A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment

Bambini, Riccardo; Cremonesi, Paolo; Turrin, Roberto

doi:10.1007/978-0-387-85820-3_9

Cited by 58 publications

(51 citation statements)

References 29 publications

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“…Bambini et al [2] described the integration of a recommendation system into FastWeb, a large IP Television (IPTV) provider. The recommendation system implemented both collaborative and content-based techniques, in order to recommend programs and videos on demand.…”

Section: Related Workmentioning

confidence: 99%

“…The data collection of TV programs and users is performed by the Data Collector (Figure 2), which is a common element of recommendation architectures [2]. In the proposed architecture, program information is extracted from the EPG, while user information is collected implicitly by the analysis of their TV viewing history.…”

Section: A Data Collection and Managementmentioning

confidence: 99%

“…Although some previous works focus on recommendation architectures applied to DTV / Smart TV [2], [3], [7], they normally deal with the recommendation of specific items (i.e., TV programs). However, many recommendation approaches can be applied with different features and goals, such as recommendation of terms, tags, etc.…”

Section: Introductionmentioning

confidence: 99%

“…In the recommendation of tags, for example, the model based on user feedback is well known [16], where the user assigns tags to several items, but this model is not suitable for the domain of DTV, since the action of assigning tags and giving explicit feedback in DTV is restricted by user experience requirements, which demand the use of the remote control as less as possible [2], because watching TV and typing with it at the same time is a time consuming and difficult task. So, ways of acquiring implicit information in DTV need to be investigated, such as the extraction of terms from program descriptions present on the EPG.…”

Section: Introductionmentioning

confidence: 99%

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Recommendation in the Digital TV Domain: an Architecture based on Textual Description Analysis

Ramos

Costa

Soares

et al. 2015

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Recommendation systems have been used in several application domains, most recently for TV (Digital TV, Smart TV, etc.). Several approaches can be used to recommend items, tags, etc., mainly based on user feedback. However, in the Digital TV domain, user feedback has to be done generally by using the remote control, which should be avoided to improve user experience, since assigning explicit feedback to items is restricted by the characteristics of this domain (difficulties when typing with the remote control, etc.). Moreover, in the Smart TV environment several types of items can be recommended (movies, musics, books, etc.). Thus, the recommendation should be generic enough to suit to different content. To solve the problem of acquiring explicit feedback and still generate personalized recommendations to be used by different Smart TV applications, this work proposes a recommendation architecture based on the extraction and classification of terms by analyzing the textual descriptions of TV programs present on electronic programming guides. In order to validate the proposed solution, a prototype using a real dataset has been developed, showing that using the recommended terms it is possible to generate final recommendations for different Smart TV applications.

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Section: Related Workmentioning

confidence: 99%

Section: A Data Collection and Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recommendation in the Digital TV Domain: an Architecture based on Textual Description Analysis

Ramos

Costa

Soares

et al. 2015

International Conferences on Software Engineering and Knowledge Engineering

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“…Content analysis takes into account the 481 features (e.g., category, price-range, facilities), the free text of the hotel description, and the free text of the hotel reviews. DirectContent is a simplified version of the LSA algorithm described in [1]. ─ Interleave is a hybrid algorithm that generates a list of recommended hotels alternating the results from PureSVD and DirectContent.…”

Section: The Design Of the Studiesmentioning

confidence: 99%

User-Centric vs. System-Centric Evaluation of Recommender Systems

Cremonesi

Garzotto

Turrin³

2013

Lecture Notes in Computer Science

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Abstract. Recommender Systems (RSs) aim at helping users search large amounts of contents and identify more effectively the items (products or services) that are likely to be more useful or attractive. The quality of a RS can be defined from two perspectives: system-centric, in which quality measures (e.g., precision, recall) are evaluated using vast datasets of preferences and opinions on items previously collected from users that are not interacting with the RS under study; user-centric, in which user measures are collected from users interacting with the RS under study. Prior research in e-commerce has provided some empirical evidence that system-centric and user-centric quality methods may lead to inconsistent results, e.g., RSs that were "best" according to systemcentric measures were not the top ones according to user-centric measures. The paper investigates if a similar mismatch also exists in the domain of e-tourism. We discuss two studies that have adopted a system-centric approach using data from 210000 users, and a user-centric approach involving 240 users interacting with an online hotel booking service. In both studies, we considered four RSs that employ an implicit user preference elicitation technique and different baseline and state-of-the-art recommendation algorithms. In these four experimental conditions, we compared system-centric quality measures against user-centric evaluation results. System-centric quality measures were consistent with usercentric measures, in contrast with past studies in e-commerce. This pinpoints that the relationship between the two kinds of metrics may depend on the business sector, is more complex that we may expect, and is a challenging issues that deserves further research.

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Video content recommendation: An overview and discussion on technologies and business models

Vriendt¹,

Degrande²,

Verhoeyen³

2011

Bell Labs Tech. J.

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This paper presents several aspects, from technologies to business models, of content recommendation for video related solutions that range from IP television (IPTV), including linear programming television (LPTV) and videoon-demand (VoD), to online video. Video content recommendation is becoming increasingly important because of the continuously increasing amount of video content available to end users. After considering some end user requirements, an analysis is provided of the most important content recommendation technologies as described in literature and implemented by many start-ups. The paper also deals with evaluation criteria for a content recommender system related to user expectations, support of different scenarios (e.g., new content, new users), and marketing and business requirements. We describe the overall architecture into which the content recommendation functionality fits, as well as its interfaces with other network components, external databases, social networks, and applications.Finally, we discuss a dedicated network provider play, the business opportunities, and several business models for content recommendation. © 2011 Alcatel-Lucent. very exciting, the overwhelming choice in content available on broadcast TV, VoD, and the Internet is also leading to much irritation and frustration of the end user, since it is becoming increasingly hard to pick out that one movie, program, or video clip best suited to his mood, environment, and interest at that moment. It demands quite some patience, perseverance, and time from the end user to sift the programming guide, click through all channels, or browse the complete VoD catalog in order to find something of interest to watch. Because of this, people tend to stick IntroductionThe expansion of the Internet and the advent of digital broadcasting have led to an ever-increasing range of content at the fingertips of the end user. Video-on-demand (VoD) libraries are approaching 20,000 titles per aggregator with substantial further growth expected; the number of online aggregators is increasing continuously; millions of Internet video clips are accessible on television (TV); and the number of linear programming television (LPTV) channels is increasing constantly, along with content on catchup TV (recordings of LPTV). Although this sounds to what they are used to watching, and the promise of having everything you like at your fingertips is still only a promise.This dilemma not only short-circuits the end user experience, but sets the stage for dissatisfaction and the risk that an end user will turn his back on the service as well as the content provider. An NDS Group survey of more than 1,000 cable customers in the United States (U.S.) found that LPTV program recommendations are one of the top applications they desire. Keeping in mind that millions of people in the U.S. are spending 150 hours per month in front of their TV sets [19], improving the end user experience with content discovery can lead to a win-win-win situation for the end user, service prov...

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A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment

Cited by 58 publications

References 29 publications

Recommendation in the Digital TV Domain: an Architecture based on Textual Description Analysis

Recommendation in the Digital TV Domain: an Architecture based on Textual Description Analysis

User-Centric vs. System-Centric Evaluation of Recommender Systems

Video content recommendation: An overview and discussion on technologies and business models

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