| Summary: | 博士 === 國立交通大學 === 資訊管理研究所 === 105 === Online review websites are nowadays popular information sharing platforms, which help users decide whether to buy products or visit business stores by referring the review opinions and ratings. However, a large amount of review information results in information overload problems and difficulty for users to find preferred products or business stores. Accordingly, it is an important issue to predict user preferences and make recommendations by analyzing the review opinions and ratings on the websites.
Traditional rating prediction methods usually adopt collaborative filtering to predict user ratings based on historical rating records. However, users’ preference ratings are usually affected by the aspect-based ratings factors including user preference emphases and business performances on various aspects. Specifically, different users may have different emphases on aspect preferences. Business stores with different aspect performances may receive similar ratings from users with different aspect preferences. Consequently, predicting user preferences by only considering user ratings of business stores, cannot effectively identifying users with similar aspect preferences and business stores with similar business performances, and thus may result in poor predictions. Traditional methods, which only consider historical user ratings, are limited and not effective in predicting user ratings. This research proposes a novel rating
prediction method considering the aspect-based ratings factors. First, the review texts are analyzed to extract the opinion semantics of various aspects. Second, user ratings on aspect semantics are analyzed to discover the aspect-based rating factors, which are used to build the user rating prediction model and business performance model. Third, the two models are then used to predict user preference ratings on business stores. Finally, experiments are conducted to evaluate the proposed method using Yelp dataset. The experiment results show that the proposed method outperforms traditional methods and can improve the accuracy of rating predictions.
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