Classifying Items with the Rating Values 3 Using Text Reviews to Improve the Recommendation Accuracy in the Collaborative Filtering Approach

Collaborative filtering is a common aspect recently used in e-commerce to increase sales and overcome information overload. One significant limitation in collaborative filtering is data sparseness. Several studies have proposed alleviating this issue by utilizing extra information such as users’ reviews. However, the researchers have been concerned with using entire reviews irrespective of the users’ ratings. This requires extra processing time and might perplex the recommendation decision. In this study, after analyzing the users’ ratings and reviews, it was noted that when the rating values are 4 or 5, most of the reviews accompanying these ratings are positive. Otherwise, when the ratings are 1 or 2, the reviews are negative. The rating value 3, however, is a confusing one. It, consequently, might mislead users’ preferences. Thus, two points are highlighted. First, analyzing and classifying the entire users’ reviews is time-consuming. Second, directly considering the rating value 3 as a like or dislike decreases the model’s accuracy. Hence, this method utilizes the users’ reviews only in case the rating value is 3. Three scenarios were formulated and fitted to this case. The first scenario considers the rating value 3 as a like. The second scenario regards the same value as a dislike. The third one classifies the users’ reviews into positive or negative regarding their content. Additionally, a probabilistic method was applied to compute the recommendation list. The proposed method was implemented on two standard datasets. Eventually, the results of the third scenario outperformed the other two in terms of accuracy.