Improving Personalized Trip Recommendation by Avoiding Crowds

Wang, Xiaoting; Chan, Jeffrey; Lim, Kwan Hui; Vaithianathan, Tharshan

doi:10.1145/2983323.2983749

Cited by 38 publications

(23 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…negative sentiment could be related to mess from falling leaves, which could be mitigated through additional maintenance), or whether park use can be promoted at optimal times. In addition to urban planning, we can also improve existing tour recommendation and route planning systems [6,17,22,47] by using our sentiment analysis approach to identify and recommend Points-of-Interest that elicit more positive sentiments.…”

Section: Discussion Of Main Findingsmentioning

confidence: 99%

The Grass is Greener on the Other Side

Lim

Lee

Kendal

et al. 2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

Self Cite

View full text Add to dashboard Cite

Green spaces are believed to improve the well-being of users in urban areas. While there are urban research exploring the emotional benefits of green spaces, these works are based on user surveys and case studies, which are typically small in scale, intrusive, time-intensive and costly. In contrast to earlier works, we utilize a non-intrusive methodology to understand green space effects at large-scale and in greater detail, via digital traces left by Twitter users. Using this methodology, we perform an empirical study on the effects of green spaces on user sentiments and emotions in Melbourne, Australia and our main findings are: (i) tweets in green spaces evoke more positive and less negative emotions, compared to those in urban areas; (ii) each season affects various emotion types differently; (iii) there are interesting changes in sentiments based on the hour, day and month that a tweet was posted; and (iv) negative sentiments are typically associated with large transport infrastructures such as train interchanges, major road junctions and railway tracks. The novelty of our study is the combination of psychological theory, alongside data collection and analysis techniques on a large-scale Twitter dataset, which overcomes the limitations of traditional methods in urban research.

show abstract

Section: Discussion Of Main Findingsmentioning

confidence: 99%

The Grass is Greener on the Other Side

Lim

Lee

Kendal

et al. 2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

Self Cite

View full text Add to dashboard Cite

show abstract

“…Other interesting papers in the domain of itinerary planning include [34], where the authors use GPS data to build an itinerary recommendation engine and evaluate it using Beijing as an example, [26], which focuses on suggesting routes in a city that also offer some utility to the user as opposed to just being the shortest sourcedestination paths, as well as various approaches that use geo-tagged social media, e.g., [4,21,24,25,30,32,33,35], and approaches based on personalization [5, 11, 12, 18-20, 22, 23, 27, 28]. A final interesting piece of related work is [31] where the authors study the orienteering problem in a tourist application from a game-theoretic view-point.…”

Section: Related Workmentioning

confidence: 99%

Orienteering Algorithms for Generating Travel Itineraries

Friggstad

Gollapudi

Kollias

et al. 2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

View full text Add to dashboard Cite

We study the problem of automatically and efficiently generating itineraries for users who are on vacation. We focus on the common case, wherein the trip duration is more than a single day. Previous efficient algorithms based on greedy heuristics suffer from two problems. First, the itineraries are often unbalanced, with excellent days visiting top attractions followed by days of exclusively lower-quality alternatives. Second, the trips often re-visit neighborhoods repeatedly in order to cover increasingly low-tier points of interest. Our primary technical contribution is an algorithm that addresses both these problems by maximizing the quality of the worst day. We give theoretical results showing that this algorithm's competitive factor is within a factor two of the guarantee of the best available algorithm for a single day, across many variations of the problem. We also give detailed empirical evaluations using two distinct datasets: (a) anonymized Google historical visit data and (b) Foursquare public check-in data. We show first that the overall utility of our itineraries is almost identical to that of algorithms specifically designed to maximize total utility, while the utility of the worst day of our itineraries is roughly twice that obtained from other approaches. We then turn to evaluation based on human raters who score our itineraries only slightly below the itineraries created by human travel experts with deep knowledge of the area.

show abstract

“…Following [3,14,29], we also require l1 and l |tr| to be at a given starting POI ls and a given ending POI le. For ease of discussion, we call such a trip recommendation problem the TripRec query:…”

Section: Problem Formulationmentioning

confidence: 99%

A Joint Context-Aware Embedding for Trip Recommendations

Ramamohanarao

2019

2019 IEEE 35th International Conference on Data Engineering (ICDE)

View full text Add to dashboard Cite

Trip recommendation is an important location-based service that helps relieve users from the time and efforts for trip planning. It aims to recommend a sequence of places of interest (POIs) for a user to visit that maximizes the user's satisfaction. When adding a POI to a recommended trip, it is essential to understand the context of the recommendation, including the POI popularity, other POIs co-occurring in the trip, and the preferences of the user. These contextual factors are learned separately in existing studies, while in reality, they impact jointly on a user's choice of a POI to visit. In this study, we propose a POI embedding model to jointly learn the impact of these contextual factors. We call the learned POI embedding a contextaware POI embedding. To showcase the effectiveness of this embedding, we apply it to generate trip recommendations given a user and a time budget. We propose two trip recommendation algorithms based on our contextaware POI embedding. The first algorithm finds the exact optimal trip by transforming and solving the trip recommendation problem as an integer linear programming problem. To achieve a high computation efficiency, the second algorithm finds a heuristically optimal trip based on adaptive large neighborhood search. We perform extensive experiments on real datasets. The results show that our proposed algorithms consistently outperform state-of-the-art algorithms in trip recommendation quality, with an advantage of up to 43% in F1-score.

show abstract

Improving Personalized Trip Recommendation by Avoiding Crowds

Cited by 38 publications

References 17 publications

The Grass is Greener on the Other Side

The Grass is Greener on the Other Side

Orienteering Algorithms for Generating Travel Itineraries

A Joint Context-Aware Embedding for Trip Recommendations

Contact Info

Product

Resources

About