In plain words: A tree narrows items from broad to specific, so a deep network scores a user against a few nodes instead of the catalog, keeping search time nearly flat as items grow. It beat usual recommenders on two large datasets and in a Taobao ad test.
Abstract · Learning Tree-based Deep Model for Recommender Systems
Model-based methods for recommender systems have been studied extensively in recent years. In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult. To overcome the calculation barriers, models such as matrix factorization resort to inner product form (i.e., model user-item preference as the inner product of user, item latent factors) and indexes to facilitate efficient approximate k-nearest neighbor searches. However, it still remains challenging to incorporate more expressive interaction forms between user and item features, e.g., interactions through deep neural networks, because of the calculation cost. In this paper, we focus on the problem of introducing arbitrary advanced models to recommender systems with large corpus. We propose a novel tree-based method which can provide logarithmic complexity w.r.t. corpus size even with more expressive models such as deep neural networks. Our main idea is to predict user interests from coarse to fine by traversing tree nodes in a top-down fashion and making decisions for each user-node pair. We also show that the tree structure can be jointly learnt towards better compatibility with users' interest distribution and hence facilitate both training and prediction. Experimental evaluations with two large-scale real-world datasets show that the proposed method significantly outperforms traditional methods. Online A/B test results in Taobao display advertising platform also demonstrate the effectiveness of the proposed method in production environments.
Han Zhu, Xiang Li, Pengye Zhang, Guozheng Li, Jie He, Han Li, Kun Gai
arXiv:1801.02294 · stat.ML, cs.IR, cs.LG · submitted Jan 8, 2018 · updated Dec 21, 2018
abstract · pdf · html · Accepted by KDD 2018
My impression of Youtube, Amazon, Tumblr and other is that the recommendation process in practice is close to useless. And this isn't because I don't want recommendations.
Moreover, in all these situations, I feel like it just be improved by asking the user instead. There are thing I'd love recommendations on and I'd be quite willing to tell these portals about my preferences. But it doesn't seem like they want to know, don't have internal search worth much, don't take my search terms account when recommending, etc.
It seems like in practice, most sites actually want the effects of not being able to immediate drill past items that leverage their crappy interface to get exposure since I assume there are extra profits in one form or another to be made with these.