{'fraction_non_alphanumeric': 0.020209580838323353, 'fraction_numerical': 0.0, 'mean_word_length': 5.21860465116279, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Info-Bead group modeling in a mobile scenario

The mobile scenario is an extremely challenging one when it comes to providing personalized, context aware services to mobile users. Users may dynamically and continuously enter and leave smart environments that may offer them relevant services. However, the environments may not know anything about the users and hence, providing personalized, context aware services becomes a challenge: users need to be identified, queried for their preferences and monitored before a service can be provided. The lack of standard, easy to use personalization infrastructure worsens the problem -- every service provider needs to build a proprietary, add-hoc user modeling component from scratch, thus to invest considerable effort in the task. This work builds on top of previous work on Info-Beads user modeling. Following past research, it suggests an Info-Beads approach for mobile user modeling for monitoring users and enabling standardization in building user models, reusing both components and data. The specific contribution is to allow monitoring mobile users, reasoning on their data and creating individual and group models from it. We demonstrate the ideas in the area of media content recommendations for groups and individual mobile users in smart environments, as a possible case study.