Given the task definition and input, reply with output. In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

We show the final results for opinion recommendation, comparing our proposed model with the following state-of-the-art baseline systems:

RS-Average is the widely-adopted baseline (e.g., by Yelp.com), using the averaged review scores as the final score.

RS-Linear estimates the rating score that a user would give by INLINEFORM0 BIBREF49 , where INLINEFORM1 and INLINEFORM2 are the the training deviations of the user INLINEFORM3 and the product INLINEFORM4 , respectively.

RS-Item applies INLINEFORM0 NN to estimate the rating score BIBREF50 . We choose the cosine similarity between INLINEFORM1 to measure the distance between product.

RS-MF is a state-of-the-art recommendation model, which uses matrix factorisation to predict rating score BIBREF8 , BIBREF41 , BIBREF25 .

Sum-Opinosis uses a graph-based framework to generate abstractive summarisation given redundant opinions BIBREF51 .

Sum-LSTM-Att is a state-of-the-art neural abstractive summariser, which uses an attentional neural model to consolidate information from multiple text sources, generating summaries using LSTM decoding BIBREF44 , BIBREF3 .

All the baseline models are single-task models, without considering rating and summarisation prediction jointly. The results are shown in Table TABREF46 .  
 Question: What are the baselines?

RS-Average  RS-Linear RS-Item RS-MF Sum-Opinosis Sum-LSTM-Att