Abstract / truncated to 115 words (read the full abstract)

Recommender systems provide recommendations about various products and services to their users by using other users’ data. These systems depend on personal user preferences on items via ratings and recommend items based on choices of similar users. Their success is imperative for both users and the e-commerce vendors utilizing such systems. Since inaccurate and unreliable product recommendations make users search alternative sites for shopping. Hence, recommender systems are a challenging research field with many unresolved problems and many different hybrid recommendation algorithms have been proposed to overcome these problems. Hybrid models that use different information sources (text, images, ratings, etc.) for recommendation are getting more attention in recent years. In this dissertation, a graph-based hybrid ... toggle 5 keywords

complex domain graph hybrid recommender systems link prediction quaternion domain.


Zuhal Kurt
Anadolu University
Publication Year
Upload Date
June 21, 2021

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