Version-sensitive mobile app recommendation

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

The Research on Key Technologies and Methods for Mobile Applications Recommendation

随着移动互联网及智能移动终端的迅猛发展，用户对移动网络的访问逐渐从在线网络转移到手机应用客户端。手机应用消费者需要花费更多的时间挑选满足其需求的手机应用，手机应用开发者同时也会开发更多功能、更加精致的手机应用来引起消费者的注意。然而，不断增长的手机应用数量以及其多样的应用功能使得用户很难查找到真正符合他们需求的手机应用，也即信息过载问题。推荐系统的提出很好地缓解了信息过载的问题，其对用户推荐项目主要通过基于项目内容来对目标用户兴趣进行匹配（基于内容过滤）、挖掘其他类似用户的兴趣（协同过滤）、或者两者相结合（混合过滤）等途径。然而，对手机应用推荐算法的设计显著不同于传统领域（例如，电影、音乐、图...With the rapid development of mobile internet and smart mobile terminals, users' access to mobile network has transformed from online world to mobile applications (Apps). Mobile consumers are hence spending more time on selecting their desired Apps, App developers are also creating more functional and sophisticated Apps to command consumers' attention. However, the sheer number of Apps and their d...学位：工学博士院系专业：航空航天学院_系统工程学号：2322014015437

Smartphone App Usage Analysis : Datasets, Methods, and Applications

As smartphones have become indispensable personal devices, the number of smartphone users has increased dramatically over the last decade. These personal devices, which are supported by a variety of smartphone apps, allow people to access Internet services in a convenient and ubiquitous manner. App developers and service providers can collect fine-grained app usage traces, revealing connections between users, apps, and smartphones. We present a comprehensive review of the most recent research on smartphone app usage analysis in this survey. Our survey summarizes advanced technologies and key patterns in smartphone app usage behaviors, all of which have significant implications for all relevant stakeholders, including academia and industry. We begin by describing four data collection methods: surveys, monitoring apps, network operators, and app stores, as well as nine publicly available app usage datasets. We then systematically summarize the related studies of app usage analysis in three domains: app domain, user domain, and smartphone domain. We make a detailed taxonomy of the problem studied, the datasets used, the methods used, and the significant results obtained in each domain. Finally, we discuss future directions in this exciting field by highlighting research challenges.Peer reviewe

Sistema de recomendação de videojogos

Esta dissertação, foca-se no estudo e comparação do desempenho de algoritmos de recomendação baseados em filtragem colaborativa, com o objetivo de propor um sistema de recomendação de videojogos. Esse sistema utiliza informações provenientes da plataforma Steam, que podem ser descritos como dados implícitos, e que posteriormente foram transformados em classificações explícitas para serem usadas nos algoritmos. Os algoritmos foram implementados com recurso à biblioteca Surprise, que permite criar e avaliar sistemas de recomendação baseados em dados explícitos. O trabalho foca-se em abordagens computacionalmente menos exigentes, demostrando que as mesmas podem obter bons resultados. Os algoritmos são avaliados e comparados entre si usando métricas como RSME, MAE, Precision@k, Recall@k e [email protected] dissertation focuses on the study and compare of the performance of collaborative filtering algorithms, with the intent of proposing a videogame-oriented recommendation system. This system uses information from the video game platform “Steam”, which can be described as implicit feedback, and that were later transformed into explicit feedback. These algorithms were implemented using Python’s Surprise library, that allows to create and evaluate recommender systems that deal with explicit data. The work focuses on computationally fewer demanding approaches, demonstrating that they can obtain good results. The algorithms are evaluated and compared with each other using metrics such as RSME, MAE, Precision@k, Recall@k and F1@k