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Incremental probabilistic latent semantic analysis for automatic question recommendation

Published: 23 October 2008 Publication History

Abstract

With the fast development of web 2.0, user-centric publishing and knowledge management platforms, such as Wiki, Blogs, and Q & A systems attract a large number of users. Given the availability of the huge amount of meaningful user generated content, incremental model based recommendation techniques can be employed to improve users' experience using automatic recommendations. In this paper, we propose an incremental recommendation algorithm based on Probabilistic Latent Semantic Analysis (PLSA). The proposed algorithm can consider not only the users' long-term and short-term interests, but also users' negative and positive feedback. We compare the proposed method with several baseline methods using a real-world Question & Answer website called Wenda. Experiments demonstrate both the effectiveness and the efficiency of the proposed methods.

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    cover image ACM Conferences
    RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems
    October 2008
    348 pages
    ISBN:9781605580937
    DOI:10.1145/1454008
    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: 23 October 2008

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    Author Tags

    1. incremental learning
    2. plsa
    3. recommendation system

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    RecSys08: ACM Conference on Recommender Systems
    October 23 - 25, 2008
    Lausanne, Switzerland

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    • (2024)Revisiting Probabilistic Latent Semantic Analysis: Extensions, Challenges and InsightsTechnologies10.3390/technologies1201000512:1(5)Online publication date: 3-Jan-2024
    • (2024)Investigating the optimal number of topics by advanced text-mining techniques: Sustainable energy researchEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108877136(108877)Online publication date: Oct-2024
    • (2023)SSC-CF: Semantic similarity and clustering-based collaborative filtering for expert recommendation in community question answering websitesInternational Journal of Information Technology10.1007/s41870-023-01458-615:8(4243-4257)Online publication date: 28-Sep-2023
    • (2022)QoS prediction for smart service management and recommendation based on the location of mobile usersNeurocomputing10.1016/j.neucom.2021.02.107471:C(12-20)Online publication date: 30-Jan-2022
    • (2022)Natural language why-question in Business Intelligence applications: model and recommendation approachCluster Computing10.1007/s10586-022-03593-425:6(3875-3898)Online publication date: 18-May-2022
    • (2021)LDA-based term profiles for expert finding in a political settingJournal of Intelligent Information Systems10.1007/s10844-021-00636-xOnline publication date: 23-Mar-2021
    • (2020)Effectiveness of Machine Learning Approaches Towards Credibility Assessment of Crowdfunding Projects for Reliable RecommendationsApplied Sciences10.3390/app1024906210:24(9062)Online publication date: 18-Dec-2020
    • (2020)Latent Semantic Feature ExtractionFeature Learning and Understanding10.1007/978-3-030-40794-0_2(13-29)Online publication date: 4-Apr-2020
    • (2019)A Novel Recommender System using Hidden Bayesian Probabilistic Model based Collaborative Filtering2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852261(1-8)Online publication date: Jul-2019
    • (2019)Towards Decisional Natural Language Why-Question Recommendation Approach in Business Intelligence Context2019 International Conference on Networking and Advanced Systems (ICNAS)10.1109/ICNAS.2019.8807856(1-6)Online publication date: Jun-2019
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