Davai predictive user modeling

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  • 1. Davai Predictive User ModelingIntroductionThe web is rapidly changing from being just a source of information consumption toa place where people produce, consume, share and interact with information. Hence,this is the social web. The need to interact socially has led to the success of sites likeFacebook - an egocentric social network that is used to stay in touchwith friends andfamily and Twitter - a micro-blogging site that is used to broadcast news, ideas, andopinions to followers.However, the change is deeper if one considers the communication patterns enabledby ubiquitous connectivity and mobile devices. We are moving away from a web ofclicks to a web of online activities and interactions that people perform in aconversation style:Users of the ‘old’ web would leave behind an anonymous trail of clicks on hyperlinksthat marks their content consumption patterns and implicit interests. This click-stream model has been successfully exploited by search engines to create a databaseof intent that assigns user intent to searches performed by users. The search modelrelies on the click since intent inference is limited to the click and very little meta-data is available other than the click.On the social web people engage in more complex activities in the context of theirnetwork of online friends. Modeling user activity by clicks or queries as on todayweb doesn’t capture the rich interaction between people that is possible movingforward. Davai’s behavior modeling is therefore based on an activity-stream model.The social web with its conversation style is inherently participatory andcommunication is shared. People are interested in what their friends and family aredoing, what they think and their opinions. Every activity is therefore in somecontext newsworthy. In fact social connectivity and preference in-itself are strong
  • 2. indicators of affinity, interests and therefore ‘intent’. This inference of intent inmade even stronger by the interactions on the social web.Social network services strive to make participation implicit by turning a userinteraction with the service into content, i.e. many activities are recordedautomatically. Changing a profile, clicking on a like button, purchasing a product; allmight be recorded automatically for friends to see.Many of today’s egocentric social networks started as community focused onlineplaces for similar minded people to interact in privacy. However the trend istowards open social networks with a significant portion of the interaction beingpublic:People want to meet new people and find new information as part of their socialinteraction, which requires sharing of a basic set of personal information,Social Network sites feel a need to monetize their membership by making theirprofile information available/accessible to business, which in turn requires thesites users to relax privacy expectations,Social Networks, which historically have been walled gardens, are pushed toopen up for external content (e.g. facebook apps) or to expose their social graphto external sites (e.g. facebook connect), andUsers accept a minimum of sharing of personal information if value addedcontext-aware online service are offered in return for the social graph andinteraction patters as long as these are generalized and the platformshields/anonymizes the users from service providers. For example sharinglocation information on mobile devices to obtain valuable location-basedservices (like on Yelp or foursquare).At Davai weare aware of these changing trends. The newfound social web paradigmthat generates online user activity is a rich context for predictive user modeling.Modeling online users generates tremendously valuable insights on userintent,which can be used to provide services to business and consumers.Developing technology that seeks to understand and predict user interests fromobserving the activity stream, profile information and social graph, published on thesocial web, to create models to predict user demographics, behavior and interests isthe core strength of the Davai platform and services.The key areas of investment are predictive user models in support of:Online direct/interactive marketing on social networks such as lead generation,personalized sales promotions, or customer relation management,A new kind of interactive and user generated content, which we call socialobjects, andContext-aware services especially on mobile devices such as personal assistants.All services we envision are permission-based, i.e. the user opts into the servicesand in turn receives personalized commercial offers, online services or directmonetary incentives.
  • 3. Mining the User Activity Stream of the Social WebAn ActivityStraem, or Live Stream or simply Stream is a feed of activists performedby an actor on one or more online web sites. Many different social networking siteshave started to publish activities stream of their users.The activity in ActivityStreams is a description of an action that was performed (theverb) at some instant in time by someone or something (the actor) against somekind of person, place, or thing (the object).There are many different social network services and each has its on set of activities,actors and objects. These formats have to be standardized into a canonicalrepresentation of actions before any kind of analysis or mining activity can beperformed.Once standardized one can approach mining correlations out of the data set. Thechallenge is to perform this in a real-time stream environment. Traditional datamining algorithms require a fixed vocabulary and fixed set of objects for theircalculation, an assumption that cannot hold for real-time streams.In order to address the need of real-time and stream based data mining incrementalalgorithms have to be used. The data set to be analyzed is typically constraint by asliding window that moves over the stream and controls which event is consideredand which not. Additional approximation of algorithms by using heuristics isnecessary to meet real-time needs.
  • 4. The following figure summarizes the high-level approach of Davai:Davai analyzes online communication of users on social networks. Conversationscenter on social objects, which are people, places and things we talk about.Locations are the real-world locations where the conversations occur.Communication on the social web manifests itself in online activity streams or actor,verb, and objects triplets over time. These constitute the observable variables forwhich a predictive user model – a set of hidden states and state transitions – has tobe generated.The predictive user models in Davai are generated through a process of statisticalmachine-learning procedures. The models allow assigning users to specific classesbased on the online behavior. Classes can indicate topic and commercial interest,responsiveness to special marketing campaigns, etc.