| 1. |
- Brodén, Björn, et al.
(författare)
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Ensemble Recommendations via Thompson Sampling : an Experimental Study within e-Commerce
- 2018
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Ingår i: Proceeding IUI '18 23rd International Conference on Intelligent User Interfaces. - New York, NY, USA : ACM Digital Library. ; , s. 19-29
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Konferensbidrag (refereegranskat)abstract
- This work presents an extension of Thompson Sampling bandit policy for orchestrating the collection of base recommendation algorithms for e-commerce. We focus on the problem of item-to-item recommendations, for which multiple behavioral and attribute-based predictors are provided to an ensemble learner. We show how to adapt Thompson Sampling to realistic situations when neither action availability nor reward stationarity is guaranteed. Furthermore, we investigate the effects of priming the sampler with pre-set parameters of reward probability distributions by utilizing the product catalog and/or event history, when such information is available. We report our experimental results based on the analysis of three real-world e-commerce datasets.
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| 2. |
- Paraschakis, Dimitris, 1980-
(författare)
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Algorithmic and Ethical Aspects of Recommender Systems in e-Commerce
- 2018
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Recommender systems have become an integral part of virtually every e-commerce application on the web. These systems enable users to quickly discover relevant products, at the same time increasing business value. Over the past decades, recommender systems have been modeled using numerous machine learning techniques. However, the adoptability of these models by commercial applications remains unclear. We assess the receptiveness of the industrial sector to algorithmic contributions of the research community by surveying more than 30 e-commerce platforms, and experimenting with various recommenders on proprietary e-commerce datasets. Another overlooked but important factor that complicates the design and use of recommender systems is their ethical implications. We identify and summarize these issues in our ethical recommendation framework, which also enables users to control sensitive moral aspects of recommendations via the proposed “ethical toolbox”. The feasibility of this tool is supported by the results of our user study. Because of moral implications associated with user profiling, we investigate algorithms capable of generating user-agnostic recommendations. We propose an ensemble learning scheme based on Thompson Sampling bandit policy, which models arms as base recommendation functions. We show how to adapt this algorithm to realistic situations when neither arm availability nor reward stationarity is guaranteed.
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| 3. |
- Paraschakis, Dimitris, 1980-, et al.
(författare)
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FlowRec : Prototyping Session-based Recommender Systemsin Streaming Mode
- 2020
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Ingår i: PAKDD 2020. - Cham : Springer. - 9783030474256 - 9783030474263
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Konferensbidrag (refereegranskat)abstract
- Despite the increasing interest towards session-based and streaming recommender systems, there is still a lack of publicly available evaluation frameworks supporting both these paradigms. To address the gap, we propose FlowRec — an extension of the streaming framework Scikit-Multiflow, which opens plentiful possibilities for prototyping recommender systems operating on sessionized data streams, thanks to the underlying collection of incremental learners and support for real-time performance tracking. We describe the extended functionalities of the adapted prequential evaluation protocol, and develop a competitive recommendation algorithm on top of Scikit-Multiflow’s implementation of a Hoeffding Tree. We compare our algorithm to other known baselines for the next-item prediction task across three different domains.
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| 4. |
- Paraschakis, Dimitris, 1980-, et al.
(författare)
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Matchmaking Under Fairness Constraints : A Speed Dating Case Study
- 2020
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Ingår i: Bias and Social Aspects in Search and Recommendation. - Cham : Springer. - 9783030524845 - 9783030524852 ; , s. 43-57
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Konferensbidrag (refereegranskat)abstract
- Reported evidence of biased matchmaking calls into question the ethicality of recommendations generated by a machine learning algorithm. In the context of dating services, the failure of an automated matchmaker to respect the user’s expressed sensitive preferences (racial, religious, etc.) may lead to biased decisions perceived by users as unfair. To address the issue, we introduce the notion of preferential fairness, and propose two algorithmic approaches for re-ranking the recommendations under preferential fairness constraints. Our experimental results demonstrate that the state of fairness can be reached with minimal accuracy compromises for both binary and non-binary attributes.
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| 5. |
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| 6. |
- Paraschakis, Dimitris, 1980-
(författare)
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Sociotechnical Aspects of Automated Recommendations : Algorithms, Ethics, and Evaluation
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Recommender systems are algorithmic tools that assist users in discovering relevant items from a wide range of available options. Along with the apparent user value in mitigating the choice overload, they have an important business value in boosting sales and customer retention. Last, but not least, they have brought a substantial research value to the algorithm developments of the past two decades, mainly in the academic community. This thesis aims to address some of the aspects that are important to consider when recommender systems pave their way towards real-life applications.
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