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In this research work, we Proposed a hybrid book recommender system to make personalized recommendations for users.
Used the item-based algorithm with the ALS to solve the sparsity problem and address the over-fitting issue.
Given the book popularity, we used the Twitter Developer API to collect streaming tweets in order to prioritize the book recommendations. We calculated the Lucene score of a book by querying the collected tweets against its description. This gave us a numerical representation of how ¡°popular¡± the book is right now.
Our proposed system was implemented using a big data framework. Apache Kafka and Apache Spark Streaming were used to ingest and process Twitter streaming data. PySpark was used to implement data pre-processing and data analytics. The ALS recommender algorithm was implemented using Apache Spark MLlib. The results showed that the accuracy of the proposed hybrid recommender system was acceptable and it assisted readers in finding appropriate books among a flood of information.
In the future, we intend to improve our model by incorporating book bibliography data such as publisher, author and category as well as users¡¯ demographic data. Regarding the big data framework, we intend to deploy the proposed hybrid recommender system on cloud platforms such as AWS Cloud or Google Cloud Platform.
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IEEE/ICACT20230130 Slide.23
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