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Recommender systems based on sentiment analysis become challenging due to the presence of enormous data available over the internet. With the lack of proper data cleaning and analysis methods, existing machine learning (ML) techniques fail to generate accurate recommendations. To overcome this issue, this paper proposes a Light Deep Learning (LightDL)-based recommender system that uses Twitter-based reviews. First, the data is collected from Twitter and cleaned by subsequent data cleaning processes. Then, this pre-processed data is fed into the LightDL model, which learns the important features like hashtags, unigrams, multigrams, etc. from each piece of data. Here, we have learned about four groups of features, including semantic features, syntactic features, symbolic features, and tweet-based features. Finally, the data is classified into positive, negative, and neutral categories according to the learned features. On the basis of classified sentiment, the review is generated to the users. Finally, the model is evaluated in terms of accuracy, precision, recall, f-measure, and error rate through extensive experiments in Matlab. The proposed LightDL model outperforms in all performance measures; specifically, it achieves 95% accuracy for the Twitter dataset. [ABSTRACT FROM AUTHOR]

Recommender systems (RS) suffer from cold start and data sparsity problem. Researchers have proposed various solutions to this problem in which cross domain recommendation is an effective approach. Cross domain recommender system (CDRS) utilizes user data from multiple domains to generate prediction for the target user. This paper proposes a proactive cross domain recommender system. This paper also introduces a parallel approach in cross domain recommendation using general purpose graphic processing unit (GPGPU). This will help to accelerate the computation in the multi-agent environment as data processing in multiple domains takes significant amount of time. A prototype of the system is developed in tourism domain using Cuda, JCuda, Java, Android studio and Jade. The system uses four domains which is restaurant, tourist places, shopping places and hotels. The performance of the parallel CDRS system is compared with non-parallel CDRS in terms of their processing speed. Also the system is compared to the normal Collaborative Filtering approach to measure accuracy of the proposed system using MAE as well as precision, recall and F-measure. The results show a significant speedup for the presented system over non-parallel system. [ABSTRACT FROM AUTHOR]