EP2169854A1 - Procédé d'apprentissage de profil utilisateur et moteur de profilage associé pour plateformes combinées de fourniture de services - Google Patents

Procédé d'apprentissage de profil utilisateur et moteur de profilage associé pour plateformes combinées de fourniture de services Download PDF

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Publication number
EP2169854A1
EP2169854A1 EP08305608A EP08305608A EP2169854A1 EP 2169854 A1 EP2169854 A1 EP 2169854A1 EP 08305608 A EP08305608 A EP 08305608A EP 08305608 A EP08305608 A EP 08305608A EP 2169854 A1 EP2169854 A1 EP 2169854A1
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Prior art keywords
interest
user
consumption
qoi
profiling
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EP08305608A
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German (de)
English (en)
Inventor
Armen Aghasaryan
Stéphane Betge-Brezetz
Christophe Senot
Yann Toms
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Alcatel Lucent SAS
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Alcatel Lucent SAS
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Priority to EP08305608A priority Critical patent/EP2169854A1/fr
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Withdrawn legal-status Critical Current

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04H—BROADCAST COMMUNICATION
    • H04H60/00—Arrangements for broadcast applications with a direct linking to broadcast information or broadcast space-time; Broadcast-related systems
    • H04H60/35—Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users
    • H04H60/46—Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for recognising users' preferences

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  • the present invention is related to a user profile learning method and an associated profiling engine.
  • telcos and service providers hold large amounts of data on end user service consumption and they are well positioned to accurately infer user preferences, interest domains, and behaviors.
  • the provider cannot leverage this information and deliver value-added services. Accordingly, it is a general object of the present invention to provide a profiling engine that automatically learns the profile of each telecommunication (or telco) customer.
  • a generic profiling engine that:
  • the object of the present invention is a user profile learning method comprising learning and updating the profile data defined in a user model, using two mechanisms which are an explicit profiling process and an implicit profiling process, in which, in the explicit profiling process, the user declares his topics of interest, e.g. via a Web portal, given that, at the initialization stage, the absence of active indication of interest in a topic implies a lack of user interest, and the implicit profiling process comprising learning and updating the profile data from all kinds of usage traces through observation and tracking of actual service consumption by the user such as the user's context or his presence status when consuming the services.
  • the implicit profiling process is based on the analysis of heterogeneous usage traces in a mobile environment.
  • three measurable quantities are used to describe contents, consumption events, and user profiles, the three measurable quantities being :
  • said periodicity is significantly larger than the average time interval between consumption events.
  • this time-based decay function in order to decide how to decrease a given Quantity of Interest, this time-based decay function take into account parameters P k i like frequency or recency of consumption events on each considered semantic concept.
  • the object of the present invention also concerns a profiling engine for Converged Service Delivery Platforms, comprising learning and updating means for learning and updating the profile data defined in a user model, using two mechanisms coming from explicit profiling means and implicit profiling means, wherein the explicit profiling means has inputs for receiving users' declarations about their topics of interest, e.g.
  • the implicit profiling means has inputs for receiving users profile data from all kinds of usage traces through observation and tracking of actual service consumption by the user, the learning/updating means receiving for learning/updating the profile data as a function of users' profile data provided by the explicit profiling means and the implicit profiling means.
  • the profiling engine offers intelligent query interfaces providing applications with generic access to all profile data thus allowing personalization of various applications.
  • these query interfaces include different distances to compute the similarity between a content and a unique user, e.g. to recommend specific content, or to compute the similarity between users, e.g. to define communities of interest.
  • the profile engine comprises a homogeneous raw data generation module to transform all platform-specific data into a common, pre-defined format processable by the profile engine.
  • the main objective of the profiling engine 1 is to ensure the automatic learning of each user's profile to develop an accurate estimation of his interest domains, service/content consumption habits, and purchasing behavior. For this purpose, large operators have the opportunity to collect and consolidate service usage and content consumption traces over their service delivery platforms 2. In addition to Web-based usage traces, they can largely exploit television 3 and mobile video 4 consumption data as well as the usage of IP communication services.
  • the profiling engine 1 aggregates the usage traces coming from all these different platforms 2 and builds an end user profile based on a common model.
  • this consolidated profile can support personalization for numerous applications 5 : content recommendation, personalized content search 6, or social networking applications 7.
  • the engine can be put to immediate use for targeted ads 8 -a new revenue stream the service providers intend to tap into-in compensation for declining voice revenues.
  • Figure 1 illustrates this multi-platform and multi-application paradigm. Having such a profiling engine is critical for service providers 1) to derive benefit from all these sources of information, and 2) by procurzing the so- obtained profile to offer the end user a unified and consistent experience across personalized services.
  • the user profile is obtained by aggregating the usage traces available on different SDPs and by combining them with content descriptions (metadata) available in content management systems (CMS).
  • CMS content management systems
  • the principle of the profiling engine according to an embodiment is depicted on Figure 2 .
  • a user consumes a service, for example, views video-on-demand (VoD) on a mobile phone 9, some consumption traces are generated on the SDP or on the terminal.
  • VoD video-on-demand
  • all these traces are injected into the profiling engine which dynamically updates the user profile in order to closely follow its real-life evolution.
  • the profiling engine relies on a user model 10 which is shaped according to the structure and the semantics of the content metadata available in the CMS 11. Nevertheless, the profiling engine 1 and its underlying update algorithms are not designed specifically for a given user profile and content metadata structure; they can be automatically applicable to a new structure and semantics.
  • the profiling engine 1 uses profiling algorithms running module 11 to learn and update the profile data defined in the user model 10. It is distinguished two mechanisms: explicit profiling and implicit profiling.
  • explicit profiling the user declares his topics of interest, e.g., via a Web portal.
  • the implicit profiling comprising learning and updating the profile from all the usage traces. It relies on content metadata describing the semantics of consumed contents and services.
  • An intelligent query interface 12 is another important component of the profiling engine 1. It provides the personalized applications with generic access to all profile data comprised in user profile database 17.
  • DB database
  • the profiling engine addresses privacy issues, via a privacy protection module 13, in order to conform with legal requirements as well as to ensure the user's consent for being profiled.
  • Our approach relies on privacy policies which allow, on the one hand, controls around access to personal profile data, and on the other hand, mechanisms to configure the level of intrusiveness of the profiling method itself.
  • This section details the technology realized in the profiling engine according to one embodiment. It covers the data model 10 (to describe the semantic concepts 14 and the measurable quantities 15), the profiling method (to incrementally build the profile), the profile data access (to personalize a wide range of applications), and finally provides an insight on privacy which is an important element of such profiling technology.
  • Semantic concepts constitute a core element of the profiling engine's data model. They are used to represent the semantic characteristics of user profiles and contents. For example, the "action" film genre is a semantic concept, it can characterize a given content, and at the same time, a given user's interest in this kind of content in general. Of course, both content and user profile are generally characterized by several semantic concepts. Semantic concepts usually have relationships between each other, expressing notions such as composition, inheritance, or dependency.
  • semantic concepts "action,” “adventure,” and “comedy” which are usually used to describe a movie, are related to the respective semantic concept “movie” by an inheritance relation, or a so-called “is-a” relation.
  • Semantic concepts can therefore be organized in a structure reflecting their relationships, e.g., a taxonomy or an ontology. In some cases, they can simply form a vocabulary of flat keywords without any structure.
  • Figure 3 illustrates how the same principle can be used to characterize the contents and the consumption events.
  • the diagram uses the following three measurable quantities which allow to describe the contents, the consumption events, and the user profiles, respectively:
  • the global user profiling process is described in Figure 4 .
  • SDP raw data platform-specific usage traces 18
  • CDRs call data records
  • cookies contains the description of consumption data 19 like watched/ranked videos, time watched, price paid, accepted offers, or search strings.
  • this platform-specific data must first be transformed, thanks to a HRD generation module 20, into a common format called homogeneous raw data (HRD). It is called homogenization of raw data the process of transforming all the heterogeneous gathered data into a pre-defined format, comprehensible by the profiling engine.
  • HRD homogeneous raw data
  • each QoC (Quantity of Consumption) value provides a normalized measure, QoC ⁇ [0,1], of the observed user interest for a given semantic concept. This measure is based on the following assumptions:
  • the QoC values are computed by modulating the QoA (Quantity of Affiliation) values of the consumed content by the numerical parameters held in the HRD.
  • the modulation function may be domain-dependent. Indeed, each SDP offers a specific service and the usage of different services cannot be put on the same scale.
  • the normalization function can also vary from one application to another.
  • This stage describes how the user profile is incrementally updated.
  • two complementary update functions co-exist: 1) consumption event-based QoI learning, and 2) time-based QoI decay.
  • the consumption event-based QoI learning function refreshes the QoI data on user interest by combining their previously known values with a newly-observed interest manifestation (defined by the QoC).
  • the weight W QoI n i is a function of the current QoI; it defines how much the new observation is influencing the profile evolution.
  • Such a variable weight allows us to obtain a "learning curve" behavior where secondary interests build relatively slowly (because of a lower weighted ranking) and primary interests are saturated by the upper limit of one (again, because of a diminishing variable weight).
  • learning curve makes reference to a relationship between the duration of a student's learning period and the knowledge or experience gained.
  • the QoI evolution for a stable consumption pattern represents a sigmoid form, as shown in Figure 7 .
  • This function is called with a given periodicity indexed by k .
  • This periodicity should be significantly larger than the average time interval between consumption events, for example it can be on a monthly or quarterly basis.
  • this function can take into account parameters P k i like frequency or recency of consumption events on that semantic concept. For example, depending on the consumption frequency, the QoI can be diminished linearly, exponentially, or without decay for a fixed period of non-consumption followed by some decay curve.
  • the raw data contains some information on the context in which the consumption event occurred, it can be used to obtain a context-aware profile.
  • the term of context can be applied in its broadest sense including geographical localization and presence (at home, at work, or on vacation), social (with friends, with family, or alone), or temporal (morning or evening) interpretations.
  • the intelligent profile query interface 12 enables personalized applications by providing Web service access to user profiles, and by providing some profile exploitation tools. In order to deal with many different applications such as targeted ads, content recommendation, or community-based applications, this interface should provide generic reusable features. In fact, three main types of requests are possible:
  • This query interface is critical as it will ensure the independence of the profiling module with respect to different personalized applications. Indeed, it is decoupled the similarity measuring from the personalization techniques deployed in the application logic. So, it is enabled both well known types of personalization techniques: the content-based (CB) approach and the collaborative filtering (CF) approach.
  • CB content-based
  • CF collaborative filtering
  • the privacy of the end user is an important element to take into account when designing a profiling system.
  • the service provider must ensure the compliance with the legal privacy rules in each country where the solution is deployed.
  • user acceptance is a major issue, as user profiling can easily be perceived by end users as a threat and an intrusion into their private life.
  • the user should be provided with a comprehensive interface for setting his privacy options.
  • the present approach encompasses two aspects of user privacy protection: access control of personal profile data and configuration of the intrusiveness level in the profiling process. This is achieved by using high-level privacy policy rules. Some of these rules are introduced by the service provider in order to define its global profiling and personalization policy in conformance with the existing legislation. For example, among the requirements put forward by the European Union, there are three core principles:
  • the second aspect of the privacy policy is introduced by each user in order to tune his personal privacy preferences.
  • a user can specify the types of services (IPTV, mobile video, or Web browsing) and traces (watching, interactivity, or zapping) that can be used for his profiling.
  • IPTV IPTV
  • traces watches, interactivity, or zapping
  • a user can define variable restrictions depending on the type of personalized application. For example, if a user does not want to receive targeted ads based on his video interests (action, sport, or adult), this part of the profile must be hidden to the targeted ad selector. The restrictions can also impact the granularity of the information made available in a given domain.
  • an explicit profiling feature the user's read/write access to his own profile data. It not only allows a user to initialize the system, but also to update and rectify the learned profile. Once updated, the profiling process continues with the new current profile modified explicitly by the user in the same way as in the initialization phase.
  • a prototype of this process has been implemented in the scope of a video-on-demand service for converging fixed (IPTV) and mobile (mobile video) content delivery platforms where the customers can use a diversity of terminals: TV/set-top box, mobile phone, and laptop.
  • the profiling has been done with two key personalized applications, namely targeted advertisement and content recommender.
  • the implementation combines ontology Web language (OWL) for the description of the user/metadata model and Web services technology, notably for intelligent query interface.
  • OWL ontology Web language
  • the use of open technologies allows rapid integration of new evolutions of the profile model as well as support for a wide range of personalized applications.
  • Figure 7 illustrates a QoI evolution in a user profile showing stable consumption of content with a given semantic concept, and an average of three weekly consumption events over approximately 140 days.
  • the decay function is calculated on the basis of the number of consumptions in a monthly time window (frequency). The higher the frequency, the lower the decay applied to QoI.
EP08305608A 2008-09-29 2008-09-29 Procédé d'apprentissage de profil utilisateur et moteur de profilage associé pour plateformes combinées de fourniture de services Withdrawn EP2169854A1 (fr)

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US10334324B2 (en) 2008-11-26 2019-06-25 Free Stream Media Corp. Relevant advertisement generation based on a user operating a client device communicatively coupled with a networked media device
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US9866925B2 (en) 2008-11-26 2018-01-09 Free Stream Media Corp. Relevancy improvement through targeting of information based on data gathered from a networked device associated with a security sandbox of a client device
US9961388B2 (en) 2008-11-26 2018-05-01 David Harrison Exposure of public internet protocol addresses in an advertising exchange server to improve relevancy of advertisements
US10986141B2 (en) 2008-11-26 2021-04-20 Free Stream Media Corp. Relevancy improvement through targeting of information based on data gathered from a networked device associated with a security sandbox of a client device
US10977693B2 (en) 2008-11-26 2021-04-13 Free Stream Media Corp. Association of content identifier of audio-visual data with additional data through capture infrastructure
US10880340B2 (en) 2008-11-26 2020-12-29 Free Stream Media Corp. Relevancy improvement through targeting of information based on data gathered from a networked device associated with a security sandbox of a client device
US9560425B2 (en) 2008-11-26 2017-01-31 Free Stream Media Corp. Remotely control devices over a network without authentication or registration
US9591381B2 (en) 2008-11-26 2017-03-07 Free Stream Media Corp. Automated discovery and launch of an application on a network enabled device
US9686596B2 (en) 2008-11-26 2017-06-20 Free Stream Media Corp. Advertisement targeting through embedded scripts in supply-side and demand-side platforms
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US9703947B2 (en) 2008-11-26 2017-07-11 Free Stream Media Corp. Relevancy improvement through targeting of information based on data gathered from a networked device associated with a security sandbox of a client device
US9716736B2 (en) 2008-11-26 2017-07-25 Free Stream Media Corp. System and method of discovery and launch associated with a networked media device
US9838758B2 (en) 2008-11-26 2017-12-05 David Harrison Relevancy improvement through targeting of information based on data gathered from a networked device associated with a security sandbox of a client device
US9848250B2 (en) 2008-11-26 2017-12-19 Free Stream Media Corp. Relevancy improvement through targeting of information based on data gathered from a networked device associated with a security sandbox of a client device
US9854330B2 (en) 2008-11-26 2017-12-26 David Harrison Relevancy improvement through targeting of information based on data gathered from a networked device associated with a security sandbox of a client device
US10791152B2 (en) 2008-11-26 2020-09-29 Free Stream Media Corp. Automatic communications between networked devices such as televisions and mobile devices
US10771525B2 (en) 2008-11-26 2020-09-08 Free Stream Media Corp. System and method of discovery and launch associated with a networked media device
US10631068B2 (en) 2008-11-26 2020-04-21 Free Stream Media Corp. Content exposure attribution based on renderings of related content across multiple devices
US9986279B2 (en) 2008-11-26 2018-05-29 Free Stream Media Corp. Discovery, access control, and communication with networked services
US10032191B2 (en) 2008-11-26 2018-07-24 Free Stream Media Corp. Advertisement targeting through embedded scripts in supply-side and demand-side platforms
US10567823B2 (en) 2008-11-26 2020-02-18 Free Stream Media Corp. Relevant advertisement generation based on a user operating a client device communicatively coupled with a networked media device
US10074108B2 (en) 2008-11-26 2018-09-11 Free Stream Media Corp. Annotation of metadata through capture infrastructure
US10425675B2 (en) 2008-11-26 2019-09-24 Free Stream Media Corp. Discovery, access control, and communication with networked services
US9967295B2 (en) 2008-11-26 2018-05-08 David Harrison Automated discovery and launch of an application on a network enabled device
FR2960323A1 (fr) * 2010-05-19 2011-11-25 Kindsight Inc Dispositif et procede d'apprentissage automatique de profilage
JP2014516447A (ja) * 2011-04-28 2014-07-10 フェイスブック，インク． ソーシャル・ネットワーキング・システムにおける認知的関連性ターゲティング
US10061847B2 (en) 2012-07-20 2018-08-28 Intertrust Technologies Corporation Information targeting systems and methods
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WO2014015305A1 (fr) 2012-07-20 2014-01-23 Intertrust Technologies Corporation Systèmes et procédés de ciblage d'informations
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US9355157B2 (en) 2012-07-20 2016-05-31 Intertrust Technologies Corporation Information targeting systems and methods
US20140114901A1 (en) * 2012-10-19 2014-04-24 Cbs Interactive Inc. System and method for recommending application resources
US20150039608A1 (en) * 2013-07-30 2015-02-05 Netflix.Com, Inc. Media content rankings for discovery of novel content
US9430532B2 (en) * 2013-07-30 2016-08-30 NETFLIX Inc. Media content rankings for discovery of novel content
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