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Solving the Cold-Start Problem in Recommender Systems Using Contextual Information in Arabic from Calendars


Cold-start problem, which is the inability to make accurate recommendations due to the unavailability of enough information about user’s preferences, is one of the challenges of recommender systems. Contextual information was widely used to solve this problem in English language. However, Arabic language is the sixth-most-spoken language in the world, Arabic text in calendars has not been used to find user’s interests in recommender systems. Our work utilizes events from users’ calendars that are written in Arabic. We first build a multi-class text classifier to classify calendar events. The classifier is trained on Wikipedia data and validated using 10-fold cross-validation. Our classifier reached an accuracy of 76.72%. We investigated the reasons for our results and we identified factors that have high impacts on them. These factors including, but not limited to, English events written in Arabic letters and Arabic names. Finally, we highlighted some research directions to tackle the existing limitations in order to improve the presented work.

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Correspondence to Nuha Alghamdi.

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Alghamdi, N., Assiri, F. Solving the Cold-Start Problem in Recommender Systems Using Contextual Information in Arabic from Calendars. Arab J Sci Eng 45, 10939–10947 (2020).

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  • Recommender systems
  • Cold-start
  • Word embeddings
  • FastText
  • Multi-class classification
  • Arabic text classification