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A versatile package recommendation framework aiming at preference score maximization

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Abstract

Package recommendation systems have gained in popularity especially in the tourism domain, where they propose combinations of different types of attractions that can be visited by someone during a city tour. These systems can also be applied in suggesting home entertainment, proper nutrition or academic courses. Such systems must optimize multiple user criteria in tandem, such as preference score, package cost or duration. This work proposes a flexible framework for recommending packages that best fit users’ preferences while satisfying several constraints on the set of the valid packages. This is achieved by modeling the relation between the items and the categories these items belong to, aiming at recommending to each user the top-k packages that cover their preferred categories and the restriction of a maximum package cost. Our contribution includes an optimal and a greedy algorithm, that both outperform a state-of-the-art system and a popularity-based baseline solution. The novelty of the optimal algorithm is that it combines the collaborative filtering predictions with a graph-based model to produce package recommendations. The problem is expressed through a minimum cost flow network and is solved by integer linear programming. The greedy algorithm has a low computational complexity and provides recommendations which are close to the optimal one. An extensive evaluation of the proposed framework has been carried out on six popular recommendation datasets. The results obtained using a set of widely accepted metrics show promising performance. Finally, the formulation of the problem for specific domains has also been addressed.

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Notes

  1. The application jar file, source code, usage instructions and a sample dataset, which has also been used for the evaluation, are available for download at https://goo.gl/IMbxq1

  2. Item-based and User-based CF implementations of Apache Mahout: https://mahout.apache.org

  3. IBM ILOG CPLEX solver.

  4. http://www.omdbapi.com/

  5. https://www.kaggle.com/CooperUnion/anime-recommendations-database

  6. https://www.last.fm

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, National Technical University of Athens, 9, Iroon Polytechniou str, Zografou, 15780, Athens, Greece

    Panagiotis Kouris, Georgios Alexandridis & Andreas Stafylopatis

  2. Department of Informatics and Telematics, Harokopio University of Athens, 9 Omirou str, Tavros, 17778, Athens, Greece

    Iraklis Varlamis

Authors
  1. Panagiotis Kouris
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  2. Iraklis Varlamis
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  3. Georgios Alexandridis
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  4. Andreas Stafylopatis
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Correspondence to Panagiotis Kouris.

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Kouris, P., Varlamis, I., Alexandridis, G. et al. A versatile package recommendation framework aiming at preference score maximization. Evolving Systems 11, 423–441 (2020). https://doi.org/10.1007/s12530-018-9231-2

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