Name
Title | ||
|---|---|---|
Relevance Models for Multi-Contextual Appropriateness in Points-Of-Interest Recommendation |
Abstract | ||
|---|---|---|
Trip-qualifiers, such as 'trip-type' (vacation, work etc.), 'accompanied-by' (e.g., solo, friends, family etc.) are potentially useful sources of information that could be used to improve the effectiveness of POI recommendation in a current context (with a given set of these constraints). Using such information is not straight forward because a user's text reviews about the POIs visited in the past do not explicitly contain such annotations (e.g., a positive review about a pub visit does not contain the information on whether the user was with friends or alone, on a business trip or vacation). We propose to use a small set of manually compiled knowledge resource to predict the associations between the review texts in a user profile and the likely trip contexts. We demonstrate that incorporating this information within an IR-based relevance modeling framework significantly improves POI recommendation.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3397271.3401197 | SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval
Virtual Event
China
July, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-8016-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anirban Chakraborty | 1 | 0 | 2.03 |
| Debasis Ganguly | 2 | 214 | 38.69 |
| Owen Conlan | 3 | 447 | 63.88 |