Agent based middleware for private data mashup in IPTV recommender services

Abstract

Data mashup is a web technology that combines information from multiple sources into a single web application. Mashup applications create a new horizon for different services like real estate services, financial services and recommender services. Recommender systems are a serious business tool for the providers of IPTV services, who seek to gain competitive advantage over competing providers and attract more customers. An IPTV provider utilizes data mashup to merge datasets from different movie recommendation sites like Netflix or IMDb in order to leverage its recommender performance and predication accuracy. However, mashup different datasets from multiple sources is a privacy hazard as it might revels customer specific preferences for different items. The ability to preserve privacy in mashuped datasets and in the same time provide accurate recommendations becomes a key success for the spread of mashup services. In this paper, we present our efforts to build an agent based middleware for private data mashup (AMPM) to serve centralized IPTV recommender service (CIRS). AMPM is equipped with obfuscation mechanisms to preserve privacy of the merged datasets form multiple sources involved in the mashup application. Also these mechanisms preserve the aggregates in the dataset to maximize the usability of information in order to attain accurate recommendations. We also provide a data mashup scenario in IPTV recommender service and experimentation results.