Related papers: Personalized Search
Modern e-commerce platforms offer vast product selections, making it difficult for customers to find items that they like and that are relevant to their current session intent. This is why it is key for e-commerce platforms to have near…
Decentralized search aims to find the target node in a large network by using only local information. The applications of it include peer-to-peer file sharing, web search and anything else that requires locating a specific target in a…
Research organisations and their research outputs have been growing considerably in the past decades. This large body of knowledge attracts various stakeholders, e.g., for knowledge sharing, technology transfer, or potential collaborations.…
The Query-By-Document (QBD) problem is an information retrieval problem where the query is a document, and the retrieved candidates are documents that match the query document, often in a domain or query specific manner. This can be crucial…
Large language models (LLMs) have recently received significant attention for their exceptional capabilities. Despite extensive efforts in developing general-purpose LLMs that can be utilized in various natural language processing (NLP)…
With the rapid growth of scientific publications, researchers need to spend more time and effort searching for papers that align with their research interests. To address this challenge, paper recommendation systems have been developed to…
Recommender systems leverage both content and user interactions to generate recommendations that fit users' preferences. The recent surge of interest in deep learning presents new opportunities for exploiting these two sources of…
Large Language Models (LLMs) excel at tackling various natural language tasks. However, due to the significant costs involved in re-training or fine-tuning them, they remain largely static and difficult to personalize. Nevertheless, a…
Academic search engines allow scientists to explore related work relevant to a given query. Often, the user is also aware of the "aspect" to retrieve a relevant document. In such cases, existing search engines can be used by expanding the…
The paper proposes three different kinds of science models as value-added services that are integrated in the retrieval process to enhance retrieval quality. The paper discusses the approaches Search Term Recommendation, Bradfordizing and…
Nowadays e-commerce search has become an integral part of many people's shopping routines. Two critical challenges stay in today's e-commerce search: how to retrieve items that are semantically relevant but not exact matching to query…
Query auto completion (QAC) systems are a standard part of search engines in industry, helping users formulate their query. Such systems update their suggestions after the user types each character, predicting the user's intent using…
In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions. To demonstrate the empirical efficiency of the proposed approaches we investigate their applications…
Because of the increasing number of electronic data, designing efficient tools to retrieve and exploit documents is a major challenge. Current search engines suffer from two main drawbacks: there is limited interaction with the list of…
Because of the data deluge in scientific publication, finding relevant information is getting harder and harder for researchers and readers. Building an enhanced scientific search engine by taking semantic relations into account poses a…
Personalization generally improves the performance of queries but in a few cases it may also harms it. If we are able to predict and therefore to disable personalization for those situations, the overall performance will be higher and users…
This paper studies retrieval-augmented approaches for personalizing large language models (LLMs), which potentially have a substantial impact on various applications and domains. We propose the first attempt to optimize the retrieval models…
Looking into the growth of information in the web it is a very tedious process of getting the exact information the user is looking for. Many search engines generate user profile related data listing. This paper involves one such process…
{\em Personalized PageRank (PPR)} stands as a fundamental proximity measure in graph mining. Since computing an exact SSPPR query answer is prohibitive, most existing solutions turn to approximate queries with guarantees. The…
This study considers the extent to which users with the same query agree as to what is relevant, and how what is considered relevant may translate into a retrieval algorithm and results display. To combine user perceptions of relevance with…