Weaknesses: 1) The experiment is somewhat inadequate. In the paper, the author only compares the proposed SIRI approach to the baseline from original Touchdown dataset paper [2]. In fact, spatial description resolution is a similar task as referring expression or instruction grounding. It is necessary for the author to further compare to approaches (such as Mattnet [18] or other new methods in 2019) in those tasks. For example, although Mattnet is not designed for spatial description resolution, but there is also semantic and position modules to handle spatial relation and object relationship reasoning, which can be served as a substitute of Part I & II of SIRI.

Paper was reviewed by four expert reviewers, with initial scores of: 6, 6, 6, 5. Reviewers acknowledge a commendable improvements of the proposed approach on a difficult and novel task. A number of issues where raised about the paper, including (1) poor exposition and language [all reviewers], (2) lack of comparison to FiLM [R1], (3) specificity of task and dataset [R2,R4], among others. Authors provided a rebuttal that was discussed by reviewers and ultimately convincing. Two of the reviewers upgraded their scores, resulting in unanimously positive, albeit marginally so, scores of: 7, 6, 6, 6. AC, despite having reservations about quality of the writing, mentioned by all reviewers, agrees that the approach is valuable and presents a significant improvement over state-of-the-art on a relatively unexplored problem. As such AC agrees with reviewers that the paper should be accepted.

The paper presents a novel method for recommendation with collaborative filtering based on Markov Random Fields (MRF). Starting from a general approach that regresses the full graph of items, the paper shows that a valid approximation can be obtained by proceeding with subgraphs that represent Markov blankets of an initial set of items. This approach yields significant computing gains, while yielding better recommendation performance compared to the state-of-the-art represented here by variational auto-encoders. As a general comment, I am wondering whether taking into account the popularity bias makes sense in the approach and if the authors thought about it. The claims are well supported by theoretical analysis.

Reviewers were initially quite favorable with respect to this paper and your response lifted some remaining doubts (especially from Reviewer #1). I am happy to recommend acceptance, congratulations! I would recommend that you take the reviewer comments into account to prepare a camera-ready version. In particular, it seems to be important to incorporate some of the discussion in bullets 1 and 2 in your response (regarding Mult-VAE and the high-level summary or pseudocode).

Mere moments after his swearing in Monday, President Donald Trump made a proclamation to attendees of his inauguration: "It shall henceforth be the policy of the United States government that there are only two genders: male and female." Trump then signed an executive order disparaging what the White House called "gender ideology" and claiming that a person's sex is "not changeable and [is] grounded in fundamental and incontrovertible reality." Trump's order, which was widely seen as an unscientific attempt to roll back the rights of transgender and gender-expansive people, also instructs federal agencies "to require that government-issued identification documents, including passports, visas, and Global Entry cards, accurately reflect the holder's sex," rather than their gender identity. It was one of 78 orders signed on Monday, some of which were part of Trump's attempts to end Biden-era policies that "socially engineer race and gender into every aspect of public and private life." While the executive order only affects federal policy, the broader implications are vast.

Keeping ML-based recommender models up-to-date as data drifts and evolves is essential to maintain accuracy. As a result, online data preprocessing plays an increasingly important role in serving recommender systems. Existing solutions employ multiple CPU workers to saturate the input bandwidth of a single training node. Such an approach results in high deployment costs and energy consumption. For instance, a recent report from industrial deployments shows that data storage and ingestion pipelines can account for over 60\% of the power consumption in a recommender system. In this paper, we tackle the issue from a hardware perspective by introducing Piper, a flexible and network-attached accelerator that executes data loading and preprocessing pipelines in a streaming fashion. As part of the design, we define MiniPipe, the smallest pipeline unit enabling multi-pipeline implementation by executing various data preprocessing tasks across the single board, giving Piper the ability to be reconfigured at runtime. Our results, using publicly released commercial pipelines, show that Piper, prototyped on a power-efficient FPGA, achieves a 39$\sim$105$\times$ speedup over a server-grade, 128-core CPU and 3$\sim$17$\times$ speedup over GPUs like RTX 3090 and A100 in multiple pipelines. The experimental analysis demonstrates that Piper provides advantages in both latency and energy efficiency for preprocessing tasks in recommender systems, providing an alternative design point for systems that today are in very high demand.

We present a novel approach for recommending actionable strategies by integrating strategic frameworks with decision heuristics through semantic analysis. While strategy frameworks provide systematic models for assessment and planning, and decision heuristics encode experiential knowledge,these traditions have historically remained separate. Our methodology bridges this gap using advanced natural language processing (NLP), demonstrated through integrating frameworks like the 6C model with the Thirty-Six Stratagems. The approach employs vector space representations and semantic similarity calculations to map framework parameters to heuristic patterns, supported by a computational architecture that combines deep semantic processing with constrained use of Large Language Models. By processing both primary content and secondary elements (diagrams, matrices) as complementary linguistic representations, we demonstrate effectiveness through corporate strategy case studies. The methodology generalizes to various analytical frameworks and heuristic sets, culminating in a plug-and-play architecture for generating recommender systems that enable cohesive integration of strategic frameworks and decision heuristics into actionable guidance.

In reality, users have different interests in different periods, regions, scenes, etc. Such changes in interest are so drastic that they are difficult to be captured by recommenders. Existing multi-domain learning can alleviate this problem. However, the structure of the industrial recommendation system is complex, the amount of data is huge, and the training cost is extremely high, so it is difficult to modify the structure of the industrial recommender and re-train it. To fill this gap, we consider recommenders as large pre-trained models and fine-tune them. We first propose the theory of the information bottleneck for fine-tuning and present an explanation for the fine-tuning technique in recommenders. To tailor for recommendation, we design an information-aware adaptive kernel (IAK) technique to fine-tune the pre-trained recommender. Specifically, we define fine-tuning as two phases: knowledge compression and knowledge matching and let the training stage of IAK explicitly approximate these two phases. Our proposed approach designed from the essence of fine-tuning is well interpretable. Extensive online and offline experiments show the superiority of our proposed method. Besides, we also share unique and important lessons we learned when deploying the method in a large-scale online platform. We also present the potential issues of fine-tuning techniques in recommendation systems and the corresponding solutions. The recommender with IAK technique has been deployed on the homepage of a billion-scale online food platform for several months and has yielded considerable profits in our business.

Recommender systems are pivotal in delivering personalised user experiences across various domains. However, capturing the heterophily patterns and the multi-dimensional nature of user-item interactions poses significant challenges. To address this, we introduce FWHDNN (Fusion-based Wavelet Hypergraph Diffusion Neural Networks), an innovative framework aimed at advancing representation learning in hypergraph-based recommendation tasks. The model incorporates three key components: (1) a cross-difference relation encoder leveraging heterophily-aware hypergraph diffusion to adapt message-passing for diverse class labels, (2) a multi-level cluster-wise encoder employing wavelet transform-based hypergraph neural network layers to capture multi-scale topological relationships, and (3) an integrated multi-modal fusion mechanism that combines structural and textual information through intermediate and late-fusion strategies. Extensive experiments on real-world datasets demonstrate that FWHDNN surpasses state-of-the-art methods in accuracy, robustness, and scalability in capturing high-order interconnections between users and items.

Personalised education is one of the domains that can greatly benefit from the most recent advances in Artificial Intelligence (AI) and Large Language Models (LLM). However, it is also one of the most challenging applications due to the cognitive complexity of teaching effectively while personalising the learning experience to suit independent learners. We hypothesise that one promising approach to excelling in such demanding use cases is using a \emph{society of minds}. In this chapter, we present TrueReason, an exemplar personalised learning system that integrates a multitude of specialised AI models that can mimic micro skills that are composed together by a LLM to operationalise planning and reasoning. The architecture of the initial prototype is presented while describing two micro skills that have been incorporated in the prototype. The proposed system demonstrates the first step in building sophisticated AI systems that can take up very complex cognitive tasks that are demanded by domains such as education.