Where shall we go today?

Brilhante, Igo Ramalho; Macêdo, José Antônio Fernandes de; Nardini, Franco Maria; Perego, Raffaele; Renso, Chiara

doi:10.1145/2505515.2505643

Cited by 61 publications

(9 citation statements)

References 14 publications

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“…For the experiments, we used three different datasets built from two data sources: the Global-scale check-in dataset from Yang et al (2016), the Semantic trails dataset by Monti et al (2018), and Trip builder used in the work of Brilhante et al (2013); the first two exploit the Foursquare LBSN, whereas the last one uses photos from Flickr to build the sequences (called trajectories in that work) followed by the users.…”

Section: Datasetsmentioning

confidence: 99%

“…Furthermore, because of the high sparsity in this domain and the difficulty to match the exact same venue the user visited in the test set, some authors report accuracy metrics but at the category level instead of the item level, this means that a metric like precision, for example, would measure how many categories that appear in the test set are recommended by the algorithm, or other variations, such as measuring the likelihood that the recommended categories would be produced at random (perplexity), or measuring the interest of a user in a recommended tour based on the time she spends on venues that belong to those categories (He et al 2017;Brilhante et al 2013;Palumbo et al 2017;Lim et al 2015). According to this, we define the Test Feature Precision (TFP) that takes into account the features of the POIs that we retrieved correctly but each category is only taken into account based on the number of categories of each type available in the test set, so, for instance, if the recommended list consists of 3 museums and in the test set there are only 2, only 2 of them will be used in the computation of the metric.…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Applying reranking strategies to route recommendation using sequence-aware evaluation

Sánchez

Bellogín

2020

User Model User-Adap Inter

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Venue recommendation approaches have become particularly useful nowadays due to the increasing number of users registered in Location-Based Social Networks (LBSNs), applications where it is possible to share the venues someone has visited and establish connections with other users in the system. Besides, the venue recommendation problem has certain characteristics that differ from traditional recommendation, and it can also benefit from other contextual aspects to not only recommend independent venues, but complete routes or venue sequences of related locations. Hence, in this paper we investigate the problem of route recommendation under the perspective of generating a sequence of meaningful locations for the users, by analyzing both their personal interests and the intrinsic relationships between the venues. We divide this problem into three stages, proposing general solutions to each case: first, we state a general methodology to derive user routes from LBSNs datasets that can be applied in as many scenarios as possible; second, we define a reranking framework that generate sequences of items from recommendation lists using different techniques; and third, we propose an evaluation metric that captures both accuracy and sequentiality at the same time. We report our experiments on several LBSNs datasets and by means of different recommendation quality metrics and algorithms. As a result, we have found that classical recommender systems are comparable to specifically tailored algorithms for this task, although exploiting the temporal dimension, in general, helps on improving the performance of these techniques; additionally, the proposed reranking strategies show promising results in terms of finding a tradeoff between relevance, sequentiality, and distance, essential dimensions in both venue and route recommendation tasks.

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Section: Datasetsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Applying reranking strategies to route recommendation using sequence-aware evaluation

Sánchez

Bellogín

2020

User Model User-Adap Inter

View full text Add to dashboard Cite

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“…An idea behind some recent work is to use geo-referenced online content (e.g., Flickr 10 pictures) to learn and recommend popular trajectories such as (Baraglia et al 2013), as we did in Cicero using Foursquare check-ins to infer popular paths. Others exploit them as sources for mining popular venues (Brilhante et al 2013), travel sequences (Zheng and Xie 2011) or, more in general, travel attractiveness (Waga et al 2012).…”

Section: Itinerary Recommendersmentioning

confidence: 99%

Enhancing cultural recommendations through social and linked open data

Sansonetti

Gasparetti

Micarelli

et al. 2019

User Model User-Adap Inter

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In this article, we describe a hybrid recommender system (RS) in the artistic and cultural heritage area, which takes into account the activities on social media performed by the target user and her friends, and takes advantage of linked open data (LOD) sources. Concretely, the proposed RS (1) extracts information from Facebook by analyzing content generated by users and their friends; (2) performs disambiguation tasks through LOD tools; (3) profiles the active user as a social graph; (4) provides her with personalized suggestions of artistic and cultural resources in the surroundings of the user's current location. The last point is performed by integrating collaborative filtering algorithms with semantic technologies in order to leverage LOD sources such as DBpedia and Europeana. Based on the recommended points of cultural interest, the proposed system is also able to suggest to the active user itineraries among them, which meet her preferences and needs and are sensitive to her physical and social contexts as well. Experimental results on real users showed the effectiveness of the different modules of the proposed recommender.

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“…Then, Chen and Cheng [18] extended this work by considering the size of groups of people traveling together retrieved from face detection. In [19]- [21], Brilhante et al developed a personalized itinerary planning method and formulated the trip planning problem as an instance of the generalized maximum coverage problem. The method comprises two steps for selection of sub-itineraries and combination to maximize users' personal interests.…”

Section: Copyright Cmentioning

confidence: 99%

Personalized Trip Planning Considering User Preferences and Environmental Variables with Uncertainty

Kim

Hong

Suh

2019

IEICE Trans. Inf. & Syst.

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Personalized trip planning is a challenging problem given that places of interest should be selected according to user preferences and sequentially arranged while satisfying various constraints. In this study, we aimed to model various uncertain aspects that should be considered during trip planning and efficiently generate personalized plans that maximize user satisfaction based on preferences and constraints. Specifically, we propose a probabilistic itinerary evaluation model based on a hybrid temporal Bayesian network that determines suitable itineraries considering preferences, constraints, and uncertain environmental variables. The model retrieves the sum of time-weighted user satisfaction, and ant colony optimization generates the trip plan that maximizes the objective function. First, the optimization algorithm generates candidate itineraries and evaluates them using the proposed model. Then, we improve candidate itineraries based on the evaluation results of previous itineraries. To validate the proposed trip planning approach, we conducted an extensive user study by asking participants to choose their preferred trip plans from options created by a human planner and our approach. The results show that our approach provides human-like trip plans, as participants selected our generated plans in 57% of the pairs. We also evaluated the efficiency of the employed ant colony optimization algorithm for trip planning by performance comparisons with other optimization methods.

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Where shall we go today?

Cited by 61 publications

References 14 publications

Applying reranking strategies to route recommendation using sequence-aware evaluation

Applying reranking strategies to route recommendation using sequence-aware evaluation

Enhancing cultural recommendations through social and linked open data

Personalized Trip Planning Considering User Preferences and Environmental Variables with Uncertainty

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