Abstract
A domain that has gained popularity in the past few years is personalized advertisement. Researchers and developers collect user contextual attributes (e.g., location, time, history, etc.) and apply state-of-the-art algorithms to present relevant ads. A problem occurs when the user has limited or no data available and, therefore, the algorithms cannot work well. This situation is widely referred in the literature as the ‘cold-start’ case. The aim of this manuscript is to explore this problem and present a prediction approach for personalized mobile advertising systems that addresses the cold-start, and especially the frozen user case, when a user has no data at all. The approach consists of three steps: (a) identify existing datasets and use specific attributes that could be gathered from a frozen user, (b) train and test machine learning models in the existing datasets and predict click-through rate, and (c) the development phase and the usage in a system.
Highlights
The cold-start problem arises in two cases: 1. The new user—When there is a new user in the system that has not yet interacted with enough data objects
Starting with the first category, Richardson et al, 2007 used a model that takes into account user search term and is based on logistic regression, to predict the click through rate (CTR) for new advertisements [16]
The second category of works tries to alleviate the cold-start problem from the new user side, as it was discussed above
Summary
The advertisement domain has gained huge popularity in the past few years and its revenue is estimated at billions every year. Mobile applications either use personalization systems that have been created by their developers or, more often, a mediator network to display advertisements (e.g., Google). To design and develop such systems, researchers and industries gather and utilize user context (e.g., location, activities, etc.) to determine his/her needs and apply personalization algorithms (contextual-based advertising) [1,2]. To achieve better context-awareness and design efficient advertisement personalization systems, researchers combine this domain with other emerging software-related domains such as artificial intelligence, semantic web, etc. Such approaches offer new opportunities for advertisers, users/potential customers, and personalized advertising mediator systems. Apart from efficiency, better customer relationships and more interactive communication between consumers and businesses can be built [4]
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