## 1 Introduction  
The recommender system has been recognized as an important issue in the data mining field and actively researched over decades [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. It is a kind of information filtering system that predicts users' ratings or preferences for items and provides users with items that they may be interested in. It is applied in a variety of domains such as movies, music, books, news, and any other contents in general [1; 4; 18; 20; 22; 25; 26; 27; 28; 29]. As a result, many recommendation algorithms have been developed for these domains in recent years. Since each domain has different characteristics, a clear grasp of the properties of a target domain is essential to develop a good recommendation algorithm. Television (TV) is the most popular viewing device for multimedia content distribution, so it is one of the most important domains for recommender systems. The number of channels is increasing, such as drama, entertainment, sports, and news, since the broadcast has changed from analog to digital. Usually, more than several tens of channels are provided by broadcast service providers. Some broadcast service providers provide more than one hundred channels. Users have a lot of choices, but they may spend a lot of time and feel confused when they try to search their favorite TV programs. Because of this issue, some broadcast service providers provide TV program information using an electronic program guide (EPG) [2; 3]. An EPG provides viewers with continuously updating menus displaying schedule information for current and upcoming programs. The schedule information includes the details of TV programs such as title, genre, synopsis, and actors. An EPG provides useful information to help channel selection, but it does not provide personalized recommendations. A lot of personalized TV content recommendation approaches are based on collaborative filtering (CF) or content-based filtering (CBF), which is also popular regarding movie recommendations [3; 4; 5; 6; 7; 8; 9; 10; 11; 12]. However, recommending programs in live TVs is more difficult and challenging compared to movies because of the following issues. First, the movie domain is unconstrained with time context, but the TV domain is time-constrained. Users can access any movies whenever they want to, but programs on live TVs are broadcast on a scheduled day and time. Since users' favorite TV programs are broadcast periodically, users tend to watch TV programs in periodic patterns. So, TV recommender systems need to consider time context to analyze users' watching patterns [14]. Moreover, a user's watching pattern can have multiple periodicities because TV programs have different broadcasting schedules. For example, if a user tends to watch a news program at 9 p.m. every day, it is regarded as a periodic pattern in hour-context. If a user is likely to watch sports programs every Saturday, the user's watching pattern may have a periodicity in day-context. Various periodicities are mixed up together in the watching patterns of users. Therefore, TV recommender systems need to model multiple time-contextual preferences of each user. Second, external user watch logs and TV program metadata are hard to be collected in standalone set-top box environments, in contrast to movie domains. Most TVs are not connected to the internet or cloud servers that store various external information. In general, the available information of TV programs in standalone environments are watching time, watching channels, and brief information on the programs, such as title and genre, from the EPG. Existing recommendation techniques such as CF and CBF, which require external users' feedback or metadata on programs, are not suitable to the TV domain. Thus, further studies for TV recommender system specialized in standalone environments are needed. In this paper, the issues in the TV domain mentioned above are considered.