D5.1 Mass Market App - Evaluation

ViSTA-TV: Video Stream Analytics for Viewers in the TV Industry

FP7 STREP ICT-296126 | 296126 co-funded by the European Commission ICT-2011-SME-DCL | SME Initiative on Digital Content and Languages

Marco Cadetg (Zattoo), Stefan Lietsch (Zattoo),

Project start date: June 1st, 2012 Project duration: 24 months Document identiﬁer: ViSTA-TV/2013/D5.1 Version: v1.1 Date due: 31 May 2013 Status: Final Submission date: 25 May 2013 Distribution: CO|RE|PU

www.vista-tv.eu ViSTA-TV Consortium

This document is part of a collaborative research project funded by the FP7 ICT Programme of the Commission of the European Communities, grant number 296126. The following partners are involved in the project:

University of Zurich (UZH) - Coordinator Dynamic and Distributed Information Systems Group (DDIS) Binzmühlstrasse 14 8050 Zürich, Switzerland Contact person: Abraham Bernstein E-mail: bernstein@iﬁ.uzh.ch

Techniche Universität Dortmund (TUDo) Computer Science VIII: Artiﬁcial Intelligence Unit D-44221 Dortmund, Germany Contact person: Katharina Morik E-mail: [email protected]

Rapid-I GmbH (RAPID-I) Stockumer Strasse 475 44227 Dortmund, Germany Contact person: Ingo Mierswa E-mail: [email protected]

Zattoo Europa AG (Zattoo) Eggbühlstrasse 28 CH-8050 Zürich, Switzerland Contact person: Bea Knecht E-mail: [email protected]

Vrije Universiteit Amsterdam (VUA) Web & Media Group, Department of Computer Science, Faculty of Sciences (FEW) De Boelelaan 1081a NL-1081 HV Amsterdam, The Netherlands Contact person: Guus Schreiber E-mail: [email protected]

The British Broadcasting Corporation (BBC) 56 Wood Lane / Centre House London W12 7SB, United Kingdom Contact person: Chris Newell E-mail: Chris.Newell@bbc.co.uk

Executive Overview

In order to get additional user input outside of the user logs and demographical data that is already considered within the VISTA-TV project, this delivery describes a mass market app / frontend that let’s users actively rate content on Zattoo’s platform. This frontend and the data gathered from the inputs will be used to enhance the data that is collected auto- matically and will serve as an interface for the Recommendation engine which will be developed in a later phase of the project. 4 ViSTA-TV

Contents

1 Introduction 5 1.1 Requirements ...... 5

2 The Zattoo Recommendation Frontend 5 2.1 Market research and key factors ...... 5 2.2 Design and implementation of the frontend ...... 6 2.3 User proﬁles and data ...... 7

3 Viewership Dashboard 7

4 Future work 7 D5.1 Mass Market App - Evaluation 5

1 Introduction

This deliverable covers the ﬁrst item of Task 5.1 Mass-Market App [Zattoo (lead), Rapid-I; D5.1-5.5; M7-M18]

Specify and develop a simple mass-market App.

2 persuade Zattoo’s users to provide data about what they are watching and whether they are enjoying it. The incentive will be beneﬁts such as an audible warning for ad-break timings or interesting information about the show. 2 The App is speciﬁcally designed to provide additional input to the recommendation tasks.

Perform research and document market requirements for the Mass-market App.

2 high-level description of the App, target customers, useful features, competitors.

Build and deploy an enhanced version of the App.

2 based on the ﬁndings of the market research.

Over the course of the project the project participants decided to slightly shift the focus of that task. Zattoo and partners worked on a deeper integration of the Mass market app into Zattoo’s existing clients (see section 2). In addition to that Zattoo has been working on an app for B2B customers which will be part of the Review Process demonstration and be on display at The IBC conference (see Section 3)

1.1 Requirements

Our task in this WP was to come up with an app for the end user that would allow us to get their explicit input on the content they have been watching. Instead of building a separate app that would live outside of Zattoo’s ecosystem as initially planned, we decided (in coordination with the project lead), to include this feature into our existing web product in terms of a Recommendation UI. This had two main advantages:

1. our users have a great incentive to give the desired information. We will give relevant content back to them. Thereby we expect much more valuable information that we can give back to the project 2. these mechanisms can be reused for later project phases (see WP4 Recommendations) when we will incorporate the results of the projects recommendation engine into our products

2 The Zattoo Recommendation Frontend

This section describes the steps taken to design, prototype and implement the Recommendation fronted, which serves as the mass market app for the VISTA-TV project going forward.

2.1 Market research and key factors

Intensive market research and cooperation with a long standing business partner led us to ﬁve key factors that we saw important for a successful design of the Recommendation UI:

1. we need to provide self explanatory and well understood user interaction patterns. The most common pattern for rating content is a simple like/dislike control or a 5-star rating. We chose to implement a combination of both in the prototype. 2. we need to make sure that the user, even before doing a recommendation, sees relevant content in order to get them to provide feedback on this content. We choose to show a selection of editorial recommendations and categorized recommendations of programs that are relevant in terms of time and availability for the user. 6 ViSTA-TV

Figure 1: Zattoo’s recommendation frontend with labels

3. Since recommendation algorithms and their implementation is not the core focus of Zattoo, and the WP4 was not expected to have a system running at that stage of the project, we needed to find a drop in solution to provide the recommendations and store the user profiles/input. We decided to go for a commercially available system called watchmi. 4. We need to give the user some guidance to understand what benefits one gets after rating shows. We need to explain what the recommendations we give are based on 5. The frontend needs to be adapted to the capabilities of the users device. E.g. on a phone recommendations need to be spot on since there is limited space, where as on a computer or tablet we can give the user a wider selection to choose from. For the prototype we picked our web client as the platform of choice since it gave us biggest flexibility in terms of features and development time.

2.2 Design and implementation of the frontend

Figure 1 shows the visual design of the recommendation frontend. As it is part of Zattoo’s frontend for Internet TV it shares certain controls and display areas with features that are not in the scope of this deliverable.

Recommendations area

The first row in Zattoo’s Categories overview is the so called Recommendations section. It holds the most relevant content for each specific user and is currently based on the output of the underlying recommendation engine. Before the user has made personal ratings, he/she gets editorial recommendations. This section will be fed from the Recommendation Engine developed in the scope of this project later on. As soon as a user did his/her first rating of a show and hits the refresh button, the recommendation section will be adapted to the users personal preference. With every new rating the recommendations will be refined and the section will be updated.

Rating area

Hovering over any show in Zattoo’s categories view will bring up a show detail view which contains the rating area. In this area there are buttons to like or dislike a show and if the show is part of the recommendations D5.1 Mass Market App - Evaluation 7

Figure 2: Zattoo’s recommendation details view section it also contains a 5-star indicator to surface the level of recommendation for this show. The example in Figure 1 displays 5 stars for the highlighted show, which means that the show is highly recommended for the current user.

Recommendation categories

By clicking on the Recommendations heading in the Recommendations section a user gets directed to another view (see Figure 2). This view contains more recommendations which are grouped by recommendation categories. If a user does ratings within one section this additional information is stored and passed on to the recommendations engine, too.

2.3 User proﬁles and data

The input that is collected from the user ratings is stored and used to build up a profile for each and every user. These profiles currently live in the system of the recommendation engine provider. However as soon as the recommendation system is made available by the VISTA-TV partners the data flows into this system and can be used in the scope of this project.

3 Viewership Dashboard

To better visualize viewership data Zattoo has also been working on a small application that holds live viewership graphs (see Figure 3). It bases on the first prototypes of the online data delivery and processing and will be used to demonstrate a view of the projects findings. So far it contains a live view of the actual share of the live TV channels on the specified platform (BBC or Zattoo). going forward more features such as detail views per channel and a viewership history will be integrated.

4 Future work

As soon as WP4’s recommendation engine (or at least a prototypical version of it) is ready, we will start it’s integration with the Recommendation frontend. 8 ViSTA-TV

Figure 3: Viewership Dashboard App

This will happen in tight cooperation with the relevant partners such as Rapid-I and TuDo. This includes deﬁning the interface between Zattoo’s frontend and the backend and adapting the frontend to additional features (e.g. recommendations for series, item-to-item recommendations, etc.).