Recommendation system for web article based on association rules and topic modelling

Guntur Budi Herwanto, Annisa Maulida Ningtyas

Abstract


The World Wide Web is now the primary source for information discovery. A user visits websites that provide information and browse on the particular information in ac-cordance with their topic interest. Through the navigational process, visitors often had to jump over the menu to find the right content. Recommendation system can help the visitors to find the right content immediately. In this study, we propose a two-level recommendation system, based on association rule and topic similarity. We generate association rule by applying Apriori algorithm. The dataset for association rule mining is a session of topics that made by combining the result of sessionization and topic modeling. On the other hand, the topic similarity made by comparing the topic proportion of web article. This topic proportion inferred from the Latent Dirichlet Allocation (LDA). The results show that in our dataset there are not many interesting topic relations in one session. This result can be resolved, by utilizing the second level of recommendation by looking into the article that has the similar topic.

Keywords


Website; Recommendation; Topic Modelling; Latent Dirichlet Allocation; Association Rule

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References


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