MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation

Li, Chang; Markov, Ilia; Rijke, Maarten de; Zoghi, Masrour

doi:10.48550/arxiv.1812.04412

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…In future work, we intend to conduct experiments on live systems, where feedback is obtained from multiple users so as to test whether CascadeHybrid can learn across users. Another direction is to adapt Thompson sampling [34] to our hybrid model, since Thompson sampling generally outperforms UCB-based algorithms [21,38].…”

Section: Discussionmentioning

confidence: 99%

Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity

Feng

Rijke

2020

Fourteenth ACM Conference on Recommender Systems

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Online learning to rank (LTR) focuses on learning a policy from user interactions that builds a list of items sorted in decreasing order of the item utility. It is a core area in modern interactive systems, such as search engines, recommender systems, or conversational assistants. Previous online LTR approaches either assume the relevance of an item in the list to be independent of other items in the list or the relevance of an item to be a submodular function of the utility of the list. The former type of approach may result in a list of low diversity that has relevant items covering the same aspects, while the latter approaches may lead to a highly diversified list but with some non-relevant items.In this paper, we study an online LTR problem that considers both item relevance and topical diversity. We assume cascading user behavior, where a user browses the displayed list of items from top to bottom and clicks the first attractive item and stops browsing the rest. We propose a hybrid contextual bandit approach, called CascadeHybrid, for solving this problem. CascadeHybrid models item relevance and topical diversity using two independent functions and simultaneously learns those functions from user click feedback. We derive a gap-free bound on the n-step regret of CascadeHybrid. We conduct experiments to evaluate CascadeHybrid on the MovieLens and Yahoo music datasets. Our experimental results show that CascadeHybrid outperforms the baselines on both datasets.

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Section: Discussionmentioning

confidence: 99%

Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity

Feng

Rijke

2020

Fourteenth ACM Conference on Recommender Systems

Self Cite

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“…One popular method is 'grid search', in which a set of possible values is defined for each parameter, then grid search determines the best value for each parameter to maximize the system effectiveness on a query set [48]. Alternative methods include 'line search' [43], 'Bayesian optimization' [39], and 'transfer learning' [45].…”

Section: Introductionmentioning

confidence: 99%

Selective Query Processing: A Risk-Sensitive Selection of Search Configurations

Mothe

Zia

2023

ACM Trans. Inf. Syst.

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In information retrieval systems, search parameters are optimized to ensure high effectiveness based on a set of past searches and these optimized parameters are then used as the search configuration for all subsequent queries. A better approach, however, would be to adapt the parameters to fit the query at hand. Selective query expansion is one such an approach, in which the system decides automatically whether or not to expand the query, resulting in two possible search configurations. This approach was extended recently to include many other parameters, leading to many possible search configurations where the system automatically selects the best configuration on a per-query basis. One problem with this approach is the system training which requires evaluation of each training query with every possible configuration. In real-world systems, so many parameters and possible values must be evaluated that this approach is impractical, especially when the system must be updated frequently, as is the case for commercial search engines. In general, the more configurations, the greater the effectiveness when configuration selection is appropriate but also the greater the risk of decreasing effectiveness in the case of an inappropriate configuration selection. To determine the ideal configurations to use on a per-query basis in real-world systems we developed a method in which a restricted number of possible configurations is pre-selected and then used in a meta-search engine that decides the best search configuration on a per query basis. We define a risk-sensitive approach for configuration pre-selection that considers the risk-reward trade-off between the number of configurations kept, and system effectiveness. We define two alternative risk functions to apply to different goals. For final configuration selection, the decision is based on query feature similarities. We compare two alternative risk functions on two query types: ad hoc and diversity and compare these to more sophisticated machine learning-based methods. We find that a relatively small number of configurations (20) selected by our risk-sensitive model is sufficient to obtain results close to the best achievable results for each query. Effectiveness is increased by about 15% according to the P@10 and nDCG@10 evaluation metrics when compared to traditional grid search using a single configuration and by about 20% when compared to learning to rank documents. Our risk-sensitive approach works for both diversity- and ad hoc-oriented searches. Moreover, the similarity-based selection method outperforms the more sophisticated approaches. Thus, we demonstrate the feasibility of developing per-query information retrieval systems, which will guide future research in this direction.

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Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model

Rijke

2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

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Non-stationarity appears in many online applications such as web search and advertising. In this paper, we study the online learning to rank problem in a non-stationary environment where user preferences change abruptly at an unknown moment in time. We consider the problem of identifying the K most attractive items and propose cascading nonstationary bandits, an online learning variant of the cascading model, where a user browses a ranked list from top to bottom and clicks on the first attractive item. We propose two algorithms for solving this non-stationary problem: CascadeDUCB and CascadeSWUCB. We analyze their performance and derive gap-dependent upper bounds on the nstep regret of these algorithms. We also establish a lower bound on the regret for cascading nonstationary bandits and show that both algorithms match the lower bound up to a logarithmic factor. Finally, we evaluate their performance on a realworld web search click dataset.

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MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation

Cited by 4 publications

References 39 publications

Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity

Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity

Selective Query Processing: A Risk-Sensitive Selection of Search Configurations

Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model

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