skip to main content
10.1145/3341161.3342869acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

Phrase-guided attention web article recommendation for next clicks and views

Published: 15 January 2020 Publication History

Abstract

As deep learning models are getting popular, upgrading the retrieval-based content recommendation system to the learning-based system is highly demanded. However, efficiency is a critical issue. For article recommendation, an effective neural network which generates a good representation of the article content could prove useful. Hence, we propose PGA-Recommender, a phrase-guided article recommendation model which mimics the process of human behavior - first browsing, then guided by key phrases, and finally aggregating the gleaned information. As this can be performed independently offline, it is thus compatible with current commercial retrieval-based (keyword-based) article recommender systems. A total of six months of real logs - from Apr 2017 to Sep 2017 - were used for experiments. Results show that PGA-Recommender outperforms different state-of-the-art schemes including session-, collaborative filter-, and content-based recommendation models. Moreover, it suggests a diverse mix of articles while maintaining superior performance in terms of both click and view predictions. The results of A/B tests show that simply using the backward version of PGA-Recommender yields 40% greater click-through rates as compared to the retrieval-based system when deployed to a language of which we have zero knowledge.

References

[1]
K.-J. Oh, W.-J. Lee, C.-G. Lim, and H.-J. Choi, "Personalized news recommendation using classified keywords to capture user preference," in Advanced Communication Technology (ICACT), 2014 16th International Conference on. IEEE, 2014, pp. 1283--1287.
[2]
P. Covington, J. Adams, and E. Sargin, "Deep neural networks for youtube recommendations," in Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016, pp. 191--198.
[3]
A. Van den Oord, S. Dieleman, and B. Schrauwen, "Deep content-based music recommendation," in Advances in neural information processing systems, 2013, pp. 2643--2651.
[4]
J. Bennett, S. Lanning et al., "The netflix prize," in Proceedings of KDD cup and workshop, vol. 2007. New York, NY, USA, 2007, p. 35.
[5]
V. Sembium, R. Rastogi, L. Tekumalla, and A. Saroop, "Bayesian models for product size recommendations," in Proceedings of the 2018 World Wide Web Conference, ser. WWW '18. International World Wide Web Conferences Steering Committee, 2018, pp. 679--687.
[6]
X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, "Neural collaborative filtering," in Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2017, pp. 173--182.
[7]
R. Devooght and H. Bersini, "Long and short-term recommendations with recurrent neural networks," in Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization. ACM, 2017, pp. 13--21.
[8]
D. Ben Shimon, M. Friedman, J. Hoerle, A. Tsikinovsky, R. Gude, and R. Aluchanov, "Deploying recommender system for the masses," in Proceedings of the companion publication of the 19th international conference on Intelligent User Interfaces. ACM, 2014, pp. 1--4.
[9]
A. Conneau, H. Schwenk, L. Barrault, and Y. Lecun, "Very deep convolutional networks for text classification," in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, vol. 1, 2017, pp. 1107--1116.
[10]
W.-F. Chen and L.-W. Ku, "Utcnn: a deep learning model of stance classificationon on social media text," arXiv preprint arXiv:1611.03599, 2016.
[11]
X. Zhang, J. Zhao, and Y. LeCun, "Character-level convolutional networks for text classification," in Advances in neural information processing systems, 2015, pp. 649--657.
[12]
Y. Kim, Y. Jernite, D. Sontag, and A. M. Rush, "Character-aware neural language models." in AAAI, 2016, pp. 2741--2749.
[13]
J. Liu, J. Shang, C. Wang, X. Ren, and J. Han, "Mining quality phrases from massive text corpora," in Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data - SIGMOD 15.
[14]
J. Shang, J. Liu, M. Jiang, X. Ren, C. R. Voss, and J. Han, "Automated phrase mining from massive text corpora," CoRR, 2017.
[15]
Y. Koren, R. Bell, and C. Volinsky, "Matrix factorization techniques for recommender systems," Computer, vol. 42, no. 8, 2009.
[16]
G. Linden, B. Smith, and J. York, "Amazon. com recommendations: Item-to-item collaborative filtering," IEEE Internet computing, vol. 7, pp. 76--80, 2003.
[17]
T. Bansal, D. Belanger, and A. McCallum, "Ask the gru: Multi-task learning for deep text recommendations," in Proceedings of the 10th ACM Conference on Recommender Systems, ser. RecSys '16. New York, NY, USA: ACM, 2016, pp. 107--114.
[18]
A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock, "Methods and metrics for cold-start recommendations," in Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2002, pp. 253--260.
[19]
M. Elahi, F. Ricci, and N. Rubens, "A survey of active learning in collaborative filtering recommender systems," Computer Science Review, vol. 20, pp. 29--50, 2016.
[20]
N. Rubens, M. Elahi, M. Sugiyama, and D. Kaplan, "Active learning in recommender systems," in Recommender systems handbook. Springer, 2015, pp. 809--846.
[21]
M. J. Pazzani and D. Billsus, "Content-based recommendation systems," in The adaptive web. Springer, 2007, pp. 325--341.
[22]
Q. Le and T. Mikolov, "Distributed representations of sentences and documents," in International Conference on Machine Learning, 2014, pp. 1188--1196.
[23]
T. Chen, L. Hong, Y. Shi, and Y. Sun, "Joint text embedding for personalized content-based recommendation," arXiv preprint arXiv:1706.01084, 2017.
[24]
Learning Semantic Representations Using Convolutional Neural Networks for Web Search. WWW 2014, 2014.
[25]
X. Chenyan, D. Zhuyun, C. Jamie, L. Zhiyuan, and P. Russell, "End-to-end neural ad-hoc ranking with kernel pooling," CoRR, vol. abs/1706.06613, 2017.
[26]
W. Shengxian, L. Yanyan, G. Jiafeng, X. Jun, P. Liang, and C. Xueqi, "A deep architecture for semantic matching with multiple positional sentence representations," CoRR, vol. abs/1511.08277, 2015.
[27]
B. Mitra, F. Diaz, and N. Craswell, "Learning to match using local and distributed representations of text for web search," in Proceedings of the 26th International Conference on World Wide Web, ser. WWW '17, 2017, pp. 1291--1299.
[28]
P. Liang, L. Yanyan, G. Jiafeng, X. Jun, W. Shengxian, and C. Xueqi, "Text matching as image recognition," CoRR, 2016.
[29]
J. Ni, Z. C. Lipton, S. Vikram, and J. McAuley, "Estimating reactions and recommending products with generative models of reviews," in Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, 2017, pp. 783--791.
[30]
S. Debnath, N. Ganguly, and P. Mitra, "Feature weighting in content based recommendation system using social network analysis," in Proceedings of the 17th international conference on World Wide Web. ACM, 2008, pp. 1041--1042.
[31]
R. He and J. McAuley, "Fusing similarity models with markov chains for sparse sequential recommendation," in Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016, pp. 191--200.
[32]
S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme, "Factorizing personalized markov chains for next-basket recommendation," in Proceedings of the 19th international conference on World wide web. ACM, 2010, pp. 811--820.
[33]
F. Aiolli, "Efficient top-n recommendation for very large scale binary rated datasets," in Proceedings of the 7th ACM conference on Recommender systems. ACM, 2013, pp. 273--280.
[34]
Y. Zhang, "Incorporating phrase-level sentiment analysis on textual reviews for personalized recommendation," in Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, ser. WSDM '15. ACM, 2015, pp. 435--440.
[35]
W. Wu, H. Wang, T. Liu, and S. Ma, "Phrase-level self-attention networks for universal sentence encoding," in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018, pp. 3729--3738.
[36]
W.-C. Kang and J. McAuley, "Self-attentive sequential recommendation," 2018.
[37]
L. Zheng, V. Noroozi, and P. S. Yu, "Joint deep modeling of users and items using reviews for recommendation," in Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ACM, 2017, pp. 425--434.
[38]
T. Rocktäschel, E. Grefenstette, K. M. Hermann, T. Kočiskỳ, and P. Blunsom, "Reasoning about entailment with neural attention," arXiv preprint arXiv:1509.06664, 2015.
[39]
Y. Kim, "Convolutional neural networks for sentence classification," arXiv preprint arXiv:1408.5882, 2014.
[40]
A. Ahmed, C. H. Teo, S. Vishwanathan, and A. Smola, "Fair and balanced: Learning to present news stories," in Proceedings of the fifth ACM international conference on Web search and data mining. ACM, 2012, pp. 333--342.
[41]
D. Doychev, A. Lawlor, R. Rafter, and B. Smyth, "An analysis of recommender algorithms for online news," in CLEF 2014 Conference and Labs of the Evaluation Forum: Information Access Evaluation Meets Multilinguality, Multimodality and Interaction, 15--18 September 2014, Sheffield, United Kingdom, 2014, pp. 177--184.
[42]
J. Pennington, R. Socher, and C. Manning, "Glove: Global vectors for word representation," in Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 2014, pp. 1532--1543.
[43]
E. M. Voorhees et al., "The trec-8 question answering track report." in Trec, vol. 99, 1999, pp. 77--82.
[44]
V. Kumar, D. Khattar, S. Gupta, M. Gupta, and V. Varma, "Deep neural architecture for news recommendation," 2017.

Cited By

View all
  • (2022)VICTOR: An Implicit Approach to Mitigate Misinformation via Continuous Verification ReadingProceedings of the ACM Web Conference 202210.1145/3485447.3512246(3511-3519)Online publication date: 25-Apr-2022
  • (2021)All the WiserProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441696(1069-1072)Online publication date: 8-Mar-2021
  1. Phrase-guided attention web article recommendation for next clicks and views

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
    August 2019
    1228 pages
    ISBN:9781450368681
    DOI:10.1145/3341161
    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]

    Sponsors

    In-Cooperation

    • IEEE CS

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 15 January 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. article recommendation
    2. attention mechanism
    3. commercial systems
    4. deep learning model
    5. phrase mining
    6. retrieval-based

    Qualifiers

    • Research-article

    Funding Sources

    • cacaFly

    Conference

    ASONAM '19
    Sponsor:

    Acceptance Rates

    ASONAM '19 Paper Acceptance Rate 41 of 286 submissions, 14%;
    Overall Acceptance Rate 116 of 549 submissions, 21%

    Upcoming Conference

    KDD '25

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)3
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 20 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2022)VICTOR: An Implicit Approach to Mitigate Misinformation via Continuous Verification ReadingProceedings of the ACM Web Conference 202210.1145/3485447.3512246(3511-3519)Online publication date: 25-Apr-2022
    • (2021)All the WiserProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441696(1069-1072)Online publication date: 8-Mar-2021

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media