Ya Zhang and Xiaokang Yang,

Shanghai Jiao Tong University Hongyuan Zha, Georgia Institute of Technology

In recent years, we have witnessed the flourish of Internet Protocol Television (IPTV)—television services over the In- ternet. IPTV offers three major types of video services: live television, time- shifted television, and video on demand (VOD), with the latter two representing

a paradigm shift from the traditional linear TV services. In particular, VOD service lets users browse a catalog of TV programs out of the usual TV program broadcasting sequence and select interesting ones to watch at their conve- nience. Powered by its feedback mecha- nism, IPTV enables service providers to collect information of fine-grained user activities, thus leading to a more inter- active and personalized TV-viewing experience. Behavior analysis for IPTV has contributed to mainly two aspects of IPTV services: quality of service and user experience. Behavior analysis has been used to optimize network plan- ning, system performance monitoring, and system efficiency analysis, in order

to offer improved quality of service. Understanding user behaviors is essential to improve IPTV user experience, which links to loyalty and revenues. Be-

havior informatics 1 is an emerging dis-

cipline that aims to gain an in-depth understanding of human behaviors. While behavior analysis has been extensively studied for many online domains, such as search engines and e-commerce, an IPTV account is usually associated with

a family rather than an individual. As a result, we can only observe the mixed behaviors of multiple family members under one IPTV account. These charac- teristics pose new opportunities as well as challenges for understanding IPTV user behaviors.

System Efficiency User behavior modeling has been used to solve IPTV system design and network efficiency issues. One key dif- ference between IPTV and traditional TV is that channel switching in IPTV imposes an extra load on the network. To reduce the channel-zapping delay, several studies have attempted to build user activity models to cap- ture events related to channel switching and Set-Top-Box (STB) power on/ off. It has been demonstrated that the efficiency of channel transmission and channel zapping may be improved by predicting the user’s viewing preferences based on their past behaviors. 2

Content delivery networks (CDNs) are a core component of the IPTV in- frastructure. Service providers have taken advantage of user behavior analysis to improve the CDN replica- tion model. Traffic models are built to match real IPTV CDN traffic so that the deployment and configuration of CDNs may be optimized. In addition, it has been observed that around 10 percent of the programs are consumed by 90 percent of the users (www. cs.ucsb.edu/~ravenben/publications/ pdf/vod-eurosys06.pdf). Accurately

predicting user’s behaviors and iden- tifying the top 10 percent of programs allows service providers to take full advantage of CDN efficiencies.

Personalization Another important line of research tar- gets provide personalized IPTV experience, such as personalized electronic program guides (EPG), personalized program recommendations, and personalized contextual advertisements. Personalization is a key differentiat- ing feature of IPTV services over traditional TV services. Modeling user preferences and interests is essential for personalization. IPTV programs span a diverse set of topics. The topics that a user frequently watches implic- itly reflect the user’s interests and back- ground. Several machine learning models have been proposed to predict user preferences of TV programs based on

a diverse set of user attributes, including activities, interests, moods, experi- ences, and demographic information. 3

In addition to what programs a user watches, when a user watches TV could embed important information about user properties. For example, it’s usually impossible for people who have

a daily job to watch TV during work- ing hours. Analysis of the user’s TV- viewing activities has revealed that the viewing count shows a strong weekly periodicity, with select TV programs that are more likely to be watched at certain time periods. For example, car- toon programs are frequently watched around 5–6 pm on weekdays. The viewing time of the programs could provide important clues for understanding user behaviors and their back- ground. IPTV user behavior modeling could benefit from a coupled analysis of viewing time and programs watched.

Recommendation The dramatic increases in the number of TV programs have made accessibility

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to TV programs a challenging issue. Recommendation plays an important role in helping users filter items of inter- est, including recommending preferred TV programs or contextual advertisements. Content-based filtering and collaborative filtering are two widely used approaches in recommender systems. In fact, hybrid approaches combin- ing collaborative filtering and content- based filtering have been demonstrated to be able to relieve the aforementioned problems to some extent. Over the last several years, many recommendation systems have been proposed to recom- mend TV programs.

Although recommender systems have been widely explored in many online domains for decades, recommendation for IPTV faces some unique challenges (as opposed to typical recommendation engines, such as Amazon’s prod- uct recommendation engine). First, be- cause IPTV is a family-based service subscription, multiple family members share the same account and it’s nontriv- ial to identify who the current viewer of a program is. Viewer identification is thus critical for delivering a personalized recommendation. Second, for cases such as VOD and news, program metadata and user activities could be too sparse to guarantee a reasonable content recommendation experience. IPTV recommendation must take advantage of a mixture of data sources. For example, external data sources such as Internet Movie Database (iMDB) and Twitter can be an invaluable information stream to enrich content metadata.

Catalyzed by recent advances in social media TV, viewing behaviors have become increasingly social. Many users, especially today’s youth, actively connect with others via social mes- saging platforms such as Twitter and Facebook while watching TV. Us- ers connect to the social media platforms to search for information about a program, learn about what

others are watching, and post about what they’re watching. Social media is shown to have a significant corre- lation with users’ TV viewing activities. Nielsen Twitter TV Ratings are proposed to measure the total activity and reach of TV-related conversations on Twitter. Hence, it’s important to wave social media into the TV-viewing activity data as an enhancement of our understanding of TV viewers’ behaviors. Recent studies have revealed that adding social regularization can significantly improve the accuracy of program recommendation. 4

Targeted Advertisement With the ability to profile users on IPTV, a trend for advertisement is to target certain ads at specific groups or individuals to increase their effective-

ness. 5 With targeted advertisement, users watching the same live TV program or VOD content receive different ads based on their profiles. While behavior targeting is nothing new to the computational advertising community, like recommendation, behavior targeting on IPTV faces the same problem of user identification. In addition, un- like Internet ads, IPTV advertisement is usually display advertisement. It’s very difficult, if not impossible, to track the conversion of IPTV advertisement in terms of click-and-purchase behaviors. To evaluate the effectiveness of IPTV advertisement would require pulling in many external data sources to perform such analysis. The challenges lie in the collection of the external data and the accurate match of external users with IPTV users.

Ya Zhang and Xiaokang Yang,