Manuscript - WEBMEDIA 2014 – XX Simpósio Brasileiro de Sistemas Multimídia e Web
NON-SUPERVISED TV SCHEDULING SCHEME BASED ON COLLABORATIVE RATING Leonardo Farage Freitas
Heloisa Simon
Aldo von Wangenheim
INCoD – Brazilian Institute for Digital Convergence UFSC – Federal University of Santa Catarina
INCoD – Brazilian Institute for Digital Convergence UFSC – Federal University of Santa Catarina
[email protected] INCoD – Brazilian Institute for Digital Convergence UFSC – Federal University of Santa Catarina
ABSTRACT
based on viewers’ feedback and local content metadata. Schedules will be built using suggestions made by a recommender system. The system manages a content pool and uses ratings from viewers for recommending content to be aired, increasing schedules variety. This system can generate complete schedules or fill vacant timeslots left by the user.
The generation of the TV program schedule is a daily challenge. Companies need to broadcast the best content according to the audience’s preference. This paper propose a novel take on collaborative filtering techniques to automate the selection of content based on viewers’ rating through an interactive digital TV application. In order to overcome the fact that a television channel can broadcast only one schedule at any one time, we group all ratings based on Time Intervals in order to generalize viewers’ preference and present the most appropriete content based on a rating system as well as content frequency.
The remaining of the paper is organized as follows. In Section 2 we discuss tv programming and its challenges. Section 3 shows a recommender systems overview for tv programming. In Section 4 the proposed system is overviewed. Section 5 presents the conclusions we’ve reached.
Keywords
2. TV SCHEDULING
recommendation systems, television scheduler, collaborative filtering technique, interactive digital television, group profiling
Understanding a channel’s target audience is an immense challenge. TV scheduling involves careful analysis of demographics and reliance on exacting survey re-search methods [12]. Audience’s profile varies according to the time of week and day. Usually, the audience wants different shows on the weekend than they do during the week. Although there are weekly programs such as series, there is no guarantee that the audience is the same, week to week [14].
1. INTRODUCTION Handling the huge amount of content from a TV channel can make a challenge out of offering the most interesting content while respecting viewers’ preferences. As said in [12], television scheduling is both a science and an art that must evaluate the quality and appeal of new shows. Those responsible for scheduling need to know all content available and be aware of the censorship of the content with regard to suitability for audiences in terms of issues such as violence, impudence, mature content, etc. Small and local television channels, such as university channels, do not have large structures and may not have a large content pool. Those responsible for scheduling need to repeat content and merge it with new content. This paper presents a work in progress that proposes a nonsupervisioned approach for generating program schedules
A television channel involves three different professionals for generating its schedule. A program director, responsible for preparing the schedule. A scheduling council, depending on the size of the channel, is made for approving the schedule. And a program coordinator that manages broadcasting the schedule and insertion of commercials in between programs. A schedule consists of different types of programs: weekly, daily and thematic, such as news, series and movies, respectively. Selecting this content for the schedule takes decisions as: ”is the content appropriated for the time?”, ”when this content aired for the last time?”, ”how many times has it been aired?” and many others. These questions make the job of creating the schedule a hard job when not aided by some kind of system that can help keep track of the answers for the questions above.
3. RECOMMENDER SYSTEMS The idea of harnessing the opinions of millions of people online in order to help on find more useful and interesting content gave birth to the recommender systems in the early 90s [19]. Since then, recommender systems have proven their
Manuscript - WEBMEDIA 2014 – XX Simpósio Brasileiro de Sistemas Multimídia e Web usefulness in many contexts such as e-commerce, web search engines and a promising future on digital television [20]. Recommender systems deal with a set of users and a set of items. Each user has a profile with ratings given for items. Based on the ratings of an active user the system predicts which items the active user might like [20]. Users’ ratings can be acquired through implicit and explicit ways and the system employs filtering techniques that use these ratings along with content’s metadata for making predictions [5, 7, 15]. These techniques can be categorized into content-based filtering, collaborative filtering and hybrid approaches [1, 16, 18, 19]. Collaborative filtering uses the information about an active user, his user profile, and information from similar users, known as neighbours, to predict the utility or relevance of an item.For predicting the possible ratings an active user might give for an item, its neighbors are used as base for making these predictions. This is done using techniques for evaluating similarities between the active user and its neighbors. There is a plethora of techniques available [5, 15, 18, 19] but the one most used in researches of collaborative recommender systems is the Pearson Correlation [3, 4, 5, 18]. The similarity between users is defined by the Pearson Coefficient [3, 4, 5, 18].
3.1 Recommender systems on television With the increase of channels and content available, searching for relevant content can be a very difficult task. To help in this task, employment of recommender systems has been used as a solution [2, 6, 10, 7, 8, 11, 17, 20]. Researches on recommender systems in television have been mostly centered on helping viewers find the best programs available among the diversity of channels. There are researches that propose solutions related directly to their television set [10, 11, 20], set-top boxes [2, 6, 10, 7, 20], through IPTV channels [9, 8] or even as guides on the web [8, 13]. All these solutions propose recommendations based on multiple channels and personalized entertainment experiences for the viewer. Little has been researched on how to apply these recommender systems for broadcast channels to generate their schedules of tv programming [17]. In other words: a single recommendation for multiple viewers at the same time. For making recommendations, researches [2, 7, 8, 11, 17] uses collaborative filtering, researches [6, 20] uses contentbased techniques and researchs [10, 13] a hybrid approach for achieving the expected result. The challenge for broadcasting companies is to know which program should be broadcasted as the same program is aired to all viewers at any given time. Using recommendation systems on this domain means a recommendation system can’t use the preferences of a single viewer as basis for recommendations, but instead has to use all viewers’ preferences. The system has to suggest to the producer or director the content that best fits the preference of most viewers.
4. SYSTEM OVERVIEW
Figure 1: System Overview
Given the problem that television channels always have only a single broadcasted schedule, and that this schedule should have the best content concerning viewer preference, we propose a non-supervisioned approach for generating a better schedule based on viewers’ feedback and content metadata. The proposed system is divided into the following parts: schedule manager, recommender module and rating module. It’s through the schedule manager that the user will request, edit and manage recommended schedules. Also, in the schedule manager the user can add/remove/edit the library of content and edit the maximum number of times a content can reprise in a time period. The recommender module is responsible for generating recommendations when requested by the schedule manager. This module will be explained more throughly in the remaining of the section. The rating module is comprised of two parts: a GINGA1 application for retriving viewers’ ratings and a webservice for receiving the viewers’ ratings and directing it to the recommender module. Figure 1 shows these interactions between the modules.
4.1 Recommender module The recommender module helps the program director choose which content should be aired through recommendations based on the viewers’ ratings (preferences). Our research proposes a system based on time intervals as a solution for abstracting the viewers’ preferences. These time intervals will be used as user profiles to the recommender system, containing the parental guidance ratings, eg.: pg-13, and preferred genres, eg.: drama, as attributes. As a channel’s schedules are the same for all viewers, taking a single viewer’s preferences as base for recommendation is irrelevant, what is needed are all viewers preferences, at once. For a single time-interval model (TI) there is only a single rating stored for an item: the mean rating by the viewers that rated on TI’s specific time. Different times have 1
GINGA is the middleware used for interactive application on the SBTVD standard (Brazilian Digital Television System)
Manuscript - WEBMEDIA 2014 – XX Simpósio Brasileiro de Sistemas Multimídia e Web different target audience, therefore a TI can be considered a user profile. This model abstracts the viewers’ preferences so the system predicts what the viewers enjoys at each time of the day. Each viewer’s rating has a timestamp and rating value. When a new rating is received, the correct TI resolves its rating using a simple mean calculation between the previous values and the new one, resulting in a new rating between the TI and the content. Our proposed system has two functions for recommending content for the producer/director. The first function uses a collaborative filtering technique (CF) for predicting ratings for content never used by the active TI and the second funcion uses a weighted selection for selecting content seen in the active TI. This approach prioritizes airing content never aired before in the active TI (recommending new content) and when none exist, it prioritizes contents not aired for a long time (recommending rated content).
Recommending new content. The recommendation of new content starts by retrieving all content that meets the active TI (uα ) criteria, such as its parental guidance ratings, and that haven’t yet been rated. The set Kuα , containing the k-top neighbors from Nuα (equation 2), is used over the set Iuα (equation 1), containing all valid items, for generating new predictions. Iuα
For testing purposes we created a small content pool of 90 movies. These 90 movies were taken from IMDB’s2 top 250 movies and the metadata contained on each profile used as the content’s data. As each movie was included a rating was generated randomly for it, this was made for simulating past usage. The total time length in minutes of this pool was 12030 minutes, or 200.5 hours, or still 8.35 days. With this pool worth a little more than a week of content, we run the process of generating a week schedule 8 times. The process consisted of running the recommender module for recommendations and inserting the recommendation onto the schedule. As a week worth of recommendation were done, randomly each content were rated automatically and the process re-run for a new week. This simulation was equivalent of generating 2 months worth of schedules. At the end of the simulation a total of 569 movies were ”broadcasted”, were 479 of the movies were reprises and these reprises consisted of 80% of movies rated 4+ stars. The most reprised content were reprised 41 times and had a rating of 5 stars. Also the user is able to set a value limiting the number of reprises allowed, but this option should be used with caution as setting limits on a small library could result on content not being available to be aired.
As of now, tests are being developed on the public broadcast company [removed for double-blind review]3 . The company’s content pool is being ported to prototype database. = {i ⊆ I , ruα ,i ∈ / Ruα ∧parentalRatinguα ≥ parentalRatingi }T . he next step is generate schedules for broadcast and open the interactive application for the viewers to rate the brod(1) casted contents. Nuα = {uj ⊆ U, i ⊆ Iuα |sim(uα , uj ) > 0}. (2)
With set Kuα defined, the function uses each of the neighbors’ sim(uα , u) as weight and using the Weighted Sum of Others’ Ratings [18] retrieves predictions for user uα . The system then recommends randomly one of the items from Iuα using the predicted rating as weight.
Recommending rated content. As small channels might not have a large content pool available, the second function is used when new content is not available. This function uses weight for randomly choosing content already rated to be recommended, while keeping the best rated content in the highest possibility range. But as all content available has been paid for or had resources spent on producing it, we can’t ignore it, even if they were badly rated. So those items that haven’t been aired in a long time have an increase on their chance to be chosen. This weight is resolved for each content already rated by the active TI using equation 3. Where uα is the active TI and i the current content being evaluated. wuα ,j is the resulting weight, ruα ,j is the rating given by uα to i. ti is the number of days since the current content last aired and T is the total length of all content available in the system. T is updated each time a new content is added to the database. wuα ,i = r uα ,i ∗
ti T
(3)
5. CONCLUSIONS The process of generating TV broadcasting channels’ schedule is a tough task because there are different kinds of people watching the same content at the same time. The program director, responsible for scheduling, must know the available channel’s content pool and set the schedule based on his experience with media, as well as his knowledge of the target audience. It becomes even more complicated when content needs to be rebroadcasted, for example, in movie sessions or in a scenario where a small channel might not have a large content pool available. This research presented a novel take on collaborative filtering techniques on recommender systems applied on a television channel broadcasting company. Unlike others researches which apply recommender systems on the viewer side helping the viewer choose content from a set of television channels, our work applies this technology on the television channel scheduler creation process. Applying our proposed system in the scheduler creation process at a television channel, the program director has an automatic schedule built using recommendations based on the viewers’ preferences explicitly acquired using a rating system based on an application for interactive television. Using the proposed system in the content curation process means the director has a tool available that can automatically generate 2
4.2 Initial test results
3
Internet Movie Database (IMDb): http://www.imdb.com [removed for double-blind review]
Manuscript - WEBMEDIA 2014 – XX Simpósio Brasileiro de Sistemas Multimídia e Web the schedule in a way which prioritizes well-rated and less frequently viewed programming. This work showed that applying recommender systems on a broadcasting company context can help television stations to better manage their content pool and schedule creation according to the audience’s preferences. Initial experiments are being made along with removed for double-blind review, an university television channel in removed for double-blind review.
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