The explosive growth of Internet of Things (IoT) and Web of Things (WoT) technologies, characterized by a vast diversity of devices and data formats, producing vast volumes of information at a high pace in real time necessitates a paradigm shift in information retrieval (IR) systems. Traditional IR struggles to navigate the dynamic landscapes of these interconnected environments. This work proposes a multidimensional taxonomy framework to bridge this critical gap. Our framework not only unifies existing classification approaches but also delves into the analysis of traditional IR subtasks, thereby establishing a cohesive foundation for future advancements in IR tailored to the evolving IoT/WoT landscape. We further contribute by identifying key challenges and posing open research questions, thus propelling the development of next-generation IR techniques specifically tailored to the intricate search demands of the evolving IoT and WoT cyber-world.

In this paper, we present the second release of VideoCLIP, an interactive CLIP-based video retrieval system that participated in the Video Browser Showdown 2023. While we continue to use the underlying architecture to map the content between image and text, we concentrate on improving the user experience for novice users. Specifically, we have implemented three different query modalities and redesigned the user interface in order to adapt to the context of the Interactive Video Retrieval for Beginners (IVR4B) workshop. These modifications ultimately aim to provide newcomers with a simple and efficient user experience to locate the desired videos.

\beginabstract Recently, modelling heterogeneous interactions in recommender systems has attracted research interest. Real-world scenarios involve sequential multi-type user-item interactions such as ''shape view'', ''shape add-to-favourites'', ''shape add-to-cart'' and ''shape purchase''. Graph Neural Network (GNN) methods have been widely adopted in Representation Learning of similar sequential user-item interactions. Promising results have been achieved by the integration of GNNs and transformers for self-attention. However, GNN based methods suffer from limited capability in handling global user-item interaction dependencies, particularly for long sequences. Moreover, these models require high computational cost of transformers, due to the quadratic memory and time complexity with respect to sequence length. This results in memory bottlenecks and slow training especially in computational resource-constrained environments. To address these challenges, we propose the FATH model which employs Flash Attention mechanism to reduce the high-bandwidth memory usage over higher-order user-item interaction sequences. Experimental results show that our model improves the training speed and reduces the memory usage with better recommendation performance in comparison with the state-of the art baselines.

Lifelogging was introduced as the process of passively capturing personal daily events via wearable devices. It ultimately creates a visual diary encoding every aspect of one's life with the aim of future sharing or recollecting. In this paper, we present LifeSeeker, a lifelog image retrieval system participating in the Lifelog Search Challenge (LSC) for 3 years, since 2019. Our objective is to support users to seek specific life moments using a combination of textual descriptions, spatial relationships, location information, and image similarities. In addition to the LSC challenge results, a further experiment was conducted in order to evaluate the power retrieval of our system on both expert and novice users. This experiment informed us about the effectiveness of the user's interaction with the system when involving non-experts.

A number of works have shown that the aggregation of several information Retrieval (IR) systems works better than each system working individually. Nevertheless, early investigation in the context of CLEF Robust-WSD task, in which semantics is involved, showed that aggregation strategies achieve only slight improvements. This paper proposes a re-ranking approach which relies on inter-document similarities. The novelty of our idea is twofold: the output of a semantic based IR, system is exploited to re-weigh documents and a new strategy based on Semantic Vectors is used to compute inter-document similarities.