Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The emergence of what is called the social b and the continuing stream of new applications and community-based platforms including Facebook, Twitter, LinkedIn and others had a substantial impact on recommender systems research and practice over the last years in different ways.   First, today's b users are more willing to share more about themselves than before the b 2.0, thus providing more information sources that can be leveraged in the user modeling and recommendation process. Furthermore, the newly available information sources can not only be used to optimize the recommendations for an individual user, but can also help to identify more general patterns and trends in the behavior of the community that can be exploited by other applications.   Second, personalization, information filtering and recommendation are often the central functionality of many of these social b based applications. On typical social networks, users for example get recommendations for news to read, songs to listen to, groups to join, friends to follow, people to connect or jobs that might be interesting.   These developments lead to different challenges to be addressed in recommender systems research. On the one hand, for example, the question arises of how to effectively combine the huge variety of information sources for improved recommendations. On the other hand, regarding the new opportunities for applying recommender systems in social b environments, in many cases new techniques are required to address the particularities of the domain or to deal with scalability issues.   The ACM RecSys 2014 Workshop on Recommender Systems and the Social b aims to be a platform for researchers from academia and industry as ll as for practitioners to present and discuss the various challenges and possible solutions related to all aspects of social b recommendations. The call for papers correspondingly covered a variety of topics in this area including all sorts of applications of recommender systems technology and their interfaces; collective knowledge creation and topic emergence;context-aware and group recommendation approaches; and case studies and empirical evaluations.   This year's workshop was already the sixth in a series of successful workshops co-located with the ACM Conference on Recommender Systems since 2009. Again,  received several submissions from researchers from academia and industry which re thoroughly revied and selected for presentation at the workshop by a program committee of international experts in the field.   The papers submitted to the workshop addressed a number of different topics and put forward novel proposals to build social b recommender system. In the context of applying recommendation technology to information personalization and resource ranking problems in Social b environments, the submitted papers for example dealt with the problem of ranking community-provided product reviews based on opinion mining or with the recommendation of friends on social networks. As an example of how to leverage Social b information to build better systems, one of the works proposed to analyze the characteristics of publicly shared music playlists to better understand how future music recommendation systems should be designed. Finally, another contribution from industry addressed challenges and lessons learned when building large-scale collaborative filtering solutions on Social b platforms in a real-world environment.
['Bamshad Mobasher', 'Dietmar Jannach', 'Werner Geyer', 'Jill Freyne', 'Andreas Hotho', 'Sarabjot Singh Anand', 'Ido Guy']
I am writing a research paper on a idea The fifth ACM RecSys workshop on recommender systems and the social web
[{"from": "human", "value": "I am writing a research paper on a idea The fifth ACM RecSys workshop on recommender systems and the social web"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The emergence of what is called the social b and the continuing stream of new applications and community-based platforms including Facebook, Twitter, LinkedIn and others had a substantial impact on recommender systems research and practice over the last years in different ways.   First, today's b users are more willing to share more about themselves than before the b 2.0, thus providing more information sources that can be leveraged in the user modeling and recommendation process. Furthermore, the newly available information sources can not only be used to optimize the recommendations for an individual user, but can also help to identify more general patterns and trends in the behavior of the community that can be exploited by other applications.   Second, personalization, information filtering and recommendation are often the central functionality of many of these social b based applications. On typical social networks, users for example get recommendations for news to read, songs to listen to, groups to join, friends to follow, people to connect or jobs that might be interesting.   These developments lead to different challenges to be addressed in recommender systems research. On the one hand, for example, the question arises of how to effectively combine the huge variety of information sources for improved recommendations. On the other hand, regarding the new opportunities for applying recommender systems in social b environments, in many cases new techniques are required to address the particularities of the domain or to deal with scalability issues.   The ACM RecSys 2014 Workshop on Recommender Systems and the Social b aims to be a platform for researchers from academia and industry as ll as for practitioners to present and discuss the various challenges and possible solutions related to all aspects of social b recommendations. The call for papers correspondingly covered a variety of topics in this area including all sorts of applications of recommender systems technology and their interfaces; collective knowledge creation and topic emergence;context-aware and group recommendation approaches; and case studies and empirical evaluations.   This year's workshop was already the sixth in a series of successful workshops co-located with the ACM Conference on Recommender Systems since 2009. Again,  received several submissions from researchers from academia and industry which re thoroughly revied and selected for presentation at the workshop by a program committee of international experts in the field.   The papers submitted to the workshop addressed a number of different topics and put forward novel proposals to build social b recommender system. In the context of applying recommendation technology to information personalization and resource ranking problems in Social b environments, the submitted papers for example dealt with the problem of ranking community-provided product reviews based on opinion mining or with the recommendation of friends on social networks. As an example of how to leverage Social b information to build better systems, one of the works proposed to analyze the characteristics of publicly shared music playlists to better understand how future music recommendation systems should be designed. Finally, another contribution from industry addressed challenges and lessons learned when building large-scale collaborative filtering solutions on Social b platforms in a real-world environment."}]