Metamorphic Evaluation of ChatGPT as a Recommender System

With the rise of Large Language Models (LLMs) such as ChatGPT, researchers have been working on how to utilize the LLMs for better recommendations. However, although LLMs exhibit black-box and probabilistic characteristics (meaning their internal working is not visible), the evaluation framework used for assessing these LLM-based recommender systems (RS) are the same as those used for traditional recommender systems. To address this gap, we introduce the metamorphic testing for the evaluation of GPT-based RS. This testing technique involves defining of metamorphic relations (MRs) between the inputs and checking if the relationship has been satisfied in the outputs. Specifically, we examined the MRs from both RS and LLMs perspectives, including rating multiplication/shifting in RS and adding spaces/randomness in the LLMs prompt via prompt perturbation. Similarity metrics (e.g. Kendall tau and Ranking Biased Overlap(RBO)) are deployed to measure whether the relationship has been satisfied in the outputs of MRs. The experiment results on MovieLens dataset with GPT3.5 show that lower similarity are obtained in terms of Kendall $\tau$ and RBO, which concludes that there is a need of a comprehensive evaluation of the LLM-based RS in addition to the existing evaluation metrics used for traditional recommender systems.

Performance-Driven QUBO for Recommender Systems on Quantum Annealers

We propose Counterfactual Analysis Quadratic Unconstrained Binary Optimization (CAQUBO) to solve QUBO problems for feature selection in recommender systems. CAQUBO leverages counterfactual analysis to measure the impact of individual features and feature combinations on model performance and employs the measurements to construct the coefficient matrix for a quantum annealer to select the optimal feature combinations for recommender systems, thereby improving their final recommendation performance. By establishing explicit connections between features and the recommendation performance, the proposed approach demonstrates superior performance compared to the state-of-the-art quantum annealing methods. Extensive experiments indicate that integrating quantum computing with counterfactual analysis holds great promise for addressing these challenges.

Perfect Counterfactuals in Imperfect Worlds: Modelling Noisy Implementation of Actions in Sequential Algorithmic Recourse

Algorithmic recourse provides actions to individuals who have been adversely affected by automated decision-making and helps them achieve a desired outcome. Knowing the recourse, however, does not guarantee that users would implement it perfectly, either due to environmental variability or personal choices. Recourse generation should thus anticipate its sub-optimal or noisy implementation. While several approaches have constructed recourse that accounts for robustness to small perturbation (i.e., noisy recourse implementation), they assume an entire recourse to be implemented in a single step and thus apply one-off uniform noise to it. Such assumption is unrealistic since recourse often includes multiple sequential steps which becomes harder to implement and subject to more noise. In this work, we consider recourse under plausible noise that adapts to the local data geometry and accumulates at every step of the way. We frame this problem as a Markov Decision Process and demonstrate that the distribution of our plausible noise satisfies the Markov property. We then propose the RObust SEquential (ROSE) recourse generator to output a sequence of steps that will lead to the desired outcome even under imperfect implementation. Given our plausible modelling of sub-optimal human actions and greater recourse robustness to accumulated uncertainty, ROSE can grant users higher chances of success under low recourse costs. Empirical evaluation shows our algorithm manages the inherent trade-off between recourse robustness and costs more effectively while ensuring its low sparsity and fast computation.

Generative Information Retrieval Evaluation

In this chapter, we consider generative information retrieval evaluation from two distinct but interrelated perspectives. First, large language models (LLMs) themselves are rapidly becoming tools for evaluation, with current research indicating that LLMs may be superior to crowdsource workers and other paid assessors on basic relevance judgement tasks. We review past and ongoing related research, including speculation on the future of shared task initiatives, such as TREC, and a discussion on the continuing need for human assessments. Second, we consider the evaluation of emerging LLM-based generative information retrieval (GenIR) systems, including retrieval augmented generation (RAG) systems. We consider approaches that focus both on the end-to-end evaluation of GenIR systems and on the evaluation of a retrieval component as an element in a RAG system. Going forward, we expect the evaluation of GenIR systems to be at least partially based on LLM-based assessment, creating an apparent circularity, with a system seemingly evaluating its own output. We resolve this apparent circularity in two ways: 1) by viewing LLM-based assessment as a form of "slow search", where a slower IR system is used for evaluation and training of a faster production IR system; and 2) by recognizing a continuing need to ground evaluation in human assessment, even if the characteristics of that human assessment must change.

Online and Offline Evaluation in Search Clarification

The effectiveness of clarification question models in engaging users within search systems is currently constrained, casting doubt on their overall usefulness. To improve the performance of these models, it is crucial to employ assessment approaches that encompass both real-time feedback from users (online evaluation) and the characteristics of clarification questions evaluated through human assessment (offline evaluation). However, the relationship between online and offline evaluations has been debated in information retrieval. This study aims to investigate how this discordance holds in search clarification. We use user engagement as ground truth and employ several offline labels to investigate to what extent the offline ranked lists of clarification resemble the ideal ranked lists based on online user engagement.

i-Align: an interpretable knowledge graph alignment model

Knowledge graphs (KGs) are becoming essential resources for many downstream applications. However, their incompleteness may limit their potential. Thus, continuous curation is needed to mitigate this problem. One of the strategies to address this problem is KG alignment, i.e., forming a more complete KG by merging two or more KGs. This paper proposes i-Align, an interpretable KG alignment model. Unlike the existing KG alignment models, i-Align provides an explanation for each alignment prediction while maintaining high alignment performance. Experts can use the explanation to check the correctness of the alignment prediction. Thus, the high quality of a KG can be maintained during the curation process (e.g., the merging process of two KGs). To this end, a novel Transformer-based Graph Encoder (Trans-GE) is proposed as a key component of i-Align for aggregating information from entities' neighbors (structures). Trans-GE uses Edge-gated Attention that combines the adjacency matrix and the self-attention matrix to learn a gating mechanism to control the information aggregation from the neighboring entities. It also uses historical embeddings, allowing Trans-GE to be trained over mini-batches, or smaller sub-graphs, to address the scalability issue when encoding a large KG. Another component of i-Align is a Transformer encoder for aggregating entities' attributes. This way, i-Align can generate explanations in the form of a set of the most influential attributes/neighbors based on attention weights. Extensive experiments are conducted to show the power of i-Align. The experiments include several aspects, such as the model's effectiveness for aligning KGs, the quality of the generated explanations, and its practicality for aligning large KGs. The results show the effectiveness of i-Align in these aspects.

Designing and Evaluating Presentation Strategies for Fact-Checked Content

With the rapid growth of online misinformation, it is crucial to have reliable fact-checking methods. Recent research on finding check-worthy claims and automated fact-checking have made significant advancements. However, limited guidance exists regarding the presentation of fact-checked content to effectively convey verified information to users. We address this research gap by exploring the critical design elements in fact-checking reports and investigating whether credibility and presentation-based design improvements can enhance users' ability to interpret the report accurately. We co-developed potential content presentation strategies through a workshop involving fact-checking professionals, communication experts, and researchers. The workshop examined the significance and utility of elements such as veracity indicators and explored the feasibility of incorporating interactive components for enhanced information disclosure. Building on the workshop outcomes, we conducted an online experiment involving 76 crowd workers to assess the efficacy of different design strategies. The results indicate that proposed strategies significantly improve users' ability to accurately interpret the verdict of fact-checking articles. Our findings underscore the critical role of effective presentation of fact reports in addressing the spread of misinformation. By adopting appropriate design enhancements, the effectiveness of fact-checking reports can be maximized, enabling users to make informed judgments.

More Is Less: When Do Recommenders Underperform for Data-rich Users?

Users of recommender systems tend to differ in their level of interaction with these algorithms, which may affect the quality of recommendations they receive and lead to undesirable performance disparity. In this paper we investigate under what conditions the performance for data-rich and data-poor users diverges for a collection of popular evaluation metrics applied to ten benchmark datasets. We find that Precision is consistently higher for data-rich users across all the datasets; Mean Average Precision is comparable across user groups but its variance is large; Recall yields a counter-intuitive result where the algorithm performs better for data-poor than for data-rich users, which bias is further exacerbated when negative item sampling is employed during evaluation. The final observation suggests that as users interact more with recommender systems, the quality of recommendations they receive degrades (when measured by Recall). Our insights clearly show the importance of an evaluation protocol and its influence on the reported results when studying recommender systems.

MIMICS-Duo: Offline & Online Evaluation of Search Clarification

Asking clarification questions is an active area of research; however, resources for training and evaluating search clarification methods are not sufficient. To address this issue, we describe MIMICS-Duo, a new freely available dataset of 306 search queries with multiple clarifications (a total of 1,034 query-clarification pairs). MIMICS-Duo contains fine-grained annotations on clarification questions and their candidate answers and enhances the existing MIMICS datasets by enabling multi-dimensional evaluation of search clarification methods, including online and offline evaluation. We conduct extensive analysis to demonstrate the relationship between offline and online search clarification datasets and outline several research directions enabled by MIMICS-Duo. We believe that this resource will help researchers better understand clarification in search.

Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor Behaviors

This paper investigates the Cyber-Physical behavior of users in a large indoor shopping mall by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the mall operators. Our analysis shows that many users exhibit a high correlation between their cyber activities and their physical context. To find this correlation, we propose a mechanism to semantically label a physical space with rich categorical information from DBPedia concepts and compute a contextual similarity that represents a user's activities with the mall context. We demonstrate the application of cyber-physical contextual similarity in two situations: user visit intent classification and future location prediction. The experimental results demonstrate that exploitation of contextual similarity significantly improves the accuracy of such applications.