Cost-Aware Cascading Bandits
نویسندگان
چکیده
منابع مشابه
Cascading Bandits
The cascade model is a well-established model of user interaction with content. In this work, we propose cascading bandits, a learning variant of the model where the objective is to learn K most attractive items out of L ground items. We cast the problem as a stochastic combinatorial bandit with a non-linear reward function and partially observed weights of items. Both of these are challenging ...
متن کاملCombinatorial Cascading Bandits
We propose combinatorial cascading bandits, a class of partial monitoring problems where at each step a learning agent chooses a tuple of ground items subject to constraints and receives a reward if and only if the weights of all chosen items are one. The weights of the items are binary, stochastic, and drawn independently of each other. The agent observes the index of the first chosen item who...
متن کاملContextual Combinatorial Cascading Bandits
We propose the contextual combinatorial cascading bandits, a combinatorial online learning game, where at each time step a learning agent is given a set of contextual information, then selects a list of items, and observes stochastic outcomes of a prefix in the selected items by some stopping criterion. In online recommendation, the stopping criterion might be the first item a user selects; in ...
متن کاملCascading Bandits for Large-Scale Recommendation Problems
Most recommender systems recommend a list of items. The user examines the list, from the first item to the last, and often chooses the first attractive item and does not examine the rest. This type of user behavior can be modeled by the cascade model. In this work, we study cascading bandits, an online learning variant of the cascade model where the goal is to recommend K most attractive items ...
متن کاملOnline Clustering of Contextual Cascading Bandits
We consider a new setting of online clustering of contextual cascading bandits, an online learning problem where the underlying cluster structure over users is unknown and needs to be learned from a random prefix feedback. More precisely, a learning agent recommends an ordered list of items to a user, who checks the list and stops at the first satisfactory item, if any. We propose an algorithm ...
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2020
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2020.3001388