A Cocktail Approach for Travel Package Recommendation

Liu, Qi; Chen, Enhong; Xiong, Hui; Ge, Yong; Li, Zhongmou; Wu, Xi

doi:10.1109/tkde.2012.233

Cited by 120 publications

(45 citation statements)

References 37 publications

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“…Their recommendation approach is to mix content-based (mapping all users and items to the TAST-model) and collaborative filtering (finding similar-minded users for the correct target area). In subsequent research [19], this model has been extended with a relation factor to a TRAST model, to also represent the relationship between travelers, making this model suitable for group recommendations.…”

Section: Related Workmentioning

confidence: 99%

Hybrid group recommendations for a travel service

Pessemier

Dhondt

Martens

2016

Multimed Tools Appl

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Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations.

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Section: Related Workmentioning

confidence: 99%

Hybrid group recommendations for a travel service

Pessemier

Dhondt

Martens

2016

Multimed Tools Appl

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“…Following Foster et al (2008), where it is suggested that the dimension be reduced to 2r in the standard CCA, we concatenate the different {Z p } as the final representation Z ∈ R (mr)×N for the subsequent learning, such as classification Farquhar et al (2005); Fisch et al (2014), clustering Yang et al (2014); Wu et al (2015), regression Kakade and Foster (2007), search ranking Xu et al (2015); Zhu et al (2015), collaborative filtering Liu et al (2014), and so on.…”

Section: Solutionsmentioning

confidence: 99%

Tensor Canonical Correlation Analysis for Multi-View Dimension Reduction

Luo

Tao

Ramamohanarao

et al. 2015

IEEE Trans. Knowl. Data Eng.

213

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Canonical correlation analysis (CCA) has proven an effective tool for two-view dimension reduction due to its profound theoretical foundation and success in practical applications. In respect of multi-view learning, however, it is limited by its capability of only handling data represented by two-view features, while in many real-world applications, the number of views is frequently many more. Although the ad hoc way of simultaneously exploring all possible pairs of features can numerically deal with multiview data, it ignores the high order statistics (correlation information) which can only be discovered by simultaneously exploring all features.Therefore, in this work, we develop tensor CCA (TCCA) which straightforwardly yet naturally generalizes CCA to handle the data of an arbitrary number of views by analyzing the covariance tensor of the different views. TCCA aims to directly maximize the canonical correlation of multiple (more than two) views. Crucially, we prove that the multi-view canonical correlation maximization problem is equivalent to finding the best rank-1 approximation of the data covariance tensor, which can be solved efficiently using the well-known alternating least squares (ALS) algorithm. As a consequence, the high order correlation information contained in the different views is explored and thus a more reliable common subspace shared by all features can be obtained. In addition, a non-linear extension of TCCA is presented. Experiments on various challenge tasks, including large scale biometric structure prediction, internet advertisement classification and web image annotation, demonstrate the effectiveness of the proposed method.

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“…One package usually consists of many landscapes that are located in one or more areas [8]. Specifically, the travel topics are the themes designed for this package, and the landscapes are the travel places of interest and attractions, which usually locate in nearby areas [7]. There are two models that have been used to design a travel package and they are: TAST (Tourist Area Season Topic) Model and TRAST (Tourist Relation Area Season Topic) Model.…”

Section: Cluster Analysis In Business (Travel Package Recommender Sysmentioning

confidence: 99%

A Survey on Applications of Data Mining using Clustering Techniques

Neha¹,

Vidyavathi²

2015

IJCA

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Data mining is a process that explores and analyses large data sets in order to discover meaningful patterns. Clustering is a main task of exploratory data analysis and data mining applications. Clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups (clusters). Clustering has wide applications in the field of medicine, business and marketing, World Wide Web, computer science, social science, educational data mining, climatology and many more. This paper mainly presents an overview of types of clustering techniques and some of the applications of data mining where clustering techniques can be applied. The main goal of clustering is to produce a good and high quality clusters that depends mainly on the similarity measure which has the ability to discover some or all hidden patterns and also make the analysis of data easy

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A Cocktail Approach for Travel Package Recommendation

Cited by 120 publications

References 37 publications

Hybrid group recommendations for a travel service

Hybrid group recommendations for a travel service

Tensor Canonical Correlation Analysis for Multi-View Dimension Reduction

A Survey on Applications of Data Mining using Clustering Techniques

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