A novel approach to hybrid recommendation systems based on association rules mining for content recommendation in asynchronous discussion groups
Recommender systems have been developed in variety of domains, including asynchronous discussion group which is one of the most interesting ones. Due to the information overload and its varieties in discussion groups, it is difficult to draw out the relevant information. Therefore, recommender systems play an important role in filtering and customizing the desired information. Nowadays, collaborative and content-based filtering are the most adopted techniques being utilized in recommender systems. The collaborative filtering technique recommends items based on liked-mind users opinions and users preferences. Alternatively, the aim of the content-based filtering technique is the identification of items which are similar to those a user has preferred in past. To overcome the drawbacks of the aforementioned techniques, a hybrid recommender system combines two or more recommendation techniques to obtain more accuracy. The most important achievement of this study is to present a novel approach in hybrid recommendation systems, which identifies the user similarity neighborhood from implicit information being collected in a discussion group. In the proposed system, initially the association rules mining technique is applied to discover the similar users, and then the related posts are recommended to them. To select the appropriate contents in the transacted posts, it is necessary to focus on the concepts rather than the key words. Therefore, to locate the semantic related concepts Word Sense Disambiguation strategy based on WordNet lexical database is exploited. The experiments carried out on the discussion group datasets proved a noticeable improvement on the accuracy of useful posts recommended to the users in comparison to content-based and the collaborative filtering techniques as well.
abbreviations: wsd word sense disambiguation cf collaborative filtering cbf content-based filtering cscl computer supported collaborative learning hf hybrid filtering mae mean absolute error