People break up, miscarry, and lose loved ones. Their online streaming and shopping recommendations, however, do not necessarily update, and may serve as unhappy reminders of their loss. When users want to renege on their past actions, they expect the recommender platforms to erase selective data at the model level. Ideally, given any specified user history, the recommender can unwind or "forget", as if the record was not part of training. To that end, this paper focuses on simple but widely deployed bi-linear models for recommendations based on matrix completion. Without incurring the cost of re-training, and without degrading the model unnecessarily, we develop Unlearn-ALS by making a few key modifications to the fine-tuning procedure under Alternating Least Squares optimisation, thus applicable to any bi-linear models regardless of the training procedure. We show that Unlearn-ALS is consistent with retraining without \emph{any} model degradation and exhibits rapid convergence, making it suitable for a large class of existing recommenders.

With the remarkable increase in the number of scientific entities such as publications, researchers, and scientific topics, and the associated information overload in science, academic recommender systems have become increasingly important for millions of researchers and science enthusiasts. However, it is often overlooked that these systems are subject to various biases. In this article, we first break down the biases of academic recommender systems and characterize them according to their impact and prevalence. In doing so, we distinguish between biases originally caused by humans and biases induced by the recommender system. Second, we provide an overview of methods that have been used to mitigate these biases in the scholarly domain. Based on this, third, we present a framework that can be used by researchers and developers to mitigate biases in scholarly recommender systems and to evaluate recommender systems fairly. Finally, we discuss open challenges and possible research directions related to scholarly biases.

We investigate non-modular function maximization in an online setting with $m$ users. The optimizer maintains a set $S_q$ for each user $q \in \{1, \ldots, m\}$. At round $i$, a user with unknown utility $h_q$ arrives; the optimizer selects a new item to add to $S_q$, and receives a noisy marginal gain. The goal is to minimize regret compared to an $\alpha$-approximation to the optimal full-knowledge selection (i.e., $\alpha$-regret). Prior works study this problem under a submodularity assumption for all $h_q$. However, this is not ideally amenable to applications, e.g., movie recommendations, that involve complementarity between items, where e.g., watching the first movie in a series enhances the impression of watching the sequels. Hence, we consider objectives $h_q$, called \textit{BP functions}, that decompose into the sum of monotone submodular $f_q$ and supermodular $g_q$; here, $g_q$ naturally models complementarity. Under different feedback assumptions, we develop UCB-style algorithms that use Nystrom sampling for computational efficiency. For these, we provide sublinear $\alpha$-regret guarantees for $\alpha = 1/\kappa_{f} [1 - e^{-(1 - \kappa^g) \kappa_{f}} ]$, and $\alpha = \min\{1 - \kappa_f/e, 1 - \kappa^g\}$; here, $\kappa_f, \kappa^g$ are submodular and supermodular curvatures. Furthermore, we provide similar $\alpha$-regret guarantees for functions that are almost submodular where $\alpha$ is parameterized by the submodularity ratio of the objective functions. We numerically validate our algorithms for movie recommendation on the MovieLens dataset and selection of training subsets for classification tasks.

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Artificial intelligence (AI) is rapidly transforming the marketing field, offering businesses new ways to personalize their messaging, analyze customer data, and create more effective marketing campaigns. By using machine learning algorithms and predictive analytics, companies can better understand customer behaviors, preferences, and needs, and tailor their marketing efforts accordingly. In this blog post, we'll explore some real-world examples of how businesses are using AI to improve their marketing efforts. Whether you're a small business owner or a marketing professional, understanding how AI is being used in marketing can help you stay ahead of the curve and make more informed decisions about your marketing strategy. So let's dive in and explore some of the most compelling case studies of AI in marketing.

Apple has conquered the world of audio several times over. First with the iPod and iTunes, then with its ubiquitous AirPods - all of which changed the way the world listened to music and made phone calls. But the one device that failed to shift the dial was the HomePod, a voice-controlled Siri smart speaker that launched in 2018 and was discontinued in 2021 after lackluster sales. The smaller HomePod Mini remains on sale. But it never held a candle to the larger device, which sounded better than just about any rival when it launched in 2018, even if it lacked some of the'smart' features offered by rivals from Google and Amazon.

ChatGPT, a dating platform, recently started using AI software to test a new category of match questions. However, 47% of users were unsure whether they'd continue dating someone who admitted to using AI technology to first communicate. The online dating platform is experimenting with the AI technology to roll out a new category of match questions. The six questions ChatGPT yielded have already been answered by over 135,000 users.

For domains in which a recommender provides repeated content suggestions, agent preferences may evolve over time as a function of prior recommendations, and algorithms must take this into account for long-run optimization. Recently, Agarwal and Brown (2022) introduced a model for studying recommendations when agents' preferences are adaptive, and gave a series of results for the case when agent preferences depend {\it uniformly} on their history of past selections. Here, the recommender shows a $k$-item menu (out of $n$) to the agent at each round, who selects one of the $k$ items via their history-dependent {\it preference model}, yielding a per-item adversarial reward for the recommender. We expand this setting to {\it non-uniform} preferences, and give a series of results for {\it $\gamma$-discounted} histories. For this problem, the feasible regret benchmarks can depend drastically on varying conditions. In the ``large $\gamma$'' regime, we show that the previously considered benchmark, the ``EIRD set'', is attainable for any {\it smooth} model, relaxing the ``local learnability'' requirement from the uniform memory case. We introduce ``pseudo-increasing'' preference models, for which we give an algorithm which can compete against any item distribution with small uniform noise (the ``smoothed simplex''). We show NP-hardness results for larger regret benchmarks in each case. We give another algorithm for pseudo-increasing models (under a restriction on the adversarial nature of the reward functions), which works for any $\gamma$ and is faster when $\gamma$ is sufficiently small, and we show a super-polynomial regret lower bound with respect to EIRD for general models in the ``small $\gamma$'' regime. We conclude with a pair of algorithms for the memoryless case.

Recently, the generality of natural language text has been leveraged to develop transferable recommender systems. The basic idea is to employ pre-trained language models~(PLM) to encode item text into item representations. Despite the promising transferability, the binding between item text and item representations might be too tight, leading to potential problems such as over-emphasizing the effect of text features and exaggerating the negative impact of domain gap. To address this issue, this paper proposes VQ-Rec, a novel approach to learning Vector-Quantized item representations for transferable sequential Recommenders. The main novelty of our approach lies in the new item representation scheme: it first maps item text into a vector of discrete indices (called item code), and then employs these indices to lookup the code embedding table for deriving item representations. Such a scheme can be denoted as "text $\Longrightarrow$ code $\Longrightarrow$ representation". Based on this representation scheme, we further propose an enhanced contrastive pre-training approach, using semi-synthetic and mixed-domain code representations as hard negatives. Furthermore, we design a new cross-domain fine-tuning method based on a differentiable permutation-based network. Extensive experiments conducted on six public benchmarks demonstrate the effectiveness of the proposed approach, in both cross-domain and cross-platform settings. Code and pre-trained model are available at: https://github.com/RUCAIBox/VQ-Rec.

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