Aspect-Similarity-Aware Historical Influence Modeling for Rating Prediction (2020)

Abstract: Many e-commerce services provide customer review systems. Previous laboratory studies have indicated that the ratings recorded by these systems differ from the actual evaluations of the users, owing to the influence of historical ratings in the system. Some studies have proposed using real-world datasets to model rating prediction. Herein, we propose an aspect-similarity-aware historical influence model for rating prediction using natural language processing techniques. In general, each user provides a rating considering different aspects. Thus, it can be assumed that historical ratings provided considering similar aspects to those of later ones will influence evaluations of users more. By focusing on the review-topic similarities, we show that our method predicts ratings more accurately than the previous historical-inference-aware model. In addition, we examine whether our model can predict “intrinsic rating,” which is given if users were not influenced by historical ratings. We performed an intrinsic rating prediction task, and showed that our model achieved improved performance. Our method can be useful to debias user ratings collected by customer review systems. The debiased ratings help users to make decision properly and systems to provide helpful recommendations. This might improve the user experience of e-commerce services.

## introduction currently, many e-commerce services like amazon provide customer review systems (crs). crss retrieve user feedbacks, which contain mainly ratings and reviews, shared across entire users, enabling (1) subsequent users to help decide whether to purchase the items and (2) the systems to make recommendations for items. previous studies have shown that historical ratings presented by a crs can create historical influence @xcite . in this study, "historical influence" refers to a phenomena that historical ratings make users give ratings apart from their natural evaluation. according to the previous work, users tend to give higher ratings to items presented with a high average historical rating. such influence affects the unbiased purchase decisions of subsequent users, and provides inaccurate and unhelpful recommendations. therefore, it is important that the recommender system estimates the "intrinsic rating", which is given if users were not influenced. recently, some studies have proposed historical-influenceaware rating prediction models @xcite @xcite . in particular, @xcite found that the subsequent rating of an item correlates with the average historical ratings at the time of evaluation. they concluded that such correlation patterns could be described by an assimilation-contrast theory @xcite . subsequent users tend to provide ratings according to the average historical rating when it is close to their intrinsic ratings (assimilation); conversely, they provide ratings against the average historical rating when it differs from their intrinsic ratings (contrast). the proposed model is called the historical-influence-aware latent factor (hialf) model, and it has achieved significant improvements in rating prediction. in addition, the model can be used to estimate intrinsic ratings. we assume that evaluators are influenced significantly by the historical ratings given under aspects similar to theirs. in general, different users focus on different aspects of an item and the rating is given based on these aspects. for example, a user concerned more about the color of an item may provide a different rating for the item than a user concerned more about the price. therefore, it is natural that the strength of the influence from each historical rating depends on the aspects under which the rating was provided. consequently, evaluators are likely to provide ratings higher or lower than intrinsic ratings, when the historical rating is high or low, respectively. in this paper, we conduct preliminary analyses to confirm that our assumption is appropriate. then, we propose an aspect-aware historical influence model for rating prediction using natural language processing techniques (figure 1 (a) ). we apply topic modeling to extract aspects from reviews, and calculate the similarities in the aspects. the calculated aspect similarity is used to weight the corresponding historical ratings. the weighted ratings are then aggregated to integrate the matrix factorization (mf) model @xcite . we conduct experiments to show that our model outperforms hialf and mf in rating prediction on four real-world datasets. additionally, to examine whether our model can predict intrinsic ratings, we evaluate our model on the additional task of predicting the first rating for each item. our results demonstrate that the proposed model predict the intrinsic ratings of users accurately. contributions: the contributions of this work are summarized as follows. ## proposed model we propose the model under the assumption that the strength of the historical influence depends on aspects similarity between an evaluator and users who gave historical ratings. we use a topic model to vectorize the review, and the extracted topic is regarded as an aspect of the review. then, we calculate the similarity between the review topics of the evaluator and those of the historical users. we use this similarity as the aspect similarity, because user reviews are considered to reflect their aspects. by considering aspect similarity, the proposed method is expected to model the historical influence accurately. our model is an extension of hialf. the major difference lies in the calculation of prior expectation. in hialf, the historical ratings and recency are used in the calculation. by contrast, the proposed model uses historical ratings and review topics. concretely, we calculate the aspect similarity between the evaluator and each historical user, and then, the prior expectation only among the ratings with high aspect similarities. the model is described as follows: here, α u represents the likelihood of the user u being influenced, and function f (•) and β(•) models the magnifying effect and the bias curve following hialf, respectively (eq. 6, 7). e simi u,i denotes the aspect-aware prior expectation using the review-topic similarity. we detail the method in the following subsection. ## experiments we conducted two tasks: ordinay rating prediction (task 1) and intrinsic rating prediction (task 2). task 1 is widely used to evaluate recommender systems. in task 1, we investigated the effect of top-k and w in eq. 9. task 2 is where a model predicts the first ratings of every item. the first ratings can be used as the ground truth for the intrinsic ratings of users, because users who provided the first rating were not exposed to any historical influence. in task 2, the ratings are predicted only from pre-trained users or item-features. the models are evaluated based on the ability to distinguish intrinsic ratings from historical influence. if a model successfully learns the intrinsic user-features, it will be able to accurately estimate the intrinsic ratings of users. ## conclusion in this study, we propose an aspect-similarity-aware historical influence model for rating prediction. first, we perform preliminary experiments to validate our assumption that review aspects relate to the historical influence. from the analyses, we concluded that the sentence similarity relates to the historical influence. thus, to model historical influence, we used textual information in user reviews, which previous models have ignored, for topic modeling. in the ordinay rating prediction task, we showed that the proposed approach achieved improvements over the previous historical-influence-aware models. furthermore, to examine whether the proposed model can distinguish the intrinsic ratings of users from the historical influence, we conducted intrinsic rating prediction experiments. the results showed that our model has better performance than previous models. our method is limited to situations where the reviews of users are obtained before they provide ratings. for usual recommendation, it might not be practical. thus, the method is not suitable for recommendations based on rating predictions. however, debiasing the historical influence to obtain intrinsic ratings can be considered as an application of our model. the intrinsic ratings would help subsequent users to make unbiased purchase decisions. in addition, recommender systems would be able to suggest acceptable items to users by using the intrinsic ratings. these improvements will provide better user experience of the e-commerce services.