Path reasoning methods over knowledge graphs have gained popularity for their potential to improve transparency in recommender systems. However, the resulting models still rely on pre-trained knowledge graph embeddings, fail to fully exploit the interdependence between entities and relations in the KG for recommendation, and may generate inaccurate explanations. In this paper, we introduce PEARLM, a novel approach that efficiently captures user behaviour and product-side knowledge through language modelling. With our approach, knowledge graph embeddings are directly learned from paths over the KG by the language model, which also unifies entities and relations in the same optimisation space. Constraints on the sequence decoding additionally guarantee path faithfulness with respect to the KG. Experiments on two datasets show the effectiveness of our approach compared to state-of-the-art baselines. Source code and datasets: AVAILABLE AFTER GETTING ACCEPTED.

Input features play a crucial role in DNN-based recommender systems with thousands of categorical and continuous fields from users, items, contexts, and interactions. Noisy features and inappropriate embedding dimension assignments can deteriorate the performance of recommender systems and introduce unnecessary complexity in model training and online serving. Optimizing the input configuration of DNN models, including feature selection and embedding dimension assignment, has become one of the essential topics in feature engineering. However, in existing industrial practices, feature selection and dimension search are optimized sequentially, i.e., feature selection is performed first, followed by dimension search to determine the optimal dimension size for each selected feature. Such a sequential optimization mechanism increases training costs and risks generating suboptimal input configurations. To address this problem, we propose a differentiable neural input razor (i-Razor) that enables joint optimization of feature selection and dimension search. Concretely, we introduce an end-to-end differentiable model to learn the relative importance of different embedding regions of each feature. Furthermore, a flexible pruning algorithm is proposed to achieve feature filtering and dimension derivation simultaneously. Extensive experiments on two large-scale public datasets in the Click-Through-Rate (CTR) prediction task demonstrate the efficacy and superiority of i-Razor in balancing model complexity and performance.

The Recommender system is a vital information service on today's Internet. Recently, graph neural networks have emerged as the leading approach for recommender systems. We try to review recent literature on graph neural network-based recommender systems, covering the background and development of both recommender systems and graph neural networks. Then categorizing recommender systems by their settings and graph neural networks by spectral and spatial models, we explore the motivation behind incorporating graph neural networks into recommender systems. We also analyze challenges and open problems in graph construction, embedding propagation and aggregation, and computation efficiency. This guides us to better explore the future directions and developments in this domain.

Multimodal recommendation aims to model user and item representations comprehensively with the involvement of multimedia content for effective recommendations. Existing research has shown that it is beneficial for recommendation performance to combine (user- and item-) ID embeddings with multimodal salient features, indicating the value of IDs. However, there is a lack of a thorough analysis of the ID embeddings in terms of feature semantics in the literature. In this paper, we revisit the value of ID embeddings for multimodal recommendation and conduct a thorough study regarding its semantics, which we recognize as subtle features of content and structures. Then, we propose a novel recommendation model by incorporating ID embeddings to enhance the semantic features of both content and structures. Specifically, we put forward a hierarchical attention mechanism to incorporate ID embeddings in modality fusing, coupled with contrastive learning, to enhance content representations. Meanwhile, we propose a lightweight graph convolutional network for each modality to amalgamate neighborhood and ID embeddings for improving structural representations. Finally, the content and structure representations are combined to form the ultimate item embedding for recommendation. Extensive experiments on three real-world datasets (Baby, Sports, and Clothing) demonstrate the superiority of our method over state-of-the-art multimodal recommendation methods and the effectiveness of fine-grained ID embeddings.

Citation recommendation is the task of finding appropriate citations based on a given piece of text. The proposed datasets for this task consist mainly of several scientific fields, lacking some core ones, such as law. Furthermore, citation recommendation is used within the legal domain to identify supporting arguments, utilizing non-scholarly legal articles. In order to alleviate the limitations of existing studies, we gather the first scholarly legal dataset for the task of citation recommendation. Also, we conduct experiments with state-of-the-art models and compare their performance on this dataset. The study suggests that, while BM25 is a strong benchmark for the legal citation recommendation task, the most effective method involves implementing a two-step process that entails pre-fetching with BM25+, followed by re-ranking with SciNCL, which enhances the performance of the baseline from 0.26 to 0.30 MAP@10. Moreover, fine-tuning leads to considerable performance increases in pre-trained models, which shows the importance of including legal articles in the training data of these models.

Learning feature interaction is the critical backbone to building recommender systems. In web-scale applications, learning feature interaction is extremely challenging due to the sparse and large input feature space; meanwhile, manually crafting effective feature interactions is infeasible because of the exponential solution space. We propose to leverage a Transformer-based architecture with attention layers to automatically capture feature interactions. Transformer architectures have witnessed great success in many domains, such as natural language processing and computer vision. However, there has not been much adoption of Transformer architecture for feature interaction modeling in industry. We aim at closing the gap. We identify two key challenges for applying the vanilla Transformer architecture to web-scale recommender systems: (1) Transformer architecture fails to capture the heterogeneous feature interactions in the self-attention layer; (2) The serving latency of Transformer architecture might be too high to be deployed in web-scale recommender systems. We first propose a heterogeneous self-attention layer, which is a simple yet effective modification to the self-attention layer in Transformer, to take into account the heterogeneity of feature interactions. We then introduce \textsc{Hiformer} (\textbf{H}eterogeneous \textbf{I}nteraction Trans\textbf{former}) to further improve the model expressiveness. With low-rank approximation and model pruning, \hiformer enjoys fast inference for online deployment. Extensive offline experiment results corroborates the effectiveness and efficiency of the \textsc{Hiformer} model. We have successfully deployed the \textsc{Hiformer} model to a real world large scale App ranking model at Google Play, with significant improvement in key engagement metrics (up to +2.66\%).

Item response theory (IRT) is the study of how people make probabilistic decisions, with diverse applications in education testing, recommendation systems, among others. The Rasch model of binary response data, one of the most fundamental models in IRT, remains an active area of research with important practical significance. Recently, Nguyen and Zhang (2022) proposed a new spectral estimation algorithm that is efficient and accurate. In this work, we extend their results in two important ways. Firstly, we obtain a refined entrywise error bound for the spectral algorithm, complementing the `average error' $\ell_2$ bound in their work. Notably, under mild sampling conditions, the spectral algorithm achieves the minimax optimal error bound (modulo a log factor). Building on the refined analysis, we also show that the spectral algorithm enjoys optimal sample complexity for top-$K$ recovery (e.g., identifying the best $K$ items from approval/disapproval response data), explaining the empirical findings in the previous work. Our second contribution addresses an important but understudied topic in IRT: privacy. Despite the human-centric applications of IRT, there has not been any proposed privacy-preserving mechanism in the literature. We develop a private extension of the spectral algorithm, leveraging its unique Markov chain formulation and the discrete Gaussian mechanism (Canonne et al., 2020). Experiments show that our approach is significantly more accurate than the baselines in the low-to-moderate privacy regime.

Recommendation systems (RSs) are the principal ingredient of many online services and platforms like Youtube, Quora, Substack, and Medium. Algorithmicall y, those platforms treat the task of recommendation as a matching problem. RSs match a user's con text, i.e., their past interactions, demographics, etc., to an item from a predetermined list of i tems, e.g., news articles, which will hopefully satisfy that user. The quality of a user-content m atch is initially unclear, so many data-driven approaches have been proposed to determine a matchin g's quality; for instance, collaborate filtering [ 23 ], matrix completion [ 37 ], and online learning [ 7 ]. However, due to their rapid adoption in commercial applications, many RSs are now dynamic economic systems with multiple stakeholders, facing challenges beyond dissolving uncertainty in matchi ng. Fairness [ 6, 15, 18, 35 ], misinformation [ 17 ], user incentives [ 3, 24 ], and privacy [ 21 ] are only some of the challenges RSs face. A recent body of research addresses tradeoffs among stakehol ders [ 9, 10, 28 ]. Online platforms have three main stakeholders: The commercial company that r uns the platform, content consumers, and content providers. Content consumers, which we refer to as users for simplicity, enjoy the RSs' content.

Wearable startup Humane AI has been dripping details about its upcoming device, the AI Pin, for months now. We firs saw it at a TED Talk in May and, more recently, got a glimpse of its promised capabilities at Paris Fashion Week, ahead of Thursday's official unveiling. However many questions regarding how the wearable AI will actually do what it says it will remain to be answered. Here's what we do know: The Humane AI Pin is a pocket-worn wearable AI assistant that can reportedly perform the tasks that many modern cellphones and digital assistants do, but in a radically different form factor. It has no screen, instead reportedly operating primarily through voice commands and occasionally through a virtual screen projected onto the user's hand.

Recommendation models trained on the user feedback collected from deployed recommendation systems are commonly biased. User feedback is considerably affected by the exposure mechanism, as users only provide feedback on the items exposed to them and passively ignore the unexposed items, thus producing numerous false negative samples. Inevitably, biases caused by such user feedback are inherited by new models and amplified via feedback loops. Moreover, the presence of false negative samples makes negative sampling difficult and introduces spurious information in the user preference modeling process of the model. Recent work has investigated the negative impact of feedback loops and unknown exposure mechanisms on recommendation quality and user experience, essentially treating them as independent factors and ignoring their cross-effects. To address these issues, we deeply analyze the data exposure mechanism from the perspective of data iteration and feedback loops with the Missing Not At Random (\textbf{MNAR}) assumption, theoretically demonstrating the existence of an available stabilization factor in the transformation of the exposure mechanism under the feedback loops. We further propose Dynamic Personalized Ranking (\textbf{DPR}), an unbiased algorithm that uses dynamic re-weighting to mitigate the cross-effects of exposure mechanisms and feedback loops without additional information. Furthermore, we design a plugin named Universal Anti-False Negative (\textbf{UFN}) to mitigate the negative impact of the false negative problem. We demonstrate theoretically that our approach mitigates the negative effects of feedback loops and unknown exposure mechanisms. Experimental results on real-world datasets demonstrate that models using DPR can better handle bias accumulation and the universality of UFN in mainstream loss methods.