\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.

Faceted Search Systems (FSS) have gained prominence in research as one of the exploratory search approaches that support complex search tasks. They provide facets to educate users about the information space and allow them to refine their search query and navigate back and forth between resources on a single results page. When the information available in the collection being searched across increases, so does the number of associated facets. This can make it impractical to display all of the facets at once. To tackle this problem, FSS employ methods for facet ranking. Ranking methods can be based on the information structure, the textual queries issued by the user, or the usage logs. Such methods reflect neither the importance of the facets nor the user interests. I focus on the problem of ranking facets from knowledge bases (KB) and Linked Open Data (LOD). KB have the advantage of containing high quality structured data. With the increasing size and complexity of LOD datasets, the task of deciding which facets should be manifest to the user, and in which order, becomes more difficult. Moreover, the idea of personalizing exploratory search can be challenging and tricky, since personalization in IR (specifically precision-oriented search engines) implicitly implies narrowing and focusing the information space to retrieve the most relevant results according to the users' interests and desires. On the contrary, exploratory search systems are typically recall-oriented and they favor covering as much from the information space as possible. They also encourage diversifying the user knowledge to help them learn and discover the unknown. The generation of a ranked list of facets should be a dynamic process for a number of reasons. First of all, manually setting up facets is a time consuming task which relies upon domain experts. Second, it is not practical on large, multi-domain datasets. Even one-off automatic facet generation and ranking might not be suitable for data that changes and grows over time. Lastly, the relevance of facets can be user, query and context dependant. I am proposing a personalized approach to the dynamic ranking of facets. The approach combines different sources of information to recommend the most relevant facets. The first source is the knowledge-base from which the facets are originally generated. The second is facets generated from the top-ranked documents in a search system. The user search query is submitted to a general search engine and the top ranked documents are used to add context to the ranking process. Finally, the third source uses a user interests profile, which is collected from social media and the user's behavior in the system. These sources contribute to the final ranking score to reflect the importance of facets without ignoring user interests. My proposed research aims to answer the following research questions: RQ1: To what extent does the addition of features from retrieved search results from a general web search improve the computation of facet relevance? RQ2: What is the most effective method to incorporate personal interests and user usage data into the ranking process? RQ3: Does personalising facet ranking have a measurable impact upon the user search experience.