Search engine advertising has become a significant element of the Web browsing experience. Choosing the right items for the query and the order in which they are displayed greatly affects the probability that a user will see and click on each item. This ranking has a strong impact on the revenue the search engine receives from the clicking on advertisements. Displaying the items to the user that they prefer to click on improves user satisfaction. Therefore, it is important to be able to accurately estimate the click-through rate of ads in the system. So the user's experience depends crucially upon the quality of content recommendations. This paper presents an overview of the content recommendation, namely how to recommend a small set of items to a user from an underlying pool of content items according to user's interest. Therefore, we build an online learning framework for personalized recommendation using recommender system. This paper focuses on an approach of interpreting users' actions for the online learning to achieve better item relevance estimation. So that User is provided with the content in which he is interested. And finally the items are ranked according to the user's interest based on the click through rate (CTR).

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Content Recommendation Recommender System Click Through Rate (ctr)