Modeling User Behaviour in Research Paper Recommendation System
- URL: http://arxiv.org/abs/2107.07831v1
- Date: Fri, 16 Jul 2021 11:31:03 GMT
- Title: Modeling User Behaviour in Research Paper Recommendation System
- Authors: Arpita Chaudhuri, Debasis Samanta, Monalisa Sarma
- Abstract summary: A user intention model is proposed based on deep sequential topic analysis.
The model predicts a user's intention in terms of the topic of interest.
The proposed approach introduces a new road map to model a user activity suitable for the design of a research paper recommendation system.
- Score: 8.980876474818153
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: User intention which often changes dynamically is considered to be an
important factor for modeling users in the design of recommendation systems.
Recent studies are starting to focus on predicting user intention (what users
want) beyond user preference (what users like). In this work, a user intention
model is proposed based on deep sequential topic analysis. The model predicts a
user's intention in terms of the topic of interest. The Hybrid Topic Model
(HTM) comprising Latent Dirichlet Allocation (LDA) and Word2Vec is proposed to
derive the topic of interest of users and the history of preferences. HTM finds
the true topics of papers estimating word-topic distribution which includes
syntactic and semantic correlations among words. Next, to model user intention,
a Long Short Term Memory (LSTM) based sequential deep learning model is
proposed. This model takes into account temporal context, namely the time
difference between clicks of two consecutive papers seen by a user. Extensive
experiments with the real-world research paper dataset indicate that the
proposed approach significantly outperforms the state-of-the-art methods.
Further, the proposed approach introduces a new road map to model a user
activity suitable for the design of a research paper recommendation system.
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