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Cold-start Point-of-interest Recommendation through Crowdsourcing

Published: 25 August 2020 Publication History

Abstract

Recommender system is a popular tool that aims to provide personalized suggestions to user about items, products, services, and so on. Recommender system has effectively been used in online social networks, especially the location-based social networks for providing suggestions for interesting places known as POIs (points-of-interest). Popular recommender systems explore historical data to learn users’ preferences and, subsequently, they recommend locations to an active user. This strategy faces a major problem when a new POI or business evolves in a city. New business has no historical user experience data. Thus, a recommender system fails to gather enough knowledge about the new businesses, resulting in ignoring them during recommendations. This scenario is popularly known as a cold-start POI problem. Users never get recommendations of the new businesses in a city even though they can be relevant to a user. Also, from a business owner’s perspective, such a recommendation strategy does not help its reachability among users. Therefore, it is important for a recommender system to remain updated with new businesses in a city and ensure that all relevant POIs are recommended to a user irrespective of their lifetime. A POI recommendation approach is proposed in this work that can effectively handle the new businesses, or the cold-start POI problem, in a city. We crowdsource descriptions of cold-start POIs from various online social networks. The reviews of users are exploited here to learn the inherent features at the existing POIs and the new crowdsourced POIs. Finally, the proposed approach recommends top-K POIs consisting of the existing and new POIs. We perform experiments on the real-world Yelp dataset, which is one of the largest available data resources containing details on a wide range of businesses, users, and reviews. The proposed approach is compared with four existing POI recommendation approaches. The obtained results show that our approach outperforms others in handling cold-start POIs.

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    cover image ACM Transactions on the Web
    ACM Transactions on the Web  Volume 14, Issue 4
    November 2020
    147 pages
    ISSN:1559-1131
    EISSN:1559-114X
    DOI:10.1145/3414043
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 25 August 2020
    Accepted: 01 June 2020
    Revised: 01 December 2019
    Received: 01 May 2018
    Published in TWEB Volume 14, Issue 4

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    Author Tags

    1. Recommender systems
    2. Yelp network
    3. clustering
    4. crowdsourcing

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    • (2024)Design and Implementation of Attention-Based CR System in the Context of Big DataIEEE Access10.1109/ACCESS.2024.337852112(58639-58650)Online publication date: 2024
    • (2024)Teaching content recommendations in music appreciation courses via graph embedding learningInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02123-515:9(3847-3862)Online publication date: 16-May-2024
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    • (2023)MEGA: Meta-Graph Augmented Pre-Training Model for Knowledge Graph CompletionACM Transactions on Knowledge Discovery from Data10.1145/361737918:1(1-24)Online publication date: 16-Oct-2023
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