A Hybrid Recommender System Applicable for Asynchronous Discussion Groups

Recommender systems have been developed in different domains. One of the important one is information retrieval. In this domain, various systems and architectures have been proposed, that some of them are only based on collaborative filtering and some others benefits from content-based filtering techniques. In other words, these systems either consider the recommendable item information or user’s profile information. But hybrid recommender systems have been considered not as much of other techniques. In hybrid recommender systems the performance is improved by combining existing techniques, and consequently overcoming the weakness of each of the mentioned techniques. In this paper a new hybrid recommender system for asynchronous discussion groups is presented. Since asynchronous discussion groups play an important role in supporting the collaborative learning methodology, extracting knowledge from discussions is a critical issue in e-learning. Recommender systems could help learners to achieve new knowledge by means of customized information delivered to them. The proposed system is used to recommend useful information from a huge amount of transcripts in group. Considering different recommendation parameters such as user’s past liking, information associated to the content of transcripts, the proposed system is compared with some of the recommender systems in the view of usefulness of suggested recommendations. The main difference between the proposed system and others is in utilizing important parameters related to discussion groups such as "application domain of transcript information". This parameter is useful for any learner who is looking for special information in transcripts such as a programming code, a mathematical formula.

کلیدواژه ها:

Recommender System ، Asynchronous Discussion Group ، Collaborative Filtering ، Content-Based Filtering ، Hybrid Recommender System ، Transcript