The evolution of large vision-language models (LVLMs) has shed light on the development of many fields, particularly for multimodal recommendation. While LVLMs offer an integrated understanding of textual and visual information of items from user interactions, their deployment in this domain remains limited due to inherent complexities. First, LVLMs are trained from enormous general datasets and lack knowledge of personalized user preferences. Second, LVLMs struggle with multiple image processing, especially with discrete, noisy, and redundant images in recommendation scenarios. To address these issues, we introduce a new reasoning strategy called Visual-Summary Thought (VST) for Multimodal Recommendation. This approach begins by prompting LVLMs to generate textual summaries of item images, which serve as contextual information. These summaries are then combined with item titles to enhance the representation of sequential interactions and improve the ranking of candidates. Our experiments, conducted across four datasets using three different LVLMs: GPT4-V, LLaVA-7b, and LLaVA-13b validate the effectiveness of VST.

Session-based Recommendation (SBR) is to predict users’ next interested items based on their previous browsing sessions. Existing methods model sessions as graphs or sequences to estimate user interests based on their interacted items to make recommendations. In recent years, graph-based methods have achieved outstanding performance on SBR. However, none of these methods consider temporal information, which is a crucial feature in SBR as it indicates timeliness or currency. Besides, the session graphs exhibit a hierarchical structure and are demonstrated to be suitable in hyperbolic geometry. But few papers design the models in hyperbolic spaces and this direction is still under exploration.In this paper, we propose Time-aware Hyperbolic Graph Attention Network (TA-HGAT) — a novel hyperbolic graph neural network framework to build a session-based recommendation model considering temporal information. More specifically, there are three components in TA-HGAT. First, a hyperbolic projection module transforms the item features into hyperbolic space. Second, the time-aware graph attention module models time intervals between items and the users’ current interests. Third, an evolutionary loss at the end of the model provides an accurate prediction of the recommended item based on the given timestamp. TA-HGAT is built in a hyperbolic space to learn the hierarchical structure of session graphs. Experimental results show that the proposed TA-HGAT has the best performance compared to ten baseline models on two real-world datasets.