Michal Aharon

Watch-It-Next: A Contextual TV Recommendation System

As consumers of television are presented with a plethora of available programming, improving recommender systems in this domain is becoming increasingly important. Television sets, though, are often shared by multiple users whose tastes may greatly vary. Recommendation systems are challenged by this setting, since viewing data is typically collected and modeled per device, aggregating over its users and obscuring their individual tastes. This paper tackles the challenge of TV recommendation, specifically aiming to provide recommendations for the next program to watch following the currently watched program the device. We present an empirical evaluation of several recommendation methods over large-scale, real-life TV viewership data. Our extentions of common state-of-the-art recommendation methods, exploiting the current watching context, demonstrate a significant improvement in recommendation quality.

Serving Ads to "Yahoo Answers" Occasional Visitors

Modern ad serving systems can benefit when allowed to accumulate user information and use it as part of the serving algorithm. However, this often does not coincide with how the web is used. Many domains will see users for only brief interactions, as users enter a domain through a search result or social media link and then leave. Having access to little or no user information and no ability to assemble a user profile over a prolonged period of use, we would still like to leverage the information we have to the best of our ability. In this paper we attempt several methods of improving ad serving for occasional users, including leveraging user information that is still available, content analysis of the page, information about the pages content generators and historical breakdown of visits to the page. We compare and combine these methods in a framework of a collaborative filtering algorithm, test them on real data collected from Yahoo Answers, and achieve significant improvements over baseline algorithms.

Adaptive Online Hyper-Parameters Tuning for Ad Event-Prediction Models

Yahoos native advertising (also known as Gemini native) is one of its fastest growing businesses, reaching a run-rate of several hundred Millions USD in the past year. Driving the Gemini native models that are used to predict both, click probability (pCTR) and conversion probability (pCONV), is OFFSET - a feature enhanced collaborative-filtering (CF) based event prediction algorithm. OFFSET is a one-pass algorithm that updates its model for every new batch of logged data using a stochastic gradient descent (SGD) based approach. As most learning algorithms, OFFSET includes several hyper-parameters that can be tuned to provide best performance for a given system conditions. Since the marketplace environment is very dynamic and influenced by seasonality and other temporal factors, having a fixed single set of hyper-parameters (or configuration) for the learning algorithm is sub-optimal. In this work we present an online hyper-parameters tuning algorithm, which takes advantage of the system parallel map-reduce based architecture, and strives to adapt the hyper-parameters set to provide the best performance at a specific time interval. Online evaluation via bucket testing of the tuning algorithm showed a significant 4.3% revenue lift overall traffic, and a staggering 8.3% lift over Yahoo Home-Page section traffic. Since then, the tuning algorithm was pushed into production, tuning both click- and conversion-prediction models, and is generating a hefty estimated revenue lift of 5% yearly for Yahoo Gemini native. The proposed tuning mechanism can be easily generalized to fit any learning algorithm that continuously learns on incoming streaming data, in order to adapt its hyper-parameters to temporal changes.