Profile reliability to improve recommendation in social-learning context

Abstract

Purpose

Generally, the user requires customized information reflecting his/her current needs and interests that are stored in his/her profile. There are many sources which may provide beneficial information to enrich the user’s interests such as his/her social network for recommendation purposes. The proposed approach rests basically on predicting the reliability of the users’ profiles which may contain conflictual interests. The paper aims to discuss this issue.

Design/methodology/approach

This approach handles conflicts by detecting the reliability of neighbors’ profiles of a user. The authors consider that these profiles are dependent on one another as they may contain interests that are enriched from non-reliable profiles. The dependency relationship is determined between profiles, each of which contains interests that are structured based on k-means algorithm. This structure takes into consideration not only the evolutionary aspect of interests but also their semantic relationships.

Findings

The proposed approach was validated in a social-learning context as evaluations were conducted on learners who are members of Moodle e-learning system and Delicious social network. The quality of the created interest structure is assessed. Then, the result of the profile reliability is evaluated. The obtained results are satisfactory. These results could promote recommendation systems as the selection of interests that are considered of enrichment depends on the reliability of the profiles where they are stored.

Research limitations/implications

Some specific limitations are recorded. As the quality of the created interest structure would evolve in order to improve the profile reliability result. In addition, as Delicious is used as a main data source for the learner’s interest enrichment, it was necessary to obtain interests from other sources, such as e-recruitement systems.

Originality/value

This research is among the pioneer papers to combine the semantic as well as the hierarchical structure of interests and conflict resolution based on a profile reliability approach.

Citation

Zayani, C.A., Ghorbel, L., Amous, I., Mezghanni, M., Péninou, A. and Sèdes, F. (2020), "Profile reliability to improve recommendation in social-learning context", Online Information Review, Vol. 44 No. 2, pp. 433-454. https://doi.org/10.1108/OIR-02-2017-0068