Learning a recommender system model from an item's raw modality features (such as image, text, audio, etc.), called MoRec, has attracted growing interest recently. One key advantage of MoRec is that it can easily benefit from advances in other fields, such as natural language processing (NLP) and computer vision (CV). Moreover, it naturally supports transfer learning across different systems through modality features, known as transferable recommender systems, or TransRec. However, so far, TransRec has made little progress, compared to groundbreaking foundation models in the fields of NLP and CV. The lack of large-scale, high-quality recommendation datasets poses a major obstacle. To this end, we introduce NineRec, a TransRec dataset suite that includes a large-scale source domain recommendation dataset and nine diverse target domain recommendation datasets. Each item in NineRec is represented by a text description and a high-resolution cover image. With NineRec, we can implement TransRec models in an end-to-end training manner instead of using pre-extracted invariant features. We conduct a benchmark study and empirical analysis of TransRec using NineRec, and our findings provide several valuable insights. To support further research, we make our code, datasets, benchmarks, and leaderboards publicly available at https://github.com/westlake-repl/NineRec.
Recommender systems (RS) have achieved significant success by leveraging explicit identification (ID) features. However, the full potential of content features, especially the pure image pixel features, remains relatively unexplored. The limited availability of large, diverse, and content-driven image recommendation datasets has hindered the use of raw images as item representations. In this regard, we present PixelRec, a massive image-centric recommendation dataset that includes approximately 200 million user-image interactions, 30 million users, and 400,000 high-quality cover images. By providing direct access to raw image pixels, PixelRec enables recommendation models to learn item representation directly from them. To demonstrate its utility, we begin by presenting the results of several classical pure ID-based baseline models, termed IDNet, trained on PixelRec. Then, to show the effectiveness of the dataset's image features, we substitute the itemID embeddings (from IDNet) with a powerful vision encoder that represents items using their raw image pixels. This new model is dubbed PixelNet.Our findings indicate that even in standard, non-cold start recommendation settings where IDNet is recognized as highly effective, PixelNet can already perform equally well or even better than IDNet. Moreover, PixelNet has several other notable advantages over IDNet, such as being more effective in cold-start and cross-domain recommendation scenarios. These results underscore the importance of visual features in PixelRec. We believe that PixelRec can serve as a critical resource and testing ground for research on recommendation models that emphasize image pixel content. The dataset, code, and leaderboard will be available at https://github.com/westlake-repl/PixelRec.
Multi-modal recommendation systems, which integrate diverse types of information, have gained widespread attention in recent years. However, compared to traditional collaborative filtering-based multi-modal recommendation systems, research on multi-modal sequential recommendation is still in its nascent stages. Unlike traditional sequential recommendation models that solely rely on item identifier (ID) information and focus on network structure design, multi-modal recommendation models need to emphasize item representation learning and the fusion of heterogeneous data sources. This paper investigates the impact of item representation learning on downstream recommendation tasks and examines the disparities in information fusion at different stages. Empirical experiments are conducted to demonstrate the need to design a framework suitable for collaborative learning and fusion of diverse information. Based on this, we propose a new model-agnostic framework for multi-modal sequential recommendation tasks, called Online Distillation-enhanced Multi-modal Transformer (ODMT), to enhance feature interaction and mutual learning among multi-source input (ID, text, and image), while avoiding conflicts among different features during training, thereby improving recommendation accuracy. To be specific, we first introduce an ID-aware Multi-modal Transformer module in the item representation learning stage to facilitate information interaction among different features. Secondly, we employ an online distillation training strategy in the prediction optimization stage to make multi-source data learn from each other and improve prediction robustness. Experimental results on a stream media recommendation dataset and three e-commerce recommendation datasets demonstrate the effectiveness of the proposed two modules, which is approximately 10% improvement in performance compared to baseline models.
Recommendation models that utilize unique identities (IDs) to represent distinct users and items have been state-of-the-art (SOTA) and dominated the recommender systems (RS) literature for over a decade. Meanwhile, the pre-trained modality encoders, such as BERT and ViT, have become increasingly powerful in modeling the raw modality features of an item, such as text and images. Given this, a natural question arises: can a purely modality-based recommendation model (MoRec) outperforms or matches a pure ID-based model (IDRec) by replacing the itemID embedding with a SOTA modality encoder? In fact, this question was answered ten years ago when IDRec beats MoRec by a strong margin in both recommendation accuracy and efficiency. We aim to revisit this `old' question and systematically study MoRec from several aspects. Specifically, we study several sub-questions: (i) which recommendation paradigm, MoRec or IDRec, performs better in practical scenarios, especially in the general setting and warm item scenarios where IDRec has a strong advantage? does this hold for items with different modality features? (ii) can the latest technical advances from other communities (i.e., natural language processing and computer vision) translate into accuracy improvement for MoRec? (iii) how to effectively utilize item modality representation, can we use it directly or do we have to adjust it with new data? (iv) are there some key challenges for MoRec to be solved in practical applications? To answer them, we conduct rigorous experiments for item recommendations with two popular modalities, i.e., text and vision. We provide the first empirical evidence that MoRec is already comparable to its IDRec counterpart with an expensive end-to-end training method, even for warm item recommendation. Our results potentially imply that the dominance of IDRec in the RS field may be greatly challenged in the future.