Unbiasing Review Ratings with Tendency Based Collaborative Filtering (2020)

Abstract: User-generated contents’ score-based prediction and item recommendation has become an inseparable part of the online recommendation systems. The ratings allow people to express their opinions and may affect the market value of items and consumer confidence in e-commerce decisions. A major problem with the models designed for user review prediction is that they unknowingly neglect the rating bias occurring due to personal user bias preferences. We propose a tendency-based approach that models the user and item tendency for score prediction along with text review analysis with respect to ratings.

## introduction our society is increasingly relying on the digitized, aggregated opinions of others, which may be biased and easily manipulated while making decisions. online reviews typically have a distribution of opinions, i.e., many are extremely positive/negative, and a few are moderate opinions. however, numerous online reviews suffer from the rating bias problem. the opinions of individual reviewers may be affected when a person allows their preformed personal bias to affect the evaluation of another. some users are very generous and do not rate an item with less than a 3 or 4 (on a scale of 5), thus introducing a positive bias in the scores. in contrast, some users do not go beyond a 1 or 2, thus introducing a negative bias. hence, the reviews can affect the product's market positively or negatively, regardless of the actual product performance. the rating bias problem was earlier studied as user bias problem by @xcite ; @xcite ; @xcite ; @xcite . in this paper, we focus on extending a simple normalization approach by @xcite , as shown in figure 1 , to mitigate user or item bias. figure 1 : the user item bias problem @xcite instead of using mean and standard deviation-based review rating normalization over all reviews @xcite , we perform user/item-specific review rating normalization in order to identify user/item-specific bias in ratings. that is, we will use an intuitive tendency based approach, a wellstudied concept in the recommender systems literature @xcite , that estimates the bias for all user-item pairs based on user and item means/tendencies and predicts the unbiased score. furthermore, our approach can also predict the rating of new review pairs of users and items, assuming the users or the items have a history of reviews with their corresponding ratings (i.e., no cold start setting). therefore, we refer to @xcite 2011 ), based on user and item means and tendencies to underscore and mitigate the rating bias problem in reviews. using this framework, we obtain unbiased ratings for user-item pairs. in section (1), we provided a brief introduction to the problem statement. the remaining parts of the paper are organized as follows: in section (2) we explain our approach to predict ratings for new user-item pairs. in section (3), we explain the experimental details. we then move on to our results and analysis in section (4). next, we discuss the related work in (5), followed by conclusions and future work in section (6). we have also released the source code along with the paper. foot_0 ## rating prediction for new user-items we propose a user-specific statistical mapping based on user and item tendency for rating-bias removal by normalizing each review score with respect to the user and item tendencies. user tendency means whether a user tends to rate every item positively or negatively in general. item tendency refers to whether the underlying population of users considers a specific item particularly good or bad. this is different from comparing the mean rating of the item to the global mean. the goal is to see if the item stands out among all the products rated by the user @xcite . let r(c u , p i ) represent the review score of user (customer) c u for item p i . as explained in @xcite , we calculate the unbiased rating r cu,p i from the available ratings of user c u and item p i , and the predicted rating (with bias) r(c k , p m ) for a new review of user c k for item p m , i.e., a new user-item pair as follows: for simplicity, for rest of the paper we represent rating r(c u , p i ) as r ui , user mean r u (c u ) as r u , item mean r i (p i ) as r i , user tendency τ u (c u ) as τ u , and item tendency τ i (p i ) as τ i . furthermore, we will also refer to n p as n u , and n c as n i for rest of the paper. to account for space constraints, the detailed derivations of reverse estimation functions are mentioned in appendix. 2 the ratings obtained by using the reverse estimation functions can now be compared with the given ratings (gold ratings) to evaluate the effectiveness of our tendency-based approach. ## experimental details equations derived in section 2, can be used to calculate the unbiased ratings for a user-item pair and predicting the ratings for a new user-item pair. we perform extensive experiments with ratings as well as the review texts associated with these ratings to evaluate our method. ## results and analysis we answer the following questions through the experiments: case sub-case reverse tendency estimation function 1.τu >0, τi >0 ## related work most of the earlier approaches in rating prediction fall into three categories: a) sentiment analysis, b) recommendation systems (with/out collaborative filtering), and c) a hybrid of both. in sentiment analysis papers, the review text is used to predict the rating. this approach completely ignores the user/item meta-data, which is useful for rating bias estimation. multiple instances of works fall in this category, but only a few are closer to our approach, e.g., @xcite ; @xcite . in the recommendation setting, rating predictions are treated as a matrix completion problems where the similarity between users is utilized for recommending new items. for example, if user a is similar to user b, one can assume that the rating of both users would be similar for a given item. in this setting, earlier work ignores the rating bias in users and items' tendencies. another line of similar work, such as @xcite ; sreepada et al. @xcite ; rao et al., which use a hybrid model for both personalized sentiment prediction and recommendation by combining information from both review and text; however, they too have not acknowledged the rating bias problem in detail. the rating bias problem was earlier studied as user bias problem by @xcite ; @xcite ; @xcite ; @xcite . however, the most relevant work to our approach is @xcite , which acknowledges the rating bias problem using simple mean/standard deviation-based normalization schemes. although, in our paper, we use simple yet effective tendency based collaborative filtering instead of a standard normalization. ## conclusion in this paper, a tendency-based approach that models the user and item tendency for rating prediction is proposed and tested on standard review datasets. to do so, we use the existing established work on tendency-based collaborative filtering for obtaining reverse estimation functions. our method can successfully mitigate user rating bias and can also help in detecting outliers/anomalies in ratings and reviews' texts. we will further extend this model by analyzing tendencies and score predictions based on the users' demographics and items' categories. currently, our model cannot handle the cold start problem and require a sufficient history of earlier ratings for users/products. thus, our model cannot predict ratings for completely new users and items. we intend to examine this problem in the future extensions of the model. moreover, despite the bias in the text and score rating, we noted that bias is more inherent while rating a product. as shown in