We demonstrate the effectiveness of the latent user intent modeling via offline analyses as well as live experiments on a large-scale industrial recommendation platform.
As reinforcement learning (RL) naturally fits this objective -- maximizing an user's reward per session -- it has become an emerging topic in recommender systems.
However, the direct use of RL algorithms in the RS setting is impractical due to challenges like off-policy training, huge action spaces and lack of sufficient reward signals.
In this paper, we plan to exploit such redundancy phenomena to improve the performance of RS.
The proposed SMORL agent augments standard recommendation models with additional RL layers that enforce it to simultaneously satisfy three principal objectives: accuracy, diversity, and novelty of recommendations.
We address how to robustly interpret natural language refinements (or critiques) in recommender systems.
In this work, we generalize the use of GCNs for N-partite graphs by considering N multiple context dimensions and propose a simple way for their seamless integration in modern deep learning RS architectures.
Untangle gives control on critiquing recommendations based on users preferences, without sacrificing on recommendation accuracy.
In this paper, we delve on research to continually learn user representations task by task, whereby new tasks are learned while using partial parameters from old ones.
A major component of RL approaches is to train the agent through interactions with the environment.
To overcome this issue, we develop a parameter efficient transfer learning architecture, termed as PeterRec, which can be configured on-the-fly to various downstream tasks.
Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation.
Recommender Systems are becoming ubiquitous in many settings and take many forms, from product recommendation in e-commerce stores, to query suggestions in search engines, to friend recommendation in social networks.
We evaluate the performance of the proposed approach on a well-known time series classification benchmark, considering full adaptation, partial adaptation, and no adaptation of the encoder to the new data type.
In this paper, we propose a task-based hard attention mechanism that preserves previous tasks' information without affecting the current task's learning.
Ranked #2 on Continual Learning on 20Newsgroup (10 tasks)
Due to the structure of the data coming from recommendation domains (i. e., one-hot-encoded vectors of item preferences), these algorithms tend to have large input and output dimensionalities that dominate their overall size.
Session-based recommendations are highly relevant in many modern on-line services (e. g. e-commerce, video streaming) and recommendation settings.
RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner.
Our results indicate that, compared to the best baseline, tree-based models can deliver up to 14% better forecasts for regular hot spots and 153% better forecasts for non-regular hot spots.
We investigate a novel cluster-of-bandit algorithm CAB for collaborative recommendation tasks that implements the underlying feedback sharing mechanism by estimating the neighborhood of users in a context-dependent manner.
We investigate an efficient context-dependent clustering technique for recommender systems based on exploration-exploitation strategies through multi-armed bandits over multiple users.
We apply recurrent neural networks (RNN) on a new domain, namely recommender systems.
Ranked #11 on Session-Based Recommendations on yoochoose1/64
Finding repeated patterns or motifs in a time series is an important unsupervised task that has still a number of open issues, starting by the definition of motif.
Our algorithm takes into account the collaborative effects that arise due to the interaction of the users with the items, by dynamically grouping users based on the items under consideration and, at the same time, grouping items based on the similarity of the clusterings induced over the users.