ABSTRACT
We study personalized recommendation of social software items, including bookmarked web-pages, blog entries, and communities. We focus on recommendations that are derived from the user's social network. Social network information is collected and aggregated across different data sources within our organization. At the core of our research is a comparison between recommendations that are based on the user's familiarity network and his/her similarity network. We also examine the effect of adding explanations to each recommended item that show related people and their relationship to the user and to the item. Evaluation, based on an extensive user survey with 290 participants and a field study including 90 users, indicates superiority of the familiarity network as a basis for recommendations. In addition, an important instant effect of explanations is found - interest rate in recommended items increases when explanations are provided.
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Index Terms
- Personalized recommendation of social software items based on social relations
Recommendations
Social media recommendation based on people and tags
SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrievalWe study personalized item recommendation within an enterprise social media application suite that includes blogs, bookmarks, communities, wikis, and shared files. Recommendations are based on two of the core elements of social media - people and tags. ...
Interactive Social Recommendation
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