, 3) presenting interactive user interface components helps to engage news readers and make them more active news consumers. We designed an algorithmic framework based on the proposed key concepts and implemented a news generation system called PINGS, which is capable of generating more personalized and interactive news stories. In this thesis, we describe the design process and implementation details that shaped the PINGS. We present a study on how news readers perceive the news values of the content generated by PINGS as well as the comments and opinions on its potential influence in the field and usability and usefulness of the system by recruiting experts for qualitative review. This thesis includes discussions on our approach to design and implement personalization and interactivity functions into a news system, and contributions it makes to the fields of journalism and HCI.I. Introduction 1 II. Theoretical Background: The Algorithmic Turn in Journalism 9 2.1 The Computational Turn in Media 9 2.2 Computational Journalism 14 2.3 The Algorithmic Turn in Journalism 19 2.4 Algorithmic News Generation Process 24 III. Practices of Algorithmic News Generation 29 3.1 Overview 29 3.2 Types of Algorithm-generated News 35 3.3 Analysis of Algorithmic Attributes 49 3.4 Discussion 56 IV. Research Questions 62 V. Developing Algorithm Framework for News Generation 68 5.1 Opportunities for Algorithmic News Generation 68 5.2 Algorithm Framework for News Generation 79 5.3 Discussion 91 VI. Design and Evaluation of the PINGS: Personalized and Interactive News Generation System 97 6.1 Overview 97 6.2 Underlying Framework Development 100 6.3 Design and Implementation of PINGS 115 6.4 Evaluation of PINGS 133 6.5 Discussion 152 VII. Discussion for Algorithmic News Generation 157 7.1 Discussion 157 7.2 Contributions 165 7.3 Limitations 169 VIII.Conclusion 174 8.1 Summary of Work 174 8.2 Opportunities for Future Work 176 References 178 Appendix A: Algorithm News Products 188 Appendix B: Study Materials 193 국문초록 204Docto