Vineeth Rakesh
Huan Liu
ABSTRACT
Recommendation in the modern world is not only about capturing the interaction between users and items, but also about understanding the relationship between items. Besides improving the quality of recommendation, it enables the generation of candidate items that can serve as substitutes and supplements of another item. For example, when recommending Xbox, PS4 could be a logical substitute and the supplements could be items such as game controllers, surround system, and travel case. Therefore, given a network of items, our objective is to learn their content features such that they explain the relationship between items in terms of substitutes and supplements. To achieve this, we propose a generative deep learning model that links two variational autoencoders using a connector neural network to create Linked Variational Autoencoder (LVA). LVA learns the latent features of items by conditioning on the observed relationship between items. Using a rigorous series of experiments, we show that LVA significantly outperforms other representative and state-of-the-art baseline methods in terms of prediction accuracy. We then extend LVA by incorporating collaborative filtering (CF) to create CLVA that captures the implicit relationship between users and items. By comparing CLVA with LVA we show that inducing CF-based features greatly improve the recommendation quality of substitutable and supplementary items on a user level.
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Index Terms
- Linked Variational AutoEncoders for Inferring Substitutable and Supplementary Items
Recommendations
Item Recommendation with Variational Autoencoders and Heterogeneous Priors
DLRS 2018: Proceedings of the 3rd Workshop on Deep Learning for Recommender SystemsIn recent years, Variational Autoencoders (VAEs) have been shown to be highly effective in both standard collaborative filtering applications and extensions such as incorporation of implicit feedback. We extend VAEs to collaborative filtering with side ...
Discovering unknown but interesting items on personal social network
PAKDD'12: Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part IISocial networking service has become very popular recently. Many recommendation systems have been proposed to integrate with social networking websites. Traditional recommendation systems focus on providing popular items or items posted by close ...
Ranking and Suggesting Popular Items
We consider the problem of ranking the popularity of items and suggesting popular items based on user feedback. User feedback is obtained by iteratively presenting a set of suggested items, and users selecting items based on their own preferences either ...
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