Serendipitous Recommendation for Mobile Apps Using Item-Item Similarity Graph
Research Area: Information Retrieval Year: 2013
Type of Publication: In Proceedings Keywords: Mobile apps, Serendipitous recommendation, Graph
Authors:
  • Upasna Bhandari
  • Kazunari Sugiyama
  • Anindya Datta
  • Rajni Jindal
 
   
Abstract:
Recommender systems can provide users with relevant items based on each user's preferences. However, in the domain of mobile applications (apps), existing recommender systems merely recommend apps that users have experienced (rated, commented, or downloaded) since this type of information indicates each user's preference for the apps. Unfortunately, this prunes the apps which are releavnt but are not featured in the recommendation lists since users have never experienced them. Motivated by this phenomenon, our work proposes a method for recommending serendipitous apps using graph-based techniques. Our approach can recommend apps even if users do not specify their preferences. In addition, our approach can discover apps that are highly diverse. Experimental results show that our approach can recommend highly novel apps and reduce over-personalization in a recommendation list.
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