Collaborative filtering for massive multinomial data |
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Authors: | Andrew Cron Liang Zhang Deepak Agarwal |
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Institution: | 1. Department of Statistical Science, Duke University, Durham, NC 27708, USA;2. Yahoo Inc., 4401 Great America Pkwy, Santa Clara, CA 95054, USA |
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Abstract: | Content recommendation on a webpage involves recommending content links (items) on multiple slots for each user visit to maximize some objective function, typically the click-through rate (CTR) which is the probability of clicking on an item for a given user visit. Most existing approaches to this problem assume user's response (click/no click) on different slots are independent of each other. This is problematic since in many scenarios CTR on a slot may depend on externalities like items recommended on other slots. Incorporating the effects of such externalities in the modeling process is important to better predictive accuracy. We therefore propose a hierarchical model that assumes a multinomial response for each visit to incorporate competition among slots and models complex interactions among (user, item, slot) combinations through factor models via a tensor approach. In addition, factors in our model are drawn with means that are based on regression functions of user/item covariates, which helps us obtain better estimates for users/items that are relatively new with little past activity. We show marked gains in predictive accuracy by various metrics. |
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Keywords: | recommender systems collaborative filtering multinomial response stochastic gradient descent hierarchical modeling tensor factorization |
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