RECORD
Venue Suggestion Using Social-Centric Scores
Information Retrieval · Submitted 22 Mar 2018
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, show that social scores are more effective than scores based venues' content.
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The abstract, sentence by sentence
User modeling is a very important task for making relevant suggestions of venues to the users. These suggestions are often based on matching the venues' features with the users' preferences, which can be collected from previously visited locations. In this paper, we present a set of relevance scores for making personalized suggestions of points of interest. These scores model each user by focusing on the different types of information extracted from venues that they have previously visited. In particular, we focus on scores extracted from social information available on location-based social networks. Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, show that social scores are more effective than scores based venues' content.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | User modeling is a very important task for making relevant suggestions of venues to the users. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | These suggestions are often based on matching the venues' features with the users' preferences, which can be collected from previously visited locations. | background 1.00 | 0.06 | 0.02 | asserted 0.65 |
| s2 | In this paper, we present a set of relevance scores for making personalized suggestions of points of interest. | contribution 1.00 | 0.90 | 0.06 | asserted 0.98 |
| s3 | These scores model each user by focusing on the different types of information extracted from venues that they have previously visited. | method 0.85 | 0.86 | 0.04 | asserted 0.99 |
| s4 | In particular, we focus on scores extracted from social information available on location-based social networks. | method 0.56 | 0.63 | 0.04 | asserted 0.87 |
| s5 | Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, show that social scores are more effective than scores based venues' content. | result 1.00 | 0.97 | 0.87 | asserted 1.00 |
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The paper
Original abstract
User modeling is a very important task for making relevant suggestions of venues to the users. These suggestions are often based on matching the venues' features with the users' preferences, which can be collected from previously visited locations. In this paper, we present a set of relevance scores for making personalized suggestions of points of interest. These scores model each user by focusing on the different types of information extracted from venues that they have previously visited. In particular, we focus on scores extracted from social information available on location-based social networks. Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, show that social scores are more effective than scores based venues' content.
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