Visualization recommendation aims to enable rapid visual analysis of massive datasets. In real-world scenarios, it is essential to quickly gather and comprehend user preferences to cover users from diverse backgrounds, including varying skill levels and analytical tasks. Previous approaches to personalized visualization recommendations are non-interactive and rely on initial user data for new users. As a result, these models cannot effectively explore options or adapt to real-time feedback. To address this limitation, we propose an interactive personalized visualization recommendation (PVisRec) system that learns on user feedback from previous interactions. For more interactive and accurate recommendations, we propose Hier-SUCB, a contextual combinatorial semi-bandit in the PVisRec setting. Theoretically, we show an improved overall regret bound with the same rank of time but an improved rank of action space. We further demonstrate the effectiveness of Hier-SUCB through extensive experiments where it is comparable to offline methods and outperforms other bandit algorithms in the setting of visualization recommendation.

In large-scale online experimentation platforms, experimenters aim to discover the best treatment (arm) among multiple candidates. Traditional A/B testing and multi-armed bandits (MAB) algorithms are two popular designs. The former usually achieves a higher power but may hurt the customers' satisfaction when always recommending a poor arm, while the latter aims at improving the customers' experience (collecting more rewards) but faces the loss of testing power. Recently, [26] combine the advantage of A/B testing and MAB algorithms to maximize the testing power while maintaining more rewards for experiments with two-arm and Bernoulli rewards. However, in practice, the number of arms is usually larger than two and the reward type also varies. In multi-arm experiments, the required sample size to find the optimal arm blows up to guarantee a false discovery rate with the increase of arm numbers, bringing high opportunity costs to experimenters. To save the cost during the long experimental process, we propose a more efficient sequential test framework named Soptima that can work with general reward types. Inspired by the design of traditional MAB algorithms in chasing rewards and A/B testing in maximizing power, we propose an Elimination-type strategy adapted to this framework to dynamically adjust the traffic split on arms. This strategy cooperating with Soptima simultaneously maintains the advantage of the A/B testing in maximizing the testing power, the sequential test methods in saving the sample size, and the MAB algorithms in collecting rewards. The theoretical analysis gives guarantees on the Type-I, Type-II, and optimality error rates of the proposed approach. A series of experiments from both simulation and industrial historical data sets are conducted to verify the superiority of our approach compared with available baselines.

In recent years, the field of recommendation systems has witnessed significant advancements, with explainable recommendation systems gaining prominence as a crucial area of research. These systems aim to enhance user experience by providing transparent and compelling recommendations, accompanied by explanations. However, a persistent challenge lies in addressing biases that can influence the recommendations and explanations offered by these systems. Such biases often stem from a tendency to favor popular items and generate explanations that highlight their common attributes, thereby deviating from the objective of delivering personalized recommendations and explanations. While existing debiasing methods have been applied in explainable recommendation systems, they often overlook the model-generated explanations in tackling biases. Consequently, biases in model-generated explanations may persist, potentially compromising system performance and user satisfaction.

Web-based applications such as chatbots, search engines and news recommendations continue to grow in scale and complexity with the recent surge in the adoption of large language models (LLMs). Online model selection has thus garnered increasing attention due to the need to choose the best model among a diverse set while balancing task reward and exploration cost. Organizations faces decisions like whether to employ a costly API-based LLM or a locally finetuned small LLM, weighing cost against performance. Traditional selection methods often evaluate every candidate model before choosing one, which are becoming impractical given the rising costs of training and finetuning LLMs. Moreover, it is undesirable to allocate excessive resources towards exploring poor-performing models. While some recent works leverage online bandit algorithm to manage such exploration-exploitation trade-off in model selection, they tend to overlook the increasing-then-converging trend in model performances as the model is iteratively finetuned, leading to less accurate predictions and suboptimal model selections.

Re-ranking, as the final stage of the multi-stage recommender systems (MRS), aims at modeling the listwise context and the cross-item interactions between the candidate items. The objective is usually the overall utility (e.g., total clicks or revenue) of the re-ranked list, which is determined not only by the relevance, but also by the diversity of the list. However, existing methods equally promote diversity for all users and often compromise the relevance ranking. In reality, users have different diversity preferences and we should diversify the list tailored to individual users’ interests and needs. Users’ behavior history contains rich information which may be used for inferring their diversity preferences, but has rarely been explored in existing work. In this work, we propose a novel neural re-ranking with personalized diversification method (dubbed RAPID) to address the above challenge. RAPID explicitly models each user’s preference distribution over different topics by exploiting the intra- and inter-topic interactions from the user’s behavior history. The personalized diversity gain brought by each candidate item is then measured by the item’s marginal diversity and the learned personalized preference. The relevance and the personalized diversity are jointly optimized in an end-to-end manner to automatically manage the relevance-diversity tradeoff. Experimental results on two public datasets and a proprietary dataset show that RAPID outperforms the state-of-the-art with the highest utility and the best relevance-diversity tradeoff. We further prove that RAPID has a regret bound of $\tilde O(\sqrt n )$ on utility, which provides theoretical guarantee that its performance is near-optimal.

Recent conversational recommender systems (CRSs) have achieved considerable success on addressing the cold-start problem. While they utilize conversational key-terms to efficiently elicit user preferences, most of them, however, neglect that key-terms can also introduce biases. Systems learning key-term-level user preferences may make a biased item recommendation based on an overrated key-term instead of the item itself. As key-term conversation is a crucial part of CRSs, it is important to properly handle such bias resulting from the item-key-term relationship. While many debiasing methods have been proposed for traditional recommender systems, most of them focus on items or item groups re-ranking or re-weighting strategies such as calibration and propensity score, which are not designed to model the relation between item and key-term user preference. There is also no effective way for traditional debiasing methods to measure potentially useful biases through conversational key-terms to enhance the recommendation performance.

Temporal difference (TD) learning with nonlinear function approximation (nonlinear TD learning for short) permits evaluating policies using neural networks, and is a core component in modern deep reinforcement learning. Though significant advances have been made to improve its effectiveness, little attention has been paid to the data privacy faced when applying it in real applications. To mitigate the privacy concerns in practical applications of nonlinear TD learning, in this paper, we consider preserving its privacy under the notion of differential privacy (DP). This problem is challenging since nonlinear TD learning is usually studied in the formulation of stochastic nonconvex-strongly-concave optimization to obtain finite-sample analysis, which requires simultaneously preserving privacy on both primal and dual sides. To this end, we adopt a single-timescale algorithm, which optimizes both sides using learning rates of the same order, to avoid unnecessary privacy costs. Further, we achieve a good trade-off between the privacy and utility guarantees by perturbing gradients on both sides using Gaussian noises with well-calibrated variances. Consequently, our algorithm achieves rigorous (ε,δ)-DP guarantee with the utility upper bounded by ~O((d/log(1/δ))1/8 (nε)1/4) where n is the trajectory length and d is the ambient dimension of the feature space. Extensive experiments conducted in OpenAI Gym validate the advantages of our algorithm.

The problem of two-sided matching markets is well-studied in social science and economics. Some recent works study how to match while learning the unknown preferences of agents in one-to-one matching markets. However, in many cases like the online recruitment platform for short-term workers, a company can select more than one agent while an agent can only select one company at a time. These short-term workers try many times in different companies to find the most suitable jobs for them. Thus we consider a more general bandit learning problem in many-to-one matching markets where each arm has a fixed capacity and agents make choices with multiple rounds of iterations. We develop algorithms in both centralized and decentralized settings and prove regret bounds of order O(log T) and Olog2 T) respectively. Extensive experiments show the convergence and effectiveness of our algorithms.

The recent advances of conversational recommendations provide a promising way to efficiently elicit users' preferences via conversational interactions. To achieve this, the recommender system conducts conversations with users, asking their preferences for different items or item categories. Most existing conversational recommender systems for cold-start users utilize a multi-armed bandit framework to learn users' preference in an online manner. However, they rely on a pre-defined conversation frequency for asking about item categories instead of individual items, which may incur excessive conversational interactions that hurt user experience. To enable more flexible questioning about key-terms, we formulate a new conversational bandit problem that allows the recommender system to choose either a key-term or an item to recommend at each round and explicitly models the rewards of these actions. This motivates us to handle a new exploration-exploitation (EE) trade-off between key-term asking and item recommendation, which requires us to accurately model the relationship between key-term and item rewards. We conduct a survey and analyze a real-world dataset to find that, unlike assumptions made in prior works, key-term rewards are mainly affected by rewards of representative items. We propose two bandit algorithms, Hier-UCB and Hier-LinUCB, that leverage this observed relationship and the hierarchical structure between key-terms and items to efficiently learn which items to recommend. We theoretically prove that our algorithm can reduce the regret bound's dependency on the total number of items from previous work. We validate our proposed algorithms and regret bound on both synthetic and real-world data.

Interactive recommender systems (IRS) have received wide attention in recent years. To capture users' dynamic preferences and maximize their long-term engagement, IRS are usually formulated as reinforcement learning (RL) problems. Despite the promise to solve complex decision-making problems, RL-based methods generally require a large amount of online interaction, restricting their applications due to economic considerations. One possible direction to alleviate this issue is cross-domain recommendation that aims to leverage abundant logged interaction data from a source domain (e.g., adventure genre in movie recommendation) to improve the recommendation quality in the target domain (e.g., crime genre). Nevertheless, prior studies mostly focus on adapting the static representations of users/items. Few have explored how the temporally dynamic user-item interaction patterns transform across domains.

Strategic behavior against sequential learning methods, such as "click framing'' in real recommendation systems, have been widely observed. Motivated by such behavior we study the problem of combinatorial multi-armed bandits (CMAB) under strategic manipulations of rewards, where each arm can modify the emitted reward signals for its own interest. This characterization of the adversarial behavior is a relaxation of previously well-studied settings such as adversarial attacks and adversarial corruption. We propose a strategic variant of the combinatorial UCB algorithm, which has a regret of at most O(mlog T + m B_max ) under strategic manipulations, where T is the time horizon, m is the number of arms, and B_max is the maximum budget of an arm. We provide lower bounds on the budget for arms to incur certain regret of the bandit algorithm. Extensive experiments on online worker selection for crowdsourcing systems, online influence maximization and online recommendations with both synthetic and real datasets corroborate our theoretical findings on robustness and regret bounds, in a variety of regimes of manipulation budgets.

Conversational recommender systems (CRSs) have been proposed recently to mitigate the cold-start problem suffered by the traditional recommender systems. By introducing conversational key-terms, existing conversational recommenders can effectively reduce the need for extensive exploration and elicit the user preferences faster and more accurately. However, existing conversational recommenders leveraging key-terms heavily rely on the availability and quality of the key-terms, and their performances might degrade significantly when the key-terms are incomplete or not well labeled, which usually happens when there are new items being consistently incorporated into the systems and involving lots of human efforts to acquire well-labeled key-terms is costly. Besides, existing CRS methods leverage the feedback to different conversational key-terms separately, without considering the underlying relations between the key-terms. In this case, the learning of the conversational recommenders is sample inefficient, especially when there is a large number of candidate conversational key-terms.

Conversational recommender systems elicit user preference via interactive conversational interactions. By introducing conversational key-terms, existing conversational recommenders can effectively reduce the need for extensive exploration in a traditional interactive recommender. However, there are still limitations of existing conversational recommender approaches eliciting user preference via key-terms. First, the key-term data of the items needs to be carefully labeled, which requires a lot of human efforts. Second, the number of the human labeled key-terms is limited and the granularity of the key-terms is fixed, while the elicited user preference is usually from coarse-grained to fine-grained during the conversations. In this paper, we propose a clustering of conversational bandits algorithm. To avoid the human labeling efforts and automatically learn the key-terms with the proper granularity, we online cluster the items and generate meaningful key-terms for the items during the conversational interactions. Our algorithm is general and can also be used in the user clustering when the feedback from multiple users is available, which further leads to more accurate learning and generations of conversational key-terms. We analyze the regret bound of our learning algorithm. In the empirical evaluations, without using any human labeled key-terms, our algorithm effectively generates meaningful coarse-to-fine grained key-terms and performs as well as or better than the state-of-the-art baseline.

To better exploit search logs and model users' behavior patterns, numerous click models are proposed to extract users' implicit interaction feedback. Most traditional click models are based on the probabilistic graphical model (PGM) framework, which requires manually designed dependencies and may oversimplify user behaviors. Recently, methods based on neural networks are proposed to improve the prediction accuracy of user behaviors by enhancing the expressive ability and allowing flexible dependencies. However, they still suffer from the data sparsity and cold-start problems. In this paper, we propose a novel graph-enhanced click model (GraphCM) for web search. Firstly, we regard each query or document as a vertex, and propose novel homogeneous graph construction methods for queries and documents respectively, to fully exploit both intra-session and inter-session information for the sparsity and cold-start problems. Secondly, following the examination hypothesis, we separately model the attractiveness estimator and examination predictor to output the attractiveness scores and examination probabilities, where graph neural networks and neighbor interaction techniques are applied to extract the auxiliary information encoded in the pre-constructed homogeneous graphs. Finally, we apply combination functions to integrate examination probabilities and attractiveness scores into click predictions. Extensive experiments conducted on three real-world session datasets show that GraphCM not only outperforms the state-of-art models, but also achieves superior performance in addressing the data sparsity and cold-start problems.

With the recent advances of conversational recommendations, the recommender system is able to actively and dynamically elicit user preference via conversational interactions. To achieve this, the system periodically queries users' preference on attributes and collects their feedback. However, most existing conversational recommender systems only enable the user to provide absolute feedback to the attributes. In practice, the absolute feedback is usually limited, as the users tend to provide biased feedback when expressing the preference. Instead, the user is often more inclined to express comparative preferences, since user preferences are inherently relative. To enable users to provide comparative preferences during conversational interactions, we propose a novel comparison-based conversational recommender system. The relative feedback, though more practical, is not easy to be incorporated since its feedback scale is always mismatched with users' absolute preferences. With effectively collecting and understanding the relative feedback from an interactive manner, we further propose a new bandit algorithm, which we call RelativeConUCB. The experiments on both synthetic and real-world datasets validate the advantage of our proposed method, compared to the existing bandit algorithms in the conversational recommender systems.

Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback. Click models, which study how users interact with a ranked list of items, provide a useful understanding of user feedback for learning ranking models. Constructing ”right” dependencies is the key of any successful click model. However, probabilistic graphical models (PGMs) have to rely on manually assigned dependencies, and oversimplify user behaviors. Existing neural network based methods promote PGMs by enhancing the expressive ability and allowing flexible dependencies, but still suffer from exposure bias and inferior estimation. In this paper, we propose a novel framework, Adversarial Imitation Click Model (AICM), based on imitation learning. Firstly, we explicitly learn the reward function that recovers users’ intrinsic utility and underlying intentions. Secondly, we model user interactions with a ranked list as a dynamic system instead of one-step click prediction, alleviating the exposure bias problem. Finally, we minimize the JS divergence through adversarial training and learn a stable distribution of click sequences, which makes AICM generalize well across different distributions of ranked lists. A theoretical analysis has indicated that AICM reduces the exposure bias from O(T2) to O(T). Our studies on a public web search dataset show that AICM not only outperforms state-of-the-art models in traditional click metrics but also achieves superior performance in addressing the exposure bias and recovering the underlying patterns of click sequences.

Many web systems rank and present a list of items to users, from recommender systems to search and advertising. An important problem in practice is to evaluate new ranking policies offline and optimize them before they are deployed. We address this problem by proposing evaluation algorithms for estimating the expected number of clicks on ranked lists from historical logged data. The existing algorithms are not guaranteed to be statistically efficient in our problem because the number of recommended lists can grow exponentially with their length. To overcome this challenge, we use models of user interaction with the list of items, the so-called click models, to construct estimators that learn statistically efficiently. We analyze our estimators and prove that they are more efficient than the estimators that do not use the structure of the click model, under the assumption that the click model holds. We evaluate our estimators in a series of experiments on a real-world dataset and show that they consistently outperform prior estimators.