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Combining Latent Factor Model for Dynamic Recommendations in Community Question Answering Forums.

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Area of Science:

  • Information Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Community Question Answering (CQA) platforms facilitate knowledge sharing but require effective methods to connect users with relevant answerers.
  • Existing systems often struggle to dynamically capture evolving user interests, impacting recommendation accuracy.

Purpose of the Study:

  • To propose a novel dynamic feature representation for latent user attributes to improve user profiling in CQA systems.
  • To develop a recommendation model that incorporates incremental learning for near real-time user interest updates.

Main Methods:

  • Utilized Latent Dirichlet Allocation (LDA) for topic modeling to extract latent features from user data.
  • Developed a user profiling segmentation technique based on these latent features.
  • Implemented incremental learning to continuously update user interest profiles.

Main Results:

  • The proposed model demonstrated superior recommendation quality in CQA forums compared to existing methods.
  • Evaluated using metrics like mean average precision, recall, discounted cumulative gain, and mean reciprocal rank.
  • Achieved improved accuracy in routing questions to relevant answerers.

Conclusions:

  • Dynamic feature representation and incremental learning significantly enhance the quality of recommendations in CQA systems.
  • The proposed approach offers a promising direction for future research in personalized CQA services.
  • Effective user profiling is crucial for improving the usability and efficiency of knowledge-sharing platforms.