SPR SYNTHESISED: AN EFFECTIVE COLLABORATIVE BASED RECOMMENDATION SYSTEM BASED ON USER SENTIMENTS

In the current era of electronic and mobile commerce, recommendation system acts as a crucial one in suggesting the products / services to the consumers as well as the users based on their interests. There exist two forms of such systems viz. content based and collaborative based. Among that content-based technique is suitable to suggest the products belonging to the same category. In that extent, collaborative based technique may be a right choice that suits all the sectors. In collaborative techniques, the recommendations may be made on the user based or the item-based similarity. For the similarity calculation, typically the ratings given by the user are used. In some situation, the ratings may be the contradictory or it may not convey the user’s opinion entirely. To prevent this, the proposed study integrates the sentiment analysis with the collaborative-based recommendation systems. Here, along with the rating, polarity of the opinion which indicates the strength of the sentence and the user sentiment is also exploited to compute the similarity score. In such practice, the recommendations given by the system will be more accurate. The results shown that, the accuracy of the system will be more for the item-based filtering when polarity or the synthesis of polarity and rating is utilized for similarity calculation. For the user-based filtering technique, the mixture of polarity, rating and the user sentiment will be the efficient one.