A Probabilistic Approach to Personalize Type-based Facet Ranking for POI Suggestion

TL;DR

This paper introduces a probabilistic, personalized method for ranking type-based facets in faceted search systems, improving user efficiency in Point Of Interest suggestions by better aligning facet relevance with user profiles.

Contribution

It proposes a novel two-step probabilistic approach for personalized t-facet ranking, enhancing search relevance in structured information spaces.

Findings

Outperforms existing personalized baselines in POI suggestion tasks.

Improves user effort reduction in search refinement.

Demonstrates effectiveness of probabilistic models for facet relevance.

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.