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Evaluating Recommendation Systems
TLDR
This paper discusses how to compare recommenders based on a set of properties that are relevant for the application, and focuses on comparative studies, where a few algorithms are compared using some evaluation metric, rather than absolute benchmarking of algorithms. Expand
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
TLDR
This paper reviews the proper construction of offline experiments for deciding on the most appropriate algorithm, and discusses three important tasks of recommender systems, and classify a set of appropriate well known evaluation metrics for each task. Expand
An MDP-Based Recommender System
TLDR
The use of an n-gram predictive model is suggested for generating the initial MDP, which induces a Markovchain model of user behavior whose predictive accuracy is greater than that of existing predictive models. Expand
A survey of point-based POMDP solvers
TLDR
This survey walks the reader through the fundamentals of point-based value iteration, explaining the main concepts and ideas, and surveys the major extensions to the basic algorithm, discussing their merits. Expand
Evaluating Recommender Systems
TLDR
This paper discusses how to compare recommenders based on a set of properties that are relevant for the application, and focuses on comparative studies, where a few algorithms are compared using some evaluation metric, rather than absolute benchmarking of algorithms. Expand
Forward Search Value Iteration for POMDPs
TLDR
A new algorithm is suggested, FSVI, that uses the underlying MDP to traverse the belief space towards rewards, finding sequences of useful backups, and show how it scales up better than HSVI on larger benchmarks. Expand
Replanning in Domains with Partial Information and Sensing Actions
TLDR
This paper's planner employs a novel, lazy, regression-based method for querying the belief state, and overcome the non-determinism in sensing actions, and the large domain size introduced by T0 by using state sampling. Expand
TALMUD: transfer learning for multiple domains
TLDR
A transfer learning technique that extracts knowledge from multiple domains containing rich data and generates recommendations for a sparse target domain and integrates the appropriate amount of knowledge from each domain in order to enrich the target domain data is proposed. Expand
Bayesian Real-Time Dynamic Programming
TLDR
VPI-RTDP leads to an improvement over state-of-the-art RTDP methods, empirically yielding up to a three-fold reduction in the amount of time and number of visited states required to achieve comparable policy performance. Expand
Improving Simple Collaborative Filtering Models Using Ensemble Methods
TLDR
It is shown that the performance of an ensemble of simple (weak) CF models such as k-NN is competitive compared with a single strong CF model (such as matrix factorization) while requiring an order of magnitude less computational cost. Expand
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