A Tensor Factorization on Rating Prediction for Recommendation by Feature Extraction from Reviews
In many online review sites or social media, each user is encouraged to assign a numeric rating and write a textual review as a feedback to each item that he had gotten, e.g., a product that he had bought, a place that he had visited, a service that he had received. Sometimes, feedbacks by some users would be affected by some contextual factors such as weather, distance, time, and season. Therefore, the context-aware approach is being developed by utilizing the user's contextual information to produce more precise recommendations than traditional approaches. Furthermore, previous works have already approved the drawback of the ignorance of textual reviews would bring mediocre performance for rating prediction.
In this work, we propose a framework TF+ for rating prediction models based on Tensor Factorization (TF) which is an extended version of Matrix Factorization (MF) by adding another dimension. We consider seasonal context as the additional dimension. Firstly, in our framework, each of the reviews is characterized by a numeric feature vector. Secondly, it uses TF which is trained by the proposed first-order gradient descent method for TF named Feature Vector Gradient Descent (FVGD). For the training of TF in TF+, FVGD decides the learning rates based on the feature vectors of reviews. In our evaluation, we use pre-processed data of five cities in YELP challenge dataset, and apply one of LDA, Doc2Vec and SCDV to get numeric feature vectors of reviews. We conduct experimental comparisons, and the results show that methods by TF+ improve the performance significantly as compared to the basic TF model.
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