Abstract
Recently. recommender systems have become a very crucial application in the online market and e-commerce as users are often astounded by choices and preferences and they need help finding what the best they are looking for. Recommender systems have proven to overcome information overload issues in the retrieval of information, but still suffer from persistent problems related to cold-start and data sparsity. On the flip side, sentiment analysis technique has been known in translating text and expressing user preferences. It is often used to help online businesses to observe customers' feedbacks on their products as well as try to understand customer needs and preferences. However, the current solution for embedding traditional sentiment analysis in recommender solutions seems to have limitations when involving multiple domains. Therefore, an issue called domain sensitivity should be addressed. In this paper, a sentiment-based model with contextual information for recommender system was proposed. A novel solution for domain sensitivity was proposed by applying a contextual information sentiment-based model for recommender systems. In evaluating the contributions of contextual information in sentiment-based recommendations, experiments were divided into standard rating model, standard sentiment model and contextual information model. Results showed that the proposed contextual information sentiment-based model illustrates better performance as compared to the traditional collaborative filtering approach.
Highlights
Collaborative filtering technique is one of the most widely and applicable techniques used by recommender systems
The results show that the value of root-mean-square error (RMSE) and mean absolute error (MAE) for sentimentCF model gives a minimal error compared to ratingCF for both domains
To make it more domain specific and realistic, the experiments have been performed in different domains which is Amazon Tvs and Films and Amazon Electronic Products
Summary
Collaborative filtering technique is one of the most widely and applicable techniques used by recommender systems. The technique filters information by exploiting the rating of other similar users. The main concept of collaborative recommendation approaches is to exploit information about the past behaviour or the opinions of an existing user’s community for predicting which items the current user of the system will most probably like [1]. Opinions that have currently been exploited by conventional recommender systems are usually in the form of numerical ratings. Collaborative filtering techniques perform well when there is sufficient rating information [2]. Their effectiveness deteriorates when there exist insufficient
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