Personalized Electronic Programme Guide

With an increasing number of channels and programme providers, it has become difficult for users to find programmes of interest despite the existence of EPGs. This is because with traditional EPGs, the user needs to navigate through the whole EPG to find something interesting to watch and this wastes time. On the other hand, if a user becomes accustomed to watching certain kinds of programmes in certain channels, they might not experiment with different programmes in other channels which could also be of interest to them. Therefore, this problem needed to be addressed and one approach is to personalise the EPG based on a user’s viewing history from which the EPG can recommend a list of programmes which is appropriate to their preferences. This new generation of EPG does not just list the information which is provided by content providers but can also process and recommend a list of programmes to users.

Another personalized EPG has been presented for digital TV and runs on the user’s set- top-box. It creates a user model based on information supplied by the user. The system depends on three sources of information to manage the creation of a user model and these are the user’s explicit preferences that should are declared by the user, information about the TV viewers classes which classified based on programmes’ categories that are preferred by viewers and the user’s viewing behaviour. Having completed the user profile, the system creates the user model which is then used in recommending programmes to the user. This is done by matching the programme information which is extracted from the TV stream and channel’s EPG with the user model. Then the resulted programmes are ranked based on viewing behaviour (Ardissono et al., 2004). However, this proposed system doesn’t have the ability to adapt to any update in the user viewing behaviour or preferences especially for events which only occur occasionally. Moreover, the user experience can suffer from over-specialization and hence, they will not experience any new programmes which may be of interest to them.

Smyth has proposed a web based personalized EPG. The user needs to create an account on a web server and register to create his profile comprising, for example, TV preferences, preferred viewing time, and genre preferences. The most important information is grading feedback provided by the user. Then based on this, the system recommends a list of programmes and their transmission times via an EPG page. The recommendation process depends on content-based and collaborative filtering techniques which help the system to

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overcome the disadvantages of each technique individually. More details about those recommendation techniques will be described in the next sections. An overview of the system is shown in figure 2.3 (Smyth & Cotter, 2001).

Fig. 2.3 Architecture of Personal TV Guide System (Smyth & Cotter, 2001)

Whilst personalized EPGs use different schemes to recommend programmes, it is still difficult to produce a precise list of programmes for each user because of the limited profiling information that is available. Sullivan proposed a personalized EPG that uses data

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mining techniques to overcome the problem of sparsity in the collaborative filtering technique. That is because users are usually rating a small number of available programmes therefore, the expected rated programmes overlap between two random users is very low which cause the sparsity problem. Sullivan’s system employs a user profile and information filtering techniques to learn about user preferences and personalize the recommendations according to their needs. Data mining methods have been applied to extract new programme metadata from user profiles in order to recommend similar programmes. However, this system doesn’t address the other problems in the collaborative filtering technique namely, the first-rating problem and cold-start problem (O’Sullivan, Smyth, Wilson, Mcdonald, & Smeaton, 2004).

Another proposed system for personalizing the EPG was presented by Chen. This EPG is integrated into the viewing device and automatically monitors watched programmes and records the user’s viewing habits. The users’ feedback is then collected and forwarded to a community’ server in order to cluster the users into several virtual communities. The EPG system comprises content search, content recommendation and content management modules. The content search module is responsible for providing programmes that are simply requested by the user via keywords or by selecting programme categories. The content recommendation module recommends programmes using a community-based recommendation process in which the system monitors the viewed programmes from each user and divides the users into different virtual communities based on their viewing habits. The content management module records the users’ feedback about the recommended programmes and then processes and updates the recommendation process based on new information provided by the content management module. Figure 2.4 shows the block diagram of the community-based programme recommendation for the EPG system (Y.-C. Chen, Huang, & Huang, 2009).

Based on this existing body of research, in order to design a personalized EPG system which can interface with any video content source and provide personal recommendations to each individual user, major functional parts are needed. These comprise a searching system that can interface with any content provider, a recommendation system that offers a list of programmes that is matched to the user’s viewing behaviour and preferences, and an ability to create, store and manage user profiles. Also, an EPG system should have the ability to extract and analyse the data collected from the social networks. Therefore, the

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next sections of this chapter discuss research work relevant to these specific aspects of EPG design and operation.

Fig. 2.4 Community-Based Programme Recommendation for EPG System (Y.-C. Chen et al., 2009)