We present RecViz, a novel web application designed to support qualitative analysis of recommender system performance on large datasets.RecViz offers real-time, interactive graph visualisation of recommendation data, enabling side-by-side comparisons of models through dual graph views.Leveraging GPU acceleration via CUDA and WebGL, it delivers fast, responsive force-directed layouts, even at scale.Unlike prior tools limited to small datasets, RecViz shows the potential to handle large datasets efficiently.For example, it maintains an average of 28 FPS while visualising the full MovieLens-1M dataset, with all 1 million interactions.RecViz is open-source and available on GitHub 1 under the Apache-2.0licence [7].

Recent deployments of learned query optimizers use expensive neural networks and ad-hoc search policies. To address these issues, we introduce LimeQO, a framework for offline query optimization leveraging low-rank learning to efficiently explore alternative query plans with minimal resource usage. By modeling the workload as a partially observed, low-rank matrix, we predict unobserved query plan latencies using purely linear methods, significantly reducing computational overhead compared to neural networks. We formalize offline exploration as an active learning problem, and present simple heuristics that reduces a 3-hour workload to 1.5 hours after just 1.5 hours of exploration. Additionally, we propose a transductive Tree Convolutional Neural Network (TCNN) that, despite higher computational costs, achieves the same workload reduction with only 0.5 hours of exploration. Unlike previous approaches that place expensive neural networks directly in the query processing ''hot'' path, our approach offers a low-overhead solution and a no-regressions guarantee, all without making assumptions about the underlying DBMS.

In recent years, the rapid growth of online multimedia services, such as e-commerce platforms, has necessitated the development of personalised recommendation approaches that can encode diverse content about each item. Indeed, modern multi-modal recommender systems exploit diverse features obtained from raw images and item descriptions to enhance the recommendation performance. However, the existing multi-modal recommender systems primarily depend on the features extracted individually from different media through pre-trained modality-specific encoders, and exhibit only shallow alignments between different modalities, thereby limiting these systems’ ability to capture the underlying relationships between the modalities. In this article, we enhance the deep alignment of large multi-modal encoders to address the shallow alignment of modalities in multi-modal recommender systems. These encoders have previously demonstrated state-of-the-art effectiveness in ranking items across various domains. Specifically, we investigate the use of three state-of-the-art large multi-modal encoders – CLIP (dual-stream), VLMo and BEiT-3 (unified) – for recommendation tasks. We explore their benefits for recommendation through using a range of strategies, including the use of pre-trained and fine-tuned encoders, as well as the evaluation of the end-to-end training of these encoders. We show that pre-trained large multi-modal encoders generate more aligned and effective user/item representations compared with existing modality-specific encoders across four existing multi-modal recommendation datasets. Furthermore, we show that fine-tuning these encoders further improves the recommendation performance, with end-to-end training emerging as the most effective paradigm, significantly outperforming both pre-trained and fine-tuned encoders with an improved recommendation performance. We also demonstrate the effectiveness of large multi-modal encoders in facilitating modality alignment by evaluating the contribution of each modality separately. Finally, we show that the dual-stream approach, specifically CLIP, is the most effective architecture for these large multi-modal encoders, outperforming the unified approaches (i.e., VLMo and BEiT3) in terms of effectiveness and efficiency.

Real-world recommendation scenarios usually need to handle diverse user-item interaction behaviours, including page views, adding items into carts, and purchasing activities. The interactions that precede the actual target behaviour (e.g., purchasing an item) allow to capture the user’s preferences from different angles, and are used as auxiliary information (e.g., page views) to enrich the system’s knowledge about the users’ preferences, thereby helping to enhance recommendation for the target behaviour. Despite efforts in modelling the users’ multi-behaviour interaction information, the existing multi-behaviour recommenders still face two challenges: (1) Data sparsity across multiple user behaviours is a common issue that limits the recommendation performance, particularly for the target behaviour, which typically exhibits fewer interactions compared to other auxiliary behaviours; (2) Noisy auxiliary interactive behaviours where the information in the auxiliary behaviours might be non-relevant for recommendation. In this case, a direct adoption of contrastive learning between the target behaviour and the auxiliary behaviours will amplify the noise in the auxiliary behaviours, thereby negatively impacting the real semantics that can be derived from the target behaviour. To address these two challenges, we propose a new model called Knowledge-Enhanced Multi-behaviour Contrastive Learning for Recommendation (KEMCL). In particular, to address the problem of sparse user multi-behaviour interaction information, we leverage a dual-perspective knowledge encoding componentthat enriches the semantic representations of items, and generate supervision signals through self-supervised learning so as to enhance recommendation. In addition, we develop a cross-behaviour learning component, which includes two contrastive learning (CL) methods, inter CL and intra CL, to alleviate the problem of noisy auxiliary interactions. Extensive experiments on three public recommendation datasets show that our proposed KEMCL model significantly outperforms seven existing state-of-the-art methods. In particular, our KEMCL model outperforms the best baseline, namely KMCLR, by 5.42% on the large Tmall dataset.

With the rapid development of online multimedia services, especially in e-commerce platforms, there is a pressing need for personalised recommender systems that can effectively encode the diverse multi-modal content associated with each item. However, we argue that existing multi-modal recommender systems typically use isolated processes for both feature extraction and modality encoding. Such isolated processes can harm the recommendation performance. Firstly, an isolated extraction process underestimates the importance of effective feature extraction in multi-modal recommendations, potentially incorporating non-relevant information, which is harmful to item representations. Second, an isolated modality encoding process produces disjoint embeddings for item modalities due to the individual processing of each modality, which leads to a suboptimal fusion of user/item representations for an effective user preferences prediction. We hypothesise that the use of a unified model for addressing both aforementioned isolated processes will enable the consistent extraction and cohesive fusion of joint multi-modal features, thereby enhancing the effectiveness of multi-modal recommender systems. In this paper, we propose a novel model, called Unified multi-modal Graph Transformer (UGT), which firstly leverages a multi-way transformer to extract aligned multi-modal features from raw data for top-k recommendation. Subsequently, we build a unified graph neural network in our UGT model to jointly fuse the multi-modal user/item representations derived from the output of the multi-way transformer. Using the graph transformer architecture of our UGT model, we show that the UGT model achieves significant effectiveness gains, especially when jointly optimised with the commonly used recommendation losses. Our extensive experiments conducted on three benchmark datasets demonstrate that our proposed UGT model consistently outperforms nine existing state-of-the-art recommendation approaches and by up to 13.97% over the best baseline.

Recommender systems commonly suffer from the long-standing data sparsity problem where insufficient user-item interaction data limits the systems’ ability to make accurate recommendations. This problem can be alleviated using cross-domain recommendation techniques. In particular, in a cross-domain setting, knowledge sharing between domains permits improved effectiveness on the target domain. While recent cross-domain recommendation techniques used a pre-training configuration, we argue that such techniques lead to a low fine-tuning efficiency, especially when using large neural models. In recent language models, prompts have been used for parameter-efficient and time-efficient tuning of the models on the downstream tasks—these prompts represent a tunable latent vector that permits to freeze the rest of the language model’s parameters. To address the cross-domain recommendation task in an efficient manner, we propose a novel Personalised Graph Prompt-based Recommendation (PGPRec) framework, which leverages the efficiency benefits from prompt-tuning. In such a framework, we develop personalised and item-wise graph prompts based on relevant items to those items the user has interacted with. In particular, we apply Contrastive Learning to generate the pre-trained embeddings, to allow an increased generalisability in the pre-training stage, and to ensure an effective prompt-tuning stage. To evaluate the effectiveness of our PGPRec framework in a cross-domain setting, we conduct an extensive evaluation with the top- k recommendation task and perform a cold-start analysis. The obtained empirical results on four Amazon Review datasets show that our proposed PGPRec framework can reduce up to 74% of the tuned parameters with a competitive performance and achieves an 11.41% improved performance compared to the strongest baseline in a cold-start scenario.

Recently micro-videos have become more popular in social media platforms such as TikTok and Instagram. Engagements in these platforms are facilitated by multi-modal recommendation systems. Indeed, such multimedia content can involve diverse modalities, often represented as visual, acoustic, and textual features to the recommender model. Existing works in micro-video recommendation tend to unify the multi-modal channels, thereby treating each modality with equal importance. However, we argue that these approaches are not sufficient to encode item representations with multiple modalities, since the used methods cannot fully disentangle the users' tastes on different modalities. To tackle this problem, we propose a novel learning method named Multi-Modal Graph Contrastive Learning (MMGCL), which aims to explicitly enhance multi-modal representation learning in a self-supervised learning manner. In particular, we devise two augmentation techniques to generate the multiple views of a user/item: modality edge dropout and modality masking. Furthermore, we introduce a novel negative sampling technique that allows to learn the correlation between modalities and ensures the effective contribution of each modality. Extensive experiments conducted on two micro-video datasets demonstrate the superiority of our proposed MMGCL method over existing state-of-the-art approaches in terms of both recommendation performance and training convergence speed.