Davai Predictive User Modeling

Introduction

The web is rapidly changing from being just a source of information consumption to a 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 like Facebook - an egocentric social network that is used to stay in touch with friends and family and Twitter - a micro-blogging site that is used to broadcast news, ideas, and opinions to followers.

However, the change is deeper if one considers the communication patterns enabled by ubiquitous connectivity and mobile devices. We are moving away from a web of clicks to a web of online activities and interactions that people perform in a conversation style:

Users of the ‘old’ web would leave behind an anonymous trail of clicks on hyperlinks that marks their content consumption patterns and implicit interests. This click-stream model has been successfully exploited by search engines to create a database of intent that assigns user intent to searches performed by users. The search model relies 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 their network of online friends. Modeling user activity by clicks or queries as on today web doesn’t capture the rich interaction between people that is possible moving forward. Davai’s behavior modeling is therefore based on an activity-stream model.

The social web with its conversation style is inherently participatory and communication is shared. People are interested in what their friends and family are doing, what they think and their opinions. Every activity is therefore in some context newsworthy. In fact social connectivity and preference in-itself are strong

Web Social Web

Click-stream Model Activity-stream Model

indicators of affinity, interests and therefore ‘intent’. This inference of intent in made even stronger by the interactions on the social web.

Social network services strive to make participation implicit by turning a user interaction with the service into content, i.e. many activities are recorded automatically. Changing a profile, clicking on a like button, purchasing a product; all might be recorded automatically for friends to see.

Many of today’s egocentric social networks started as community focused online places for similar minded people to interact in privacy. However the trend is towards open social networks with a significant portion of the interaction being public: People want to meet new people and find new information as part of their social

interaction, which requires sharing of a basic set of personal information, Social Network sites feel a need to monetize their membership by making their

profile information available/accessible to business, which in turn requires the sites users to relax privacy expectations,

Social Networks, which historically have been walled gardens, are pushed to open up for external content (e.g. facebook apps) or to expose their social graph to external sites (e.g. facebook connect), and

Users accept a minimum of sharing of personal information if value added context-aware online service are offered in return for the social graph and interaction patters as long as these are generalized and the platform shields/anonymizes the users from service providers. For example sharing location information on mobile devices to obtain valuable location-based services (like on Yelp or foursquare).

At Davai we are aware of these changing trends. The newfound social web paradigm that generates online user activity is a rich context for predictive user modeling. Modeling online users generates tremendously valuable insights on user intent, which can be used to provide services to business and consumers.

Developing technology that seeks to understand and predict user interests from observing the activity stream, profile information and social graph, published on the social web, to create models to predict user demographics, behavior and interests is the 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 social

objects, and Context-aware services especially on mobile devices such as personal assistants.

All services we envision are permission-based, i.e. the user opts into the services and in turn receives personalized commercial offers, online services or direct monetary incentives.

Mining the User Activity Stream of the Social Web An ActivityStraem, or Live Stream or simply Stream is a feed of activists performed by an actor on one or more online web sites. Many different social networking sites have started to publish activities stream of their users.

The activity in ActivityStreams is a description of an action that was performed (the verb) at some instant in time by someone or something (the actor) against some kind 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 canonical representation of actions before any kind of analysis or mining activity can be performed.

Once standardized one can approach mining correlations out of the data set. The challenge is to perform this in a real-time stream environment. Traditional data mining algorithms require a fixed vocabulary and fixed set of objects for their calculation, an assumption that cannot hold for real-time streams.

In order to address the need of real-time and stream based data mining incremental algorithms have to be used. The data set to be analyzed is typically constraint by a sliding window that moves over the stream and controls which event is considered and which not. Additional approximation of algorithms by using heuristics is necessary to meet real-time needs.

The following figure summarizes the high-level approach of Davai:

Davai analyzes online communication of users on social networks. Conversations center 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 for which a predictive user model – a set of hidden states and state transitions – has to be generated.

The predictive user models in Davai are generated through a process of statistical machine-learning procedures. The models allow assigning users to specific classes based on the online behavior. Classes can indicate topic and commercial interest, responsiveness to special marketing campaigns, etc.