Interactive path reasoning on graph for conversational recommendation
Traditional recommendation systems estimate user preference on items from past interaction
history, thus suffering from the limitations of obtaining fine-grained and dynamic user …
history, thus suffering from the limitations of obtaining fine-grained and dynamic user …
Estimation-action-reflection: Towards deep interaction between conversational and recommender systems
Recommender systems are embracing conversational technologies to obtain user
preferences dynamically, and to overcome inherent limitations of their static models. A …
preferences dynamically, and to overcome inherent limitations of their static models. A …
Multi-armed bandits in recommendation systems: A survey of the state-of-the-art and future directions
Abstract Recommender Systems (RSs) have assumed a crucial role in several digital
companies by directly affecting their key performance indicators. Nowadays, in this era of big …
companies by directly affecting their key performance indicators. Nowadays, in this era of big …
Weighted linear bandits for non-stationary environments
We consider a stochastic linear bandit model in which the available actions correspond to
arbitrary context vectors whose associated rewards follow a non-stationary linear regression …
arbitrary context vectors whose associated rewards follow a non-stationary linear regression …
Seamlessly unifying attributes and items: Conversational recommendation for cold-start users
Static recommendation methods like collaborative filtering suffer from the inherent limitation
of performing real-time personalization for cold-start users. Online recommendation, eg …
of performing real-time personalization for cold-start users. Online recommendation, eg …
" Deep reinforcement learning for search, recommendation, and online advertising: a survey" by Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin with Martin …
Search, recommendation, and online advertising are the three most important information-
providing mechanisms on the web. These information seeking techniques, satisfying users' …
providing mechanisms on the web. These information seeking techniques, satisfying users' …
Fair contextual multi-armed bandits: Theory and experiments
When an AI system interacts with multiple users, it frequently needs to make allocation
decisions. For instance, a virtual agent decides whom to pay attention to in a group, or a …
decisions. For instance, a virtual agent decides whom to pay attention to in a group, or a …
Dynamic causal Bayesian optimization
V Aglietti, N Dhir, J González… - Advances in Neural …, 2021 - proceedings.neurips.cc
We study the problem of performing a sequence of optimal interventions in a dynamic causal
system where both the target variable of interest, and the inputs, evolve over time. This …
system where both the target variable of interest, and the inputs, evolve over time. This …
Deep reinforcement learning for information retrieval: Fundamentals and advances
Information retrieval (IR) techniques, such as search, recommendation and online
advertising, satisfying users' information needs by suggesting users personalized objects …
advertising, satisfying users' information needs by suggesting users personalized objects …
When people change their mind: Off-policy evaluation in non-stationary recommendation environments
We consider the novel problem of evaluating a recommendation policy offline in
environments where the reward signal is non-stationary. Non-stationarity appears in many …
environments where the reward signal is non-stationary. Non-stationarity appears in many …