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Jun 13, 2024, 12:42:48 AM6/13/24

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Full Citation in the ACM Digital LibrarySESSION: Keynote TalksGenerative Information RetrievalMarc NajorkHistorically, information retrieval systems have all followed the same paradigm: information seekers frame their needs in the form of a short query, the system selects a small set of relevant results from a corpus of available documents, rank-orders the results by decreasing relevance, possibly excerpts a responsive passage for each result, and returns a list of references and excerpts to the user. Retrieval systems typically did not attempt fusing information from multiple documents into an answer and displaying that answer directly. This was largely due to available technology: at the core of each retrieval system is an index that maps lexical tokens or semantic embeddings to document identifiers. Indices are designed for retrieving responsive documents; they do not support integrating these documents into a holistic answer.

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The keynote will summarize the short history of these generative information retrieval systems, and focus on the many open challenges in this emerging field: ensuring that answers are grounded, attributing answer passages to a primary source, providing nuanced answers to non-factoid-seeking questions, avoiding bias, and going beyond simple regurgitation of memorized facts. It will also touch on the changing nature of the content ecosystem. LLMs are starting to be used to generate web content. Should search engines treat such derived content equal to human-authored content? Is it possible to distinguish generated from original content? How should we view hybrid authorship where humans contribute ideas and LLMs shape these ideas into prose? And how will this parallel technical evolution of search engines and content ecosystems affect their respective business models?

The rough use of machine learning methods is common and sometimes unavoidable. The reason is that nothing is called a perfect use of a machine learning method. Further, it is not easy to assess the seriousness of the situation. We argue that having high-quality and easy-to-use software is an important way to improve the practical use of machine learning techniques.

Such a platform could monitor conversion funnels, identify anomalous behaviors, intercept live users exhibiting those behaviors, and solicit explicit feedback in situ. This feedback could take many forms: survey responses, screen recordings of participants performing tasks, think-aloud audio, and more. By combining data from multiple users and correlating across feedback types, the platform could surface not just insights that a particular conversion funnel had been affected, but hypotheses about what had caused the change in user behavior. The platform could then rank these insights by how often the observed behavior occurred in the wild, using large-scale analytics to contextualize the results from small-scale UX tests.

To this end, a decade of research has focused on interaction mining: a set of techniques for capturing interaction and design data from digital artifacts, and aggregating these multimodal data streams into structured representations bridging quantitative and qualitative experience testing[1-4]. During user sessions, interaction mining systems record user interactions (e.g., clicks, scrolls, text input), screen captures, and render-time data structures (e.g., website DOMs, native app view hierarchies). Once captured, these data streams are aligned and combined into user traces, sequences of user interactions semanticized by the design data of their UI targets [5]. The structure of these traces affords new workflows for composing quantitative and qualitative methods, building toward a unified platform for optimizing digital experiences.

Tasks are central to information retrieval (IR) and drive interactions with search systems [2, 4, 10]. Understanding and modeling tasks helps these systems better support user needs [8, 9, 11]. This keynote focuses on search tasks, the emergence of generative artificial intelligence (AI), and the implications of recent work at their intersection for the future of search. Recent estimates suggest that half of Web search queries go unanswered, many of them connected to complex search tasks that are ill-defined or multi-step and span several queries[6]. AI copilots, e.g., ChatGPT and Bing Chat, are emerging to address complex search tasks and many other challenges. These copilots are built on large foundation models such as GPT-4 and are being extended with skills and plugins. Copilots broaden the surface of tasks achievable via search, moving toward creation not just finding (e.g., interview preparation, email composition), and can make searchers more efficient and more successful.

Users currently engage with AI copilots via natural language queries and dialog and the copilots generate answers with source attribution [7]. However, in delegating responsibility for answer generation, searchers also lose some control over aspects of the search process, such as directly manipulating queries and examining lists of search results [1]. The efficiency gains from auto-generating a single, synthesized answer may also reduce opportunities for user learning and serendipity. A wholesale move to copilots for all search tasks is neither practical nor necessary: model inference is expensive, conversational interfaces are unfamiliar to many users in a search context, and traditional search already excels for many types of task. Instead, experiences that unite search and chat are becoming more common, enabling users to adjust the modality and other aspects (e.g., answer tone) based on the task.

The rise of AI copilots creates many opportunities for IR, including aligning generated answers with user intent, tasks, and applications via human feedback [3]; understanding copilot usage, including functional fixedness [5]; using context and data to tailor responses to people and situations (e.g., grounding, personalization); new search experiences (e.g., unifying search and chat); reliability and safety (e.g., accuracy, bias); understanding impacts on user learning and agency; and evaluation (e.g., model-based feedback, searcher simulations [12] repeatability). Research in these and related areas will enable search systems to more effectively utilize new copilot technologies together with traditional search to help searchers better tackle a wider variety of tasks.

The graph neural network-based collaborative filtering (CF) models user-item interactions as a bipartite graph and performs iterative aggregation to enhance performance. Unfortunately, the aggregation process may amplify the popularity bias, which impedes user engagement with niche (unpopular) items. While some efforts have studied the popularity bias in CF, they often focus on modifying loss functions, which can not fully address the popularity bias in GNN-based CF models. This is because the debiasing loss can be falsely backpropagated to non-target nodes during the backward pass of the aggregation.

In this work, we study whether we can fundamentally neutralize the popularity bias in the aggregation process of GNN-based CF models. This is challenging because 1) estimating the effect of popularity is difficult due to the varied popularity caused by the aggregation from high-order neighbors, and 2) it is hard to train learnable popularity debiasing aggregation functions because of data sparsity. To this end, we theoretically analyze the cause of popularity bias and propose a quantitative metric, named inverse popularity score, to measure the effect of popularity in the representation space. Based on it, a novel graph aggregator named APDA is proposed to learn per-edge weight to neutralize popularity bias in aggregation. We further strengthen the debiasing effect with a weight scaling mechanism and residual connections. We apply APDA to two backbones and conduct extensive experiments on three real-world datasets. The results show that APDA significantly outperforms the state-of-the-art baselines in terms of recommendation performance and popularity debiasing.

User interaction data is an important source of supervision in counterfactual learning to rank (CLTR). Such data suffers from presentation bias. Much work in unbiased learning to rank (ULTR) focuses on position bias, i.e., items at higher ranks are more likely to be examined and clicked. Inter-item dependencies also influence examination probabilities, with outlier items in a ranking as an important example. They are defined as items that observably deviate from the rest and therefore stand out in the ranking. In this paper, we identify and introduce the bias brought about by outlier items: users tend to click more on outlier items and their close neighbors.

The issue of fairness in recommendation systems has recently become a matter of growing concern for both the academic and industrial sectors due to the potential for bias in machine learning models. One such bias is that of feedback loops, where the collection of data from an unfair online system hinders the accurate evaluation of the relevance scores between users and items. Given that recommendation systems often recommend popular content and vendors, the underlying relevance scores between users and items may not be accurately represented in the training data. Hence, this creates a feedback loop in which the user is not longer recommended based on their true relevance score but instead based on biased training data. To address this problem of feedback loops, we propose a two-stage representation learning framework, B-FAIR, aimed at rectifying the unfairness caused by biased historical data in recommendation systems. The framework disentangles the context data into sensitive and non-sensitive components using a variational autoencoder and then applies a novel Balanced Fairness Objective (BFO) to remove bias in the observational data when training a recommendation model. The efficacy of B-FAIR is demonstrated through experiments on both synthetic and real-world benchmarks, showing improved performance over state-of-the-art algorithms.