Large-scale Subscriber Preference Modelling for Telcos – Part 2

In Part 1 of this blog article, we looked at the problem of tokenising a URL as an intermediate step towards learning user preference models from browsing histories. In Part 2, we next look at the problem of learning a URL classifier model from the preprocessed Shalla dataset using Support Vector Machines. A standard way … More Large-scale Subscriber Preference Modelling for Telcos – Part 2

Large-scale Subscriber Preference Modelling for Telcos – Part 1

An important way telcos can increase revenue is to improve, within the constraints of privacy laws, provision of personalised services for subscribers. To achieve that, they need to be able to build good subscriber preference models. These can take a number of forms, depending on the specific business context and the exact data available. In this … More Large-scale Subscriber Preference Modelling for Telcos – Part 1

A Text-Based and Self-Adapting Product Recommendation Engine

Content-based recommendation engines are typically done in two steps. In the first, a user-preference model is constructed using a set of predefined features. (For example, in the context of retail, we may have features like Price Sensitivity, Promotion Sensitivity, Coupon Redeemer, Calorie Counter, Working Mums, etc.) In the second step, products are mapped, either directly … More A Text-Based and Self-Adapting Product Recommendation Engine