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RecSys'11 Workshop Outline PeMA 2011: Personalization in Mobile Applications

机译:RecSys'11工作坊提要PeMA 2011:移动应用中的个性化

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The rise of location-enabled mobile phones and location based services offers a great opportunity to apply personalization and recommender system technology to people's everyday lives. A variety of digital traces can now be used to infer how people move about their city and extract their context and habits. Personalization and recommender systems, potentially merged with the data that people store online (e.g., social networks, web ratings), can then not only be used to recommend new places and events that they may find interesting to attend, but, more broadly, personalize and enhance any service that people find themselves using. The goal of this one-day workshop was to bring together researchers and practitioners to explore, discuss, and understand challenges and new opportunities for personalisation within the urban domain. Topics of discussion during the workshop included: 1. Innovative applications of recommender systems in mobile settings; 2. Location and/or context-based recommender systems; 3. Recommending locations, venues, and social events; 4. Incentives and persuasion in mobile settings; 5. User-centric evaluation of personalised mobile services; 6. Recommender systems for smart cities/urban environments; 7. Intelligent transport systems; 8. Real-time and/or multi-source information processing for personalisation; 9. Personalized and adaptive mobile interfaces; 10. Security, privacy, reputation and trust issues in mobile recommenders; 11. Geographic/location-based social networks and social filtering; 12. Case studies of recommender systems in mobile environments.
机译:支持位置的移动电话和基于位置的服务的兴起为将个性化和推荐系统技术应用于人们的日常生活提供了绝佳的机会。现在可以使用各种数字踪迹来推断人们如何在城市中移动并提取其背景和习惯。可能与人们在线存储的数据(例如,社交网络,网络评分)合并的个性化和推荐系统,不仅可以用于推荐他们可能会感兴趣的新地点和活动,而且可以更广泛地进行个性化并增强人们发现自己使用的任何服务。这个为期一天的研讨会的目的是将研究人员和从业人员召集在一起,以探索,讨论和理解城市领域内个性化的挑战和新机会。研讨会期间讨论的主题包括:1.推荐系统在移动环境中的创新应用; 2.基于位置和/或上下文的推荐系统; 3.推荐地点,场所和社交活动; 4.移动环境中的激励和说服力; 5.以用户为中心的个性化移动服务评估; 6.用于智慧城市/城市环境的推荐系统; 7.智能交通系统; 8.实时和/或多源信息处理以进行个性化; 9.个性化和自适应的移动界面; 10.移动推荐器中的安全性,隐私,声誉和信任问题; 11.基于地理/位置的社交网络和社交过滤; 12.移动环境中推荐系统的案例研究。

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