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Recommender systems are now popular both commercially and in the research community, where many algorithms have been suggested for providing recommendations. These algorithms typically perform differently in various domains and tasks. Therefore, it is important from the research perspective, as well as from a practical view, to be able to decide on an(More)
Recent scaling up of POMDP solvers towards realistic applications is largely due to point-based methods which quickly converge to an approximate solution for medium-sized problems. Of this family HSVI, which uses trial-based asynchronous value iteration, can handle the largest domains. In this paper we suggest a new algorithm, FSVI, that uses the underlying(More)
Replanning via determinization is a recent, popular approach for online planning in MDPs. In this paper we adapt this idea to classical, non-stochastic domains with partial information and sensing actions, presenting a new planner: SDR (Sample, Determinize, Replan). At each step we generate a solution plan to a classical planning problem induced by the(More)
Real-time dynamic programming (RTDP) solves Markov decision processes (MDPs) when the initial state is restricted, by focusing dynamic programming on the envelope of states reachable from an initial state set. RTDP often provides performance guarantees without visiting the entire state space. Building on RTDP, recent work has sought to improve its(More)
Users often configure complex objects with many possible internal choices. Recommendation engines that automatically configure such objects given user preferences and constraints, may provide much value in such cases. These applications generate appropriate recommendations based on user preferences. It is likely, though, that the user will not be able to(More)
During the past few years, point-based POMDP solvers have gradually scaled up to handle medium sized domains through better selection of the set of points and efficient backup methods. Point-based research has focused on flat, explicit representation of the state space, yet in many realistic domains a factored representation is more appropriate. The latter(More)
Most collaborative Recommender Systems (RS) operate in a single domain (such as movies, books, etc.) and are capable of providing recommendations based on historical usage data which is collected in the specific domain only. Cross-domain recommenders address the sparsity problem by using Machine Learning (ML) techniques to transfer knowledge from a dense(More)