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However, those recommender systems did not consider the current reading trends when making book recommendations. How to use social media information to detect current reading trends is an interesting research direction. Another critical issue of current recommender systems is the scalability of algorithms with large amounts of data. Dealing with massive amounts of dynamic data generated by online interactions between users and online book platforms is a challenge.
We use social media information to detect current reading trends and combine the book popularity and personal interests to recommend books.
Book popularity is identified by the collected tweets to indicate current reading trends. The book popularity is represented by Lucene score of each book based on the collected tweets.
We implement the proposed hybrid book recommender system on top of big data frameworks such as Apache Spark, Apache Kafka, Spark MLLib and Apache Solr.
Our system provide the scalability of algorithms with large amount of data.
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IEEE/ICACT20230130 Slide.06
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