Blending of search results from several vertical sources became standard among web search engines. Similar scenarios appear in computational advertising, news recommendation, and other interactive systems. As such environments give only partial feedback, the evaluation of new policies conventionally requires expensive online A/B tests. Counterfactual approach is a promising alternative, nevertheless, it requires specific conditions for a valid off-policy evaluation. We release a large-scale, real-world vertical-blending dataset gathered bySeznam.cz web search engine. The dataset contains logged partial feedback with the corresponding propensity and is thus suited for counterfactual evaluation. We provide basic checks for validity and evaluate several learning methods.

Interest Beat (inbeat.eu) is an open source recommender framework that fulfills some of the demands raised by emerging applications that infer ratings from sensor input or use linked open data cloud for feature expansion. As a recommender algorithm, InBeat uses association rules, which allow to explain why a specific recommendation was made. Due to modular architecture, other algorithms can be easily plugged in. InBeat has a pure JavaScript version, which allows to confine processing to a client-side device. There is a performance optimized server-side bundle, which succesfully participated in two recent recommender competitions involving large volumes of streaming data. InBeat works on a number of platforms and is also available for Docker.

This paper demonstrates Interest Beat (InBeat.eu) as a recommender system for online videos, which determines user interest in the content based on gaze tracking with Microsoft Kinect in addition to explicit user feedback. Content of the videos is represented using a semantic wikifier. User profile is constructed from preference rules, which are discovered with an association rule learner.

GAIN (inbeat.eu) is a web application and service for capturing and preprocessing user interactions with semantically described content. GAIN outputs a set of instances in tabular form suitable for further processing with generic machine-learning algorithms. GAIN is demoed as a component of a "SMART-TV" recommender system. Content is automatically described with DBpedia types using a Named Entity Recognition (NER) system. Interest is determined based on explicit user actions and user's attention computed by 3D head pose estimation. Preference rules are learnt with an association rule mining algorithm. These can be e.g. deployed to a business rules system, acting as a recommender.