Abstract
Click-through rate prediction (CTR) is an essential task in recommender system. The existing methods of CTR prediction are generally divided into two classes. The first class is focused on modeling feature interactions, the second class is focused on solving time-series problems. However, the existing models of the second class are not able to handle time-series problems with user feedback information, so we propose PMN to solve this kind of problem. To be able to take full advantage of historical user behavior along with the user feedback, PMN uses the attention mechanism to get the user historical behavior representation and the user preference representation from the original input. Specially, user preference representation is derived from the user feedback information and it explicitly shows the user's attitude towards the candidate, which greatly improve the model performance. Finally, we introduced user preference baselines to solve the problem of inconsistent scoring standards for different users. In this paper, we focus on the CTR prediction modeling in the scenario of video recommendation in Video On Demand (VOD) service. Experimental results on multiple data sets have shown that our PMN model is effective.
Highlights
The Internet content industry has developed rapidly recently, such as online news, online video sites, and online music platforms
There are roughly two classes of mainstream ranking models in Click-through rate prediction (CTR) research field: the first class developed from FM (Factorization Machine) is focused on modeling feature interactions [2]–[8]; the second class is focused on processing time-series data [9], [10]
There are many kinds of time-series data in the real world, such as users’ browsing records or users’ watch history and the FM-type model cannot handle this kind of data very well. Data such as user browsing records are closer to the original data, which means that the data contains the most comprehensive information, but it means that the data contains much noise, so how to fully mine the user preference information contained in these time-series data becomes a key issue in CTR prediction
Summary
The Internet content industry has developed rapidly recently, such as online news, online video sites, and online music platforms. The PMN model we proposed uses a vector to explicitly represent the user preference over the candidate video. There are many kinds of time-series data in the real world, such as users’ browsing records or users’ watch history and the FM-type model cannot handle this kind of data very well Data such as user browsing records are closer to the original data, which means that the data contains the most comprehensive information, but it means that the data contains much noise, so how to fully mine the user preference information contained in these time-series data becomes a key issue in CTR prediction. In the video recommendation scenario which we will discuss in the rest of the paper, Query is the candidate video, and Memory is the user’s watch history. In order to make reasonable recommendations to users based on their historical viewing records and historical feedback scores, our proposed model PMN is used to solve the problem of ranking candidate sets in a recommender system.
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