This paper presents the novel development of an embedded system that aims at digital TV content recommendation based on descriptive metadata collected from versatile sources. The described system comprises a user profiling subsystem identifying user preferences and a user agent subsystem performing content rating. TV content items are ranked using a combined multimodal approach integrating classification-based and keyword-based similarity predictions so that a user is presented with a limited subset of relevant content. Observable user behaviors are discussed as instrumental in user profiling and a formula is provided for implicitly estimating the degree of user appreciation of content. A new relation-based similarity measure is suggested to improve categorized content rating precision. Experimental results show that our system can recommend desired content to users with significant amount of accuracy.

With the rapid development of intelligent mobile terminals and communication technologies, location-based services (LBSs) have become an essential part of users’ lives. LBS providers upload and share the collected users’ location data. The more commonly used methods for location privacy protection are differential privacy and its extensions. However, the semantic information about location, which is an integral part of the location data, often contains sensitive user information. Most existing research methods have failed to pay enough attention to protecting the semantic information in the location data. To remedy this problem, two different scenarios for location semantic privacy protection methods are proposed in this paper to address single-point and continuous location queries. Simulation experiments on real social location check-in datasets, and comparison of three different privacy protection mechanisms, show that our solution demonstrates good service quality and privacy protection considering location semantics.

Most research on detecting shilling attacks focuses on users' rating behavior but does not consider that attackers may also attack the users' trusting behavior. For example, attackers may give a low score to other users' ratings so that people would think the ratings from the users are not helpful. In this paper, we define the trust shilling attack, propose the behavior features of trust attacks, and present an effective detection method using machine learning methods. The experimental results demonstrate that, based on our proposed behavior features of trust attacks, we can detect trust shilling attacks as well as traditional shilling attacks accurately.

Distributed appliances connected to the Internet have provided various multimedia services. In particular, networked Personal Video Recorders (PVRs) can store broadcast TV programs in their storage devices or receive them from central servers, enabling people to watch the programs they want at any desired time. However, the conventional CDNs capable of supporting a large number of concurrent users have limitations in scalability because more servers are required in proportion to the increased users. To address this problem, we have developed a time-shifted live streaming system over P2P networks so that PVRs can share TV programs with each other. We propose cooperative buffering schemes to provide the streaming services for time-shifted periods even when the number of PVRs playing back at the periods is not sufficient; we do so by utilizing the idle resources of the PVRs playing at the live broadcast time. To determine which chunks to be buffered, they consider the degree of deficiency and proximity and the ratio of playback requests to chunk copies. Through extensive simulations, we show that our proposed buffering schemes can significantly extend the time-shifting hours and compare the performance of two buffering schemes in terms of playback continuity and startup delay.