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A probabilistic approach to personalize type-based facet ranking for POI suggestion

Ali, Esraa orcid logoORCID: 0000-0003-1600-3161, Caputo, Annalina orcid logoORCID: 0000-0002-7144-8545, Lawless, Séamus orcid logoORCID: 0000-0001-6302-258X and Conlan, Owen orcid logoORCID: 0000-0002-9054-9747 (2021) A probabilistic approach to personalize type-based facet ranking for POI suggestion. In: International Conference on Web Engineering, 18-21May 2021, Biarritz, France (Online). ISBN 978-3-030-74295-9

Abstract
Faceted Search Systems (FSS) have become one of the main search interfaces used in vertical search systems, offering users meaningful facets to refine their search query and narrow down the results quickly to find the intended search target. This work focuses on the problem of ranking type-based facets. In a structured information space, type-based facets (t-facets) indicate the category to which each object belongs. When they belong to a large multi-level taxonomy, it is desirable to rank them separately before ranking other facet groups. This helps the searcher in filtering the results according to their type first. This also makes it easier to rank the rest of the facets once the type of the intended search target is selected. Existing research employs the same ranking methods for different facet groups. In this research, we propose a two-step approach to personalize t-facet ranking. The first step assigns a relevance score to each individual leaf-node t-facet. The score is generated using probabilistic models and it reflects t-facet relevance to the query and the user profile. In the second step, this score is used to re-order and select the sub-tree to present to the user. We investigate the usefulness of the proposed method to a Point Of Interest (POI) suggestion task. Our evaluation aims at capturing the user effort required to fulfil her search needs by using the ranked facets. The proposed approach achieved better results than other existing personalized baselines.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Type-based Facets; Faceted Search; Personalization
Subjects:Computer Science > Information retrieval
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Web Engineering - 21st International Conference, ICWE 2021, Proceedings. Lecture Notes in Computer Science (LNCS) 12706. Springer. ISBN 978-3-030-74295-9
Publisher:Springer
Official URL:https://dx.doi.org/10.1007%2F978-3-030-74296-6_14
Copyright Information:© 2021 Springer
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland (SFI) Grant No. 13/RC/2106, Science Foundation Ireland (SFI) Research Centres Programme and is co-funded under the European Regional Development Fund (ERDF) Grant No. 13/RC/2106_P2
ID Code:25948
Deposited On:02 Jun 2021 13:51 by Annalina Caputo . Last Modified 02 Jun 2021 13:51
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