Manara - Qatar Research Repository
10.1109_access.2023.3327271.pdf (3.07 MB)

Enhancing Performance of Movie Recommendations Using LSTM With Meta Path Analysis

Download (3.07 MB)
journal contribution
submitted on 2023-12-11, 10:46 and posted on 2023-12-14, 06:51 authored by Zulfiqar Ali, Asif Muhammad, Ahmad Sami Al-Shamayleh, Kashif Naseer Qureshi, Wagdi Alrawagfeh, Adnan Akhunzada

Movie recommendation algorithms play an important role in assisting consumers in identifying films that match their likes. Deep Learning, particularly Long Short-Term Memory (LSTM) networks, has shown substantial promise in collecting sequential patterns to improve movie recommendations among the different techniques used for this purpose. Long Short-Term Memory-Inter Intra-metapath Aggregation (LSTM-IIMA) in movie recommendation systems is proposed in this study, with a specific focus on incorporating intra and inter-metapath analysis. The intra-metapath analysis investigates interactions within a single metapath, whereas the inter-metapath analysis investigates links between numerous metapaths. Intra and inter-metapath analyses are used in the LSTM-based movie recommendation system LSTM-IIMA to capitalise on these rich linkages. Each metapath sequence records the dependencies of a user’s interactions with films and other things. The LSTM architecture has been modified to handle these metapath sequences, processing them to record temporal dependencies and entity interactions. To optimize the parameters and minimize prediction errors, the model is trained using supervised learning techniques. To measure the quality and usefulness of the recommendations, the LSTM-IIMA evaluation incorporates metrics such as precision, recall, ablation analysis, time efficiency and Area Under the Curve (AUC). The performance of the system is compared to that of alternative recommendation techniques HAN and MAGNN. Overall, incorporating intra and inter-metapath analysis into the LSTM-IIMA improves its ability to capture complex linkages and dependencies between movies, users, and other things.

Other Information

Published in: IEEE Access
See article on publisher's website:


Open Access funding provided by the Qatar National Library.



  • English



Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Institution affiliated with

  • University of Doha for Science and Technology
  • College of Computing and Information Technology - UDST