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The Impact of Selection Bias on Preference Elicitation for Recommendation Systems


핵심 개념
Selection bias in the preference elicitation stage can negatively impact the performance of subsequent item recommendations, but existing debiasing methods can help mitigate this effect.
초록
This paper explores the problem of selection bias in the preference elicitation (PE) stage of recommendation systems. The authors note that while selection bias in user interactions (e.g., ratings) has been widely studied, the effects of bias in the PE stage have not been investigated. The authors first discuss how common debiasing methods for item recommendation, such as inverse propensity scoring (IPS), can be applied to the PE stage. They then introduce a method for simulating a PE stage from static recommendation datasets, as there is currently no publicly available dataset that represents PE interactions. Through experiments on both a semi-synthetic dataset (based on the Yahoo! R3 dataset) and a fully-synthetic dataset, the authors demonstrate that: Ignoring the effect of selection bias in the PE stage can lead to an exacerbation of overrepresentation in subsequent item recommendations. Applying debiasing methods, such as IPS, can significantly improve the performance of item recommendations by alleviating the effects of selection bias in the PE stage. The authors propose their simulation method and initial results as a starting point and motivation for future research into this important but overlooked problem setting.
통계
The rating distribution over item topics in the Coat dataset shows a clear popularity bias, with a small number of topics receiving most of the ratings. The genre popularity distribution in the MovieLens dataset also exhibits a skewed distribution, indicating the presence of selection bias.
인용구
"Selection bias in user interaction data is widely studied [12, 13, 13, 18, 19, 22], to the best of our knowledge, previous work has not considered the effects of selection bias in PE." "Our experimental results in the simulator reveal that selection bias in the PE stage does, indeed, have negative effects on subsequent item recommendation." "We find that existing debiasing methods can be adapted to reduce these effects, leading to significantly better recommendations."

더 깊은 질문

How can the proposed simulation method be extended to capture more realistic aspects of preference elicitation, such as dynamic user preferences or the interactive nature of conversational recommender systems

The proposed simulation method can be extended to capture more realistic aspects of preference elicitation by incorporating dynamic user preferences and the interactive nature of conversational recommender systems. To simulate dynamic user preferences, the simulation can be modified to include temporal dynamics, where user preferences evolve over time. This can be achieved by introducing time stamps to the interactions and updating user preferences based on historical data. Additionally, to simulate the interactive nature of conversational recommender systems, the simulation can be enhanced to include a feedback loop where recommendations are refined based on user feedback during the preference elicitation process. This feedback loop can mimic the iterative nature of conversations in conversational recommender systems, where recommendations are adjusted based on user responses.

What are the potential limitations of applying debiasing methods in the PE stage, and how can they be addressed

Applying debiasing methods in the PE stage may have potential limitations that need to be addressed. One limitation is the reliance on accurate propensity scores for debiasing, which may be challenging to estimate in practice. Biased estimation of propensity scores can lead to ineffective debiasing and potentially worsen the bias in the recommendations. To address this limitation, robust estimation techniques or alternative methods for handling bias, such as model-based approaches, can be explored. Another limitation is the assumption of independence between the observed ratings and the selection bias, which may not hold in real-world scenarios. Addressing this limitation may require more sophisticated modeling techniques that account for dependencies between observed ratings and selection bias, such as causal inference methods.

How can the insights from this work on selection bias in PE be applied to other areas of recommender systems, such as multi-stakeholder recommendation or fairness-aware recommendation

The insights from this work on selection bias in PE can be applied to other areas of recommender systems, such as multi-stakeholder recommendation or fairness-aware recommendation. In multi-stakeholder recommendation scenarios, where recommendations need to consider preferences from multiple user groups or stakeholders, understanding and mitigating selection bias in preference elicitation becomes crucial. By incorporating debiasing methods and simulation techniques similar to those proposed in this work, recommender systems can provide more equitable and inclusive recommendations that cater to diverse user preferences. Additionally, in fairness-aware recommendation settings, where ensuring fairness and mitigating bias is essential, the insights on selection bias can inform the development of fairer recommendation algorithms that account for biases introduced during preference elicitation. By addressing selection bias in the PE stage, recommender systems can strive towards more transparent and unbiased recommendation processes.
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