Towards serendipity for content-based recommender systems

Nur Izyan Yasmin Saat, Shahrul Azman Mohd Noah, Masnizah Mohd

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Recommender systems are intelligent applications build to predict the rating or preference that a user would give to an item. One of the fundamental recommendation methods in the content-based method that predict ratings by exploiting attributes about the users and items such as users' profile and textual content of items. A current issue faces by recommender systems based on this method is that the systems seem to recommend too similar items to what users have known. Thus, creating over-specialisation issues, in which a self-referential loop is created that leaves user in their own circle of finding and never get expose to new items. In order for these systems to be of significance used, it is important that not only relevant items been recommender, but the items must be also interesting and serendipitous. Having a serendipitous recommendation let users explore new items that they least expect. This has resulted in the issues of serendipity in recommender systems. However, it is difficult to define serendipity because in recommender system, there is no consensus definition for this term. Most of researchers define serendipity based on their research purposes. From the reviews, majority shows that unexpected as the important aspect in defining serendipity. Thus, in this paper, we aim to formally define the concept of serendipity in recommender systems based on the literature work done. We also reviewed few approaches that apply serendipity in the content-based methods in recommendation. Techniques that used Linked Open Data (LOD) approaches seems to be a good candidate to find relevant, unexpected and novel item in a large dataset.

Original languageEnglish
Pages (from-to)1762-1769
Number of pages8
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Volume8
Issue number4-2
Publication statusPublished - 1 Jan 2018

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Keywords

  • Content-based recommendation
  • Recommender systems
  • Serendipity

ASJC Scopus subject areas

  • Computer Science(all)
  • Agricultural and Biological Sciences(all)
  • Engineering(all)

Cite this

Towards serendipity for content-based recommender systems. / Saat, Nur Izyan Yasmin; Mohd Noah, Shahrul Azman; Mohd, Masnizah.

In: International Journal on Advanced Science, Engineering and Information Technology, Vol. 8, No. 4-2, 01.01.2018, p. 1762-1769.

Research output: Contribution to journalArticle

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