research-article

Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation

Authors:

Jiawei HanAuthors Info & Claims

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 1245 - 1254

https://doi.org/10.1145/3097983.3098094

Published: 04 August 2017 Publication History

Abstract

Recommender system is one of the most popular data mining topics that keep drawing extensive attention from both academia and industry. Among them, POI (point of interest) recommendation is extremely practical but challenging: it greatly benefits both users and businesses in real-world life, but it is hard due to data scarcity and various context. While a number of algorithms attempt to tackle the problem w.r.t. specific data and problem settings, they often fail when the scenarios change. In this work, we propose to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs. To enable such a framework, we develop PACE (Preference And Context Embedding), a deep neural architecture that jointly learns the embeddings of users and POIs to predict both user preference over POIs and various context associated with users and POIs. We show that PACE successfully bridges CF (collaborative filtering) and SSL by generalizing the de facto methods matrix factorization of CF and graph Laplacian regularization of SSL. Extensive experiments on two real location-based social network datasets demonstrate the effectiveness of PACE.

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Index Terms

Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Semantic networks
    2. Natural language processing
  2. Machine learning
2. Information systems

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cover image ACM Conferences

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2017

2240 pages

ISBN:9781450348874

DOI:10.1145/3097983

General Chairs:
Stan Matwin
Dalhousie University
,
Shipeng Yu
LinkedIn
,
Faisal Farooq
IBM

Copyright © 2017 ACM.

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Published: 04 August 2017

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KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 13 - 17, 2017

NS, Halifax, Canada

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KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

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https://doi.org/10.1109/TFUZZ.2023.3300370
Sun Y(2024)TransTARec: Time-Adaptive Translating Embedding Model for Next POI Recommendation2024 5th International Conference on Computer Engineering and Application (ICCEA)10.1109/ICCEA62105.2024.10603711(647-651)Online publication date: 12-Apr-2024
https://doi.org/10.1109/ICCEA62105.2024.10603711
Ammar KInoubli WZghal SBorji ANguifo E(2024)Trans-Trip: Translation-based embedding with Triplets for Heterogeneous Graphs.Procedia Computer Science10.1016/j.procs.2023.10.098225:C(1104-1113)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1016/j.procs.2023.10.098
Kumar AJain DMallik AKumar S(2024)Modified node2vec and attention based fusion framework for next POI recommendationInformation Fusion10.1016/j.inffus.2023.101998101(101998)Online publication date: Jan-2024
https://doi.org/10.1016/j.inffus.2023.101998
Halder SLim KChan JZhang X(2024)A survey on personalized itinerary recommendation: From optimisation to deep learningApplied Soft Computing10.1016/j.asoc.2023.111200152(111200)Online publication date: Feb-2024
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Acharya MMohbey K(2024)Exploring the evolution, progress, and future of point-of-interest recommendation over location-based social network: a comprehensive reviewGeoInformatica10.1007/s10707-024-00531-xOnline publication date: 28-Oct-2024
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Yao DGuo FWen ZGuo YCheng PHe YBi J(2024)Trajectory Completion via Context-Guided Neural Filtering and EncodingDatabase Systems for Advanced Applications10.1007/978-981-97-5552-3_1(3-19)Online publication date: 1-Oct-2024
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Liu X(2023)POI Recommendation Model Using Multi-Head Attention in Location-Based Social Network Big DataInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.31814216:2(1-16)Online publication date: 17-Feb-2023
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Chang WSun DDu Q(2023)Intelligent Sensors for POI Recommendation Model Using Deep Learning in Location-Based Social Network Big DataSensors10.3390/s2302085023:2(850)Online publication date: 11-Jan-2023
https://doi.org/10.3390/s23020850
Abinaya SIndira KKarthiga SRajasenbagam T(2023)Time Cluster Personalized Ranking Recommender System in Multi-CloudMathematics10.3390/math1106130011:6(1300)Online publication date: 8-Mar-2023
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