A Novel Recommender System in IoT
暂无分享,去创建一个
I. ABSTRACT In Internet of Things (IoT), selling smart physical objects together with a compatible mobile app becomes an upcoming trend. The app allows to control or monitor the physical object and its sensors in an easy, ubiquitous, and user friendly way. In this post, we present a novel recommender system which uses publicly available data about these apps as a source for personalization. The proposed system infers user’s physical objects from exploring the installed apps on her mobile devices like smartphones and tablets and builds a digital inventory of physical objects of each user. Then, these inventories can be used to create personalized recommendations. A. Background Smartphones have become our daily companions and support us in almost any situation of our daily life. We use them for communication, gaming, news reading, watching movies, payment, mobile banking and many others. The way we use smartphones is correlated with individuals’ needs, interests, habits, and personality [1]. Custom-tailored content like recommendations, advertisement, personalized prices and search results can be presented to consumers, based on observed activities on their devices [2]. From the perspective of IoT, smartphones are becoming increasingly important because they are used as hubs between the physical objects and Internet. For instance, smartphones provide dashboards to manage our smart home appliance (like heating system, light-bulb, key, TV) as well as retrieving and presenting data from our smart gadgets (like pedometers, body sensors). Since there is no method to automatically get a list of individuals’ physical objects, offering fitting recommendations in IoT is difficult. Recent studies propose to gather data about installed apps on smartphones as a way to get valuable user information and to derive user profiles [3]. We believe that the installed apps are also a door opener for gathering information about the connected physical objects and we are able to automatically
[1] Peng-Ting Chen,et al. Personalized mobile advertising: Its key attributes, trends, and social impact , 2012 .
[2] John Riedl,et al. Do You Trust Your Recommendations? An Exploration of Security and Privacy Issues in Recommender Systems , 2006, ETRICS.
[3] David N. Chin,et al. Social Media Sources for Personality Profiling , 2014, UMAP Workshops.
[4] Prasant Mohapatra,et al. Predicting user traits from a snapshot of apps installed on a smartphone , 2014, MOCO.